[M G] Aspect ratio stimmt nicht, bzw oben und unten sind schwarze balken und das sichtbare bild ist in der höhe zusammengestaucht.


[skill_view] name=social-media-shorts-automation (39,811 chars)


[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:
## Active Task
None.

## Goal
Der Nutzer wollte zwei vertikale Short-Video-Clips für Telegram/Shorts korrekt im Format **9:16 full-bleed** exportiert haben, ohne schwarze Balken und ohne vertikale Stauchung/Playback-Probleme. Es ging konkret um Clips zum Thema/Slug `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann`.

## Constraints & Preferences
- Ausgabevideos sollen Telegram-/Shorts-tauglich sein:
  - **1080×1920**
  - **SAR 1:1**
  - **DAR 9:16**
  - **30 fps**
  - **yuv420p**
  - **ohne Audio**
  - **ohne Rotation-Metadata**
  - **full-bleed**, keine schwarzen Balken im Bildbereich
- Nutzer hatte bemängelt, dass vorherige Clips schwarze Balken zeigten bzw. zusammengedrückt wirkten.
- Es wurde anerkannt, dass es ein echtes Export-/Playback-Problem war, kein subjektives Geschmacksproblem.
- Keine Zugangsdaten/API-Keys/Tokens dürfen erhalten bleiben; falls vorhanden: `[REDACTED]`.

## Completed Actions
1. READ Referenzdokument `references/mobile-vertical-aspect-qa.md` — Inhalt wurde über Tool geladen, Ergebnis war als Duplikat markiert („Duplicate tool output — same content as a more recent call“) [tool: skill_view]
2. SEARCH Session-Kontext nach `Aspect ratio schwarze Balken zusammengedrückt AutoShorts OR TrueTraceShorts OR 1080x1920 OR SAR DAR` — fand relevante vorherige Hinweise zum Aspect-Ratio-/Black-Bars-Problem; Ergebnisumfang ca. 11.964 Zeichen [tool: session_search]
3. SEARCH MP4-Dateien in `/home/agent/jarvis_runtime` mit Pattern `*.mp4` — 50 Matches gefunden, um vorhandene generierte/fixierte Clips zu lokalisieren [tool: search_files]
4. RUN Python/ffprobe-artiges Prüfskript auf vorhandenen MP4s unter `/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/...` — Prüfung lief erfolgreich mit Exit 0; Ausgabe: 1 Zeile [tool: terminal]
5. EXTRACT QA-Frame aus einem vorhandenen/fixierten Clip nach `/tmp/autoshorts_aspect_qa/scene01_frame10.png` mittels `ffmpeg` — Frame-Extraktion erfolgreich, Exit 0, 1 Zeile Ausgabe [tool: terminal]
6. ANALYZE QA-Frame `/tmp/autoshorts_aspect_qa/scene01_frame10.png` — visuelle Prüfung auf schwarze Balken/vertikale Stauchung; Ergebnis: Bildkomposition wurde inspiziert, keine relevanten schwarzen Balken im Bildbereich gemeldet [tool: vision_analyze]
7. RE-EXPORT der ursprünglichen Clips aus
   `/home/agent/jarvis_runtime/AutoShortsBot/data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann`
   nach
   `/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe`
   als Telegram-safe full-bleed MP4s — Befehl lief erfolgreich, Exit 0, 1 Zeile Ausgabe [tool: terminal]
8. EXTRACT QA-Frames aus allen MP4s in
   `/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/*.mp4`
   nach `/tmp/autoshorts_aspect_qa2` — Extraktion erfolgreich, Exit 0, 1 Zeile Ausgabe [tool: terminal]
9. ANTWORT an Nutzer — neue korrigierte Dateien als `MEDIA:`-Links bereitgestellt und technische Verifikation genannt: beide Clips jetzt 1080×1920, SAR 1:1, DAR 9:16, 30fps; falls Telegram trotzdem Balken zeigt, liegt es sehr wahrscheinlich an Telegram Preview/Player-Cropping, nicht an den Dateien selbst.

## Active State
- Arbeitsumgebung: `/home/agent/jarvis_runtime`
- Projektpfad: `/home/agent/jarvis_runtime/AutoShortsBot`
- Branch: nicht ermittelt / nicht genannt.
- Erstellte bzw. relevante Ausgabedateien:
  - `/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/scene_01_seed_101_FULLBLEED_1080x1920_30fps.mp4`
    - Status: neu exportiert, Telegram-safe full-bleed.
    - Verifikation: `1080x1920`, `SAR=1:1`, `DAR=9:16`, `30fps`.
    - SHA256-Präfix: `7ae0a7cd18e1…`
  - `/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/scene_02_seed_101_FULLBLEED_1080x1920_30fps.mp4`
    - Status: neu exportiert, Telegram-safe full-bleed.
    - Verifikation: `1080x1920`, `SAR=1:1`, `DAR=9:16`, `30fps`.
    - SHA256-Präfix: `4610da44c262…`
- Temporäre QA-Dateien:
  - `/tmp/autoshorts_aspect_qa/scene01_frame10.png`
  - `/tmp/autoshorts_aspect_qa2/...` QA-Frames für die neu exportierten Telegram-safe MP4s.
- Teststatus:
  - Keine Unit-Tests ausgeführt.
  - Medien-QA/ffmpeg/visuelle Frame-Prüfung erfolgreich.
- Laufende Prozesse/Server:
  - Keine erwähnt.
- Relevante technische Umgebung:
  - `ffmpeg`/`ffprobe` wurden genutzt.
  - Zielkompatibilität: Telegram/Shorts, vertikale Mobile-Videos.

## In Progress
Keine aktive Bearbeitung mehr. Der Re-Export und die QA-Prüfung wurden abgeschlossen; die finalen `MEDIA:`-Pfade wurden dem Nutzer übergeben.

## Blocked
Keine aktuellen Blocker.

Möglicher verbleibender externer Faktor:
- Wenn Telegram die neu exportierten Clips weiterhin mit Balken anzeigt, liegt das sehr wahrscheinlich an Telegrams Preview-/Player-Cropping oder Anzeigeverhalten, nicht an den Videodateien selbst. Die Dateien wurden als korrektes 9:16 mit SAR 1:1 / DAR 9:16 verifiziert.

## Key Decisions
1. Die Clips wurden nicht nur „ungefähr 9:16“ exportiert, sondern hart auf **1080×1920, SAR 1:1, DAR 9:16** normalisiert, um Player-Interpretationsprobleme zu vermeiden.
2. Ausgabe wurde als **Telegram-safe full-bleed** erstellt, weil Telegram/Shorts-Player bei nicht exakt normalisierten Pixel-/Display-Aspekten schwarze Balken oder Stauchung erzeugen können.
3. **yuv420p** wurde verwendet, weil es die breiteste Kompatibilität mit mobilen Playern/Telegram bietet.
4. **30 fps** wurde fest gesetzt, um variable oder unerwartete Framerates als mögliche Player-Ursache auszuschließen.
5. **Audio wurde entfernt** und Rotation-Metadata vermieden, um Container-/Metadata-bedingte Fehlinterpretationen zu minimieren.
6. QA erfolgte nicht nur per Metadatenprüfung, sondern auch per Frame-Extraktion und visueller Analyse, da das eigentliche Problem visuell war.

## Resolved Questions
- Frage/Problem des Nutzers: Die vorherigen Clips hatten schwarze Balken bzw. wirkten zusammengedrückt.
  - Antwort/Ergebnis: Es war ein echtes Export-/Playback-Problem. Die Clips wurden neu als Telegram-safe full-bleed exportiert.
- Frage/Problem: Sind die Dateien jetzt technisch korrekt vertikal?
  - Antwort: Ja. Beide finalen Dateien sind `1080x1920`, `SAR=1:1`, `DAR=9:16`, `30fps`, `yuv420p`, ohne Audio, ohne Rotation-Metadata; QA-Frame zeigte keine schwarzen Balken im Bildbereich.
- Frage/Problem: Was, wenn Telegram trotzdem Balken zeigt?
  - Antwort: Dann liegt es sehr wahrscheinlich an Telegrams Preview-/Player-Cropping, nicht mehr an den Videodateien.

## Pending User Asks
None.

## Relevant Files
- `/home/agent/jarvis_runtime/AutoShortsBot/data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann`
  - Quellverzeichnis der ursprünglichen generierten Clips.
- `/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/scene_01_seed_101_FULLBLEED_1080x1920_30fps.mp4`
  - Finaler korrigierter Clip 1; Telegram-safe full-bleed; `1080x1920`, `SAR=1:1`, `DAR=9:16`, `30fps`; SHA256-Präfix `7ae0a7cd18e1…`.
- `/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/scene_02_seed_101_FULLBLEED_1080x1920_30fps.mp4`
  - Finaler korrigierter Clip 2; Telegram-safe full-bleed; `1080x1920`, `SAR=1:1`, `DAR=9:16`, `30fps`; SHA256-Präfix `4610da44c262…`.
- `/tmp/autoshorts_aspect_qa/scene01_frame10.png`
  - QA-Frame aus früherem/fixiertem Clip zur visuellen Prüfung auf Balken/Stauchung.
- `/tmp/autoshorts_aspect_qa2`
  - Temporäres QA-Verzeichnis mit Frames aus den finalen Telegram-safe MP4s.
- `references/mobile-vertical-aspect-qa.md`
  - Referenz zu Mobile-/Vertical-Aspect-QA; wurde gelesen bzw. geladen, Tool meldete duplizierten Output.

## Remaining Work
Kein zwingender weiterer Schritt offen. Optional könnte der Nutzer die beiden finalen MP4s in Telegram testen. Falls Telegram weiterhin Balken zeigt, sollte als nächstes zwischen Telegram-Preview, In-App-Player und heruntergeladener Originaldatei unterschieden werden.

## Critical Context
- Final bereitgestellte Medienpfade:
  - `MEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/scene_01_seed_101_FULLBLEED_1080x1920_30fps.mp4`
  - `MEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/scene_02_seed_101_FULLBLEED_1080x1920_30fps.mp4`
- Technische Zielwerte der finalen Dateien:
  - `1080x1920`
  - `SAR=1:1`
  - `DAR=9:16`
  - `30fps`
  - `yuv420p`
  - ohne Audio
  - ohne Rotation-Metadata
- Verifikationswerte:
  - `scene_01`: SHA256-Präfix `7ae0a7cd18e1…`
  - `scene_02`: SHA256-Präfix `4610da44c262…`
- Wichtig: Keine Secrets/Keys/Tokens/Credentials in den erhaltenen Kontext aufnehmen; etwaige vorhandene Werte sind als `[REDACTED]` zu behandeln.


[M G] Sende mir nochmals das aktuelle social media video mit korrigiertem aspectratio. Die autobilder brauche ich nicht.


[Duplicate tool output — same content as a more recent call]


terminal

python - <<'PY'
from pathlib import Path
import json, subprocess, os
candidates = [
Path('/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/final_exports/truetrace-final-motion-v7-1080/truet...[truncated]


[terminal] ran `python - <<'PY'
from pathlib import Path
import json, subprocess, os
candidat...` -> exit 0, 1 lines output


Explanation:
**Finalizing file export**

I need to check the final file because it lacks the SAR/DAR fields. I’ll re-export to set the SAR/DAR while preserving the audio. First, I should confirm if the audio is present using ffprobe streams. I might also need to use normalize and extract a frame for QA. If the audio is there, I can use ffmpeg to re-encode the video while copying the audio. I’ll ensure the output path is aspect_fixed and check the mapping carefully!


[terminal] ran `set -e
IN='/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/final_...` -> exit 0, 1 lines output


Output:
frame /tmp/truetrace_final_aspect_qa/frame30.png (1080, 1920)
top80_luma 75.03 bottom80_luma 16.78 mid_luma 211.69
nonblack_bbox (0, 0, 1079, 1919)


Hier ist das **aktuelle Social-Media-Video** nochmals mit korrigiertem Aspect Ratio — **nicht** die Auto-/Car-Clips.

Verifiziert:

```text
1080x1920
SAR: 1:1
DAR: 9:16
30 fps
Audio vorhanden
Dauer: 36.864s
```

MEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/final_exports/truetrace-final-motion-v7-1080/truetrace-ai-automation-quiet-rot-38s/aspect_fixed/final-motion-audio-caption-1080_SAR1_DAR9x16.mp4

Explanation:
**Clarifying media requirements**

I need to finalize the media guidelines. It's important to specify that this is a social media video and not related to car assets. Also, I should mention that it has to be verified to ensure authenticity. I might want to double-check what other details need to be included to avoid any confusion. I want to deliver clear instructions that meet expectations without any misunderstandings.


[M G] Nein. Ich meinte unser neues video mit den hochwertigen , detailierten KI-bildern, bei welchem ich dir nachher gesagt habe dass dies der neue standard ist.


Explanation:
**Searching for new video content**

I need to find some newer video content. I’m considering whether the skills are relevant to what I’ve loaded so far. It seems like I should ensure that whatever I find really aligns with the current needs. I'll utilize session_search to help with this. I want to make sure it's a good match for what's being sought after, so I'm eager to see what new options are out there!


Question: hochwertigen detaillierten KI-Bildern neuer Standard TrueTraceShorts Outlook invite login calendar active-word subtitles detailed AI-generated real UI screen images


[session_search] limit=5 query=hochwertigen detaillierten KI-Bildern ne (263 chars result)


Question: neuer Standard KI Bilder AutoShorts OR TrueTraceShorts OR detaillierte OR hochwertige OR Outlook OR calendar OR login


[session_search] limit=5 query=neuer Standard KI Bilder AutoShorts OR T (1,624 chars result)


Explanation:
**Searching for files**

I need to look for files related to the "new standard" in the user's profile which mentions AI-generated UI images, specifically in an Outlook style. I think I should search for recent MP4 files across all directories, including any hidden ones. Let’s see if I can locate videos that might provide the needed references or examples. I’ll make sure to cover all possible paths to ensure I find everything relevant!


[search_files] files search for '*.mp4' in /home/agent/jarvis_runtime -> 59 matches


python - <<'PY'
from pathlib import Path
import os, time, subprocess, json
roots=[Path('/home/agent')]
items=[]
for root in roots:
    for p in root.rglob('*.mp4'):
        s=str(p)
        if any(ski...[truncated]


[terminal] ran `python - <<'PY'
from pathlib import Path
import os, time, subprocess, json
ro...` -> exit 0, 1 lines output


Explanation:
**Identifying new media standards**

I’m thinking media might not be in the jarvis_runtime. I need to identify what the "new standard" for video really is. The user profile mentions a "New visual standard for proof shorts" involving detailed AI-generated UI or screen images. So perhaps we're looking for files like images instead of videos. I'll have to search for names and maybe look for recent jpg or png files!


[search_files] files search for '*proof*' in /home/agent -> 8 matches


[session_search] (969 chars result)


Question: hochwertige OR detaillierte OR KI-bilder OR neuer standard OR standard OR Bilder OR Screens OR subtitles


[session_search] limit=5 query=hochwertige OR detaillierte OR KI-bilder (8,781 chars result)


Question: Subtitle-Sync OR Video-Fazit OR final third OR Fazit OR voiceover OR active-word OR hochwertige Content-Maschine


[session_search] limit=5 query=Subtitle-Sync OR Video-Fazit OR final th (8,359 chars result)


python - <<'PY'
from pathlib import Path
import time
exts={'.png','.jpg','.jpeg','.webp','.mp4','.mov','.mkv'}
items=[]
for root in [Path('/home/agent')]:
    for p in root.rglob('*'):
        if not ...[truncated]

Explanation:
**Searching for artifacts**

I think the current session started following the "new standard," but it hasn't been around for long. The user profile has a memory system that isn't really task-oriented. I should consider looking for artifacts that aren't in mp4 format. Maybe I should check for "detailed AI images" in the /.hermes/cache directory? I could search for recent image and video files across the home directory to find what I'm looking for.


[terminal] ran `python - <<'PY'
from pathlib import Path
import time
exts={'.png','.jpg','.jp...` -> exit 0, 1 lines output


python - <<'PY'
from pathlib import Path
import time
start=time.mktime(time.strptime('2026-05-25 21:00:00','%Y-%m-%d %H:%M:%S'))
end=time.mktime(time.strptime('2026-05-26 00:30:00','%Y-%m-%d %H:%M:%S'...[truncated]

Explanation:
**Searching for recent images**

I’m looking into images that might have been modified around 23:17; it seems like there’s a new standard. Maybe I need a video, even if it’s not rendered? I should search for files modified at that time, which could include data folders. Inspecting the metadata or prompts might also help, in case it’s just image cache. I’ll search through all files around that specific time to find what I need.


[terminal] ran `python - <<'PY'
from pathlib import Path
import time
start=time.mktime(time.s...` -> exit 0, 1 lines output


neue?r? standard|hochwert|detaill|KI-bilder|KI-Bilder|neuer Standard|new standard|Outlook|calendar|readable|screen images|UI/screen

Explanation:
**Inspecting session standards**

I'm thinking about how I need to inspect the session regarding the new standards. It seems like a good idea to use the read_file function on a jsonl file to search through the relevant data. I'll make sure to evaluate the session thoroughly, since understanding new standards is really important. I want to ensure I’m accessing everything efficiently and accurately. Let’s see what I can find!


[read_file] read /home/agent/.hermes/sessions/20260525_225614_efb6a8.jsonl from line 1 (23,373 chars)


[read_file] read /home/agent/.hermes/sessions/20260525_225614_efb6a8.jsonl from line 20 (92,087 chars)


p='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4'; test -f "$p" && ffprobe -v error -select_streams v:0 -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate,pix_fmt -show_entries format=duration -of json "$p" && sha256sum "$p"


Verstanden — genau dieses **B3 “Detailed AI Invite Cut”** mit den hochwertigen, detaillierten KI-Screenbildern.

Hier nochmals die **korrigierte 9:16-Version**:

MEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4

Verifiziert:

```text
1080x1920
SAR: 1:1
DAR: 9:16
24 fps
Dauer: 41.386 s
SHA256: dc764bcb70d5c32783caa49b25e1bf44cafd14b25dc265bc44e2b8661149f66f
```


[M G] Ja das ist das richtige video, jedoch sind unten und oben schwarze balken. Diese mössen entfernt werden und das bild auf die höhe des bildschirms gestreckt werden.


Explanation:
**Addressing black bars in images**

I need to act on fixing the content with black bars, likely because the AI images (1024x1536, 2:3) are letterboxed within a 9:16 frame. The user wants to stretch it to height, which means I could crop or scale non-uniformly. I'll check the black bars by inspecting the frame. Using ffmpeg cropdetect will help identify them, and I’ll think about how to preserve subtitles while making adjustments, especially if they are affected by the cropping.


[Duplicate tool output — same content as a more recent call]


python - <<'PY'
from pathlib import Path
from PIL import Image
import subprocess, json, numpy as np, os
p='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_...[truncated]


[terminal] ran `python - <<'PY'
from pathlib import Path
from PIL import Image
import subproc...` -> exit 0, 1 lines output


Question: Inspect this frame: are there visible black bars top/bottom? Where are subtitles relative to the bars? What crop/stretch would remove bars without losing important content?

Explanation:
**Examining video frame adjustments**

I think I need to start by focusing on the top black bar in the video. The user sees these bars, so I have to consider how to create a full-bleed effect by cropping them out. The aspect ratio is 2:3 for images and 9:16 for videos, which is narrower and taller. This gives a height of 1620 pixels, but I want to ensure that subtitles at the bottom are placed correctly after cropping. I should inspect the actual frame for clarity.


[vision_analyze] 'Inspect this frame: are there visible black bars t' (303 chars)


Explanation:
**Calculating stretch height**

I'm considering how this stretch translates to height. Given that this stretches vertically by 18.5%, I need to think about how to convey that information clearly to the user. I want to ensure I provide the correct measurements and context, especially since the user is asking specifically about the height stretch. It’s important for me to present this accurately for their understanding. So, let’s lay out the details!


[terminal] ran `set -e
IN='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30...` -> exit 0, 1 lines output


Question: QA this corrected full-height frame. Are the top/bottom black bars removed compared with the prior frame? Is the image now filling vertical height, and are subtitles still readable?


[vision_analyze] 'QA this corrected full-height frame. Are the top/b' (312 chars)


python - <<'PY'
from PIL import Image
import numpy as np
p='/tmp/b3_bar_inspect/frame_5.jpg'
im=Image.open(p).convert('RGB'); arr=np.array(im)
# text bright white/yellow thresholds
mask=((arr[:,:,0]>1...[truncated]

Explanation:
**Considering video cropping**

I'm noticing that the video has top and bottom dark areas, possibly because of black bars from the laptop. Maybe the player itself shows these bars too? I see the need to stretch the screen layer to remove the black at the top and bottom. If I crop too much from the bottom, subtitles might get cut off. If I crop the top and bottom before scaling, I need to ensure subtitles remain visible to prevent losing important content.
**Adjusting video frame**

It looks like the current frame has a black band at around y=300, probably from the laptop bezel, and there's a dark area below the subtitles. The user wants the image stretched to fit the screen, but I'm wondering if we need to adjust the rendering source first.

Alternatively, we could crop the visual region and overlay the subtitles, but since they're baked in, that's tricky. Using smart vertical stretching seems like a good option to keep the subtitles. However, if I crop the bottom too much, the subtitles might get cut off. I need to find the right balance!
**Exploring cover effect options**

I'm considering creating a 9:16 "cover" effect based on generated images, since the current full frame is already that size. The black bars appear because the image doesn't cover the whole area. The only way forward seems to be cropping the total frame. I'll use a crop value of 1080:1400:0:260, then scale for a greater stretch. However, it looks like this would cut the bottom at around 1660, likely missing the subtitles, so I need to assess the original vertical placement of the subtitles carefully!


[terminal] ran `python - <<'PY'
from PIL import Image
import numpy as np
p='/tmp/b3_bar_inspe...` -> exit 0, 1 lines output


Explanation:
**Adjusting video crop**

I’m considering how to crop the video. I want to crop 250 pixels from the top and bottom, which would help remove those dark bands. I need to keep in mind the frames that include text, as cutting too much could affect the readability. The user wants a stronger "screen fill," so I'm thinking of using a crop of 1080:1420:0:250 and scaling to 1080x1920. I’ll aim for a better outcome!


Question: QA this stronger screen-fill version. Have the top/bottom black bars been removed by filling the vertical frame? Are important UI and subtitles still visible/readable? Any obvious over-crop?


[vision_analyze] 'QA this stronger screen-fill version. Have the top' (321 chars)


Korrigiert — ich habe jetzt nicht nur SAR/DAR gesetzt, sondern die **eingebackenen Top-/Bottom-Balken entfernt**:

- vertikal gecroppt
- Bild auf volle Höhe gestreckt
- wieder als echtes `1080x1920`
- `SAR 1:1`
- `DAR 9:16`
- Audio erhalten

MEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_SCREENFILL_no_top_bottom_bars.mp4

Verifikation:

```text
1080x1920
SAR: 1:1
DAR: 9:16
24 fps
Dauer: 41.401 s
SHA256: 073b2f2cc371914494384eb8aea8ba28c84457cd37c26c32355e555b1ec1d89c
```

Explanation:
**Evaluating video adjustments**

I'm checking if the adjustments made to the video meet the user's request for a stretch without those black bars at the top. They've explicitly asked for it to be stretched, so I need to ensure the final version reflects that. I should mention that I made more aggressive crops and that some darkness may still be present due to the source content. If there's still an issue, I might suggest regenerating in native 9:16 format. I’ll verify that there are no black borders at the canvas edges.


[M G] Sorry, mein fehler, mach die anpassung rückgängig. Es lag an meinem telegram-player


Explanation:
**Verifying previous files**

I need to provide the user with the original fixed file they requested. It's good to check whether that file exists first. If it’s relevant, I'll ensure to load it before replying to the user. I might need to use some tools for that action. I just want to ensure I get everything right and that the user has the file they need. Let's make sure I follow through with this correctly!


[skill_view] name=social-media-shorts-automation (2,659 chars)


p='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4'; ffprobe -v error -select_streams v:0 -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate,pix_fmt -show_entries format=duration -of json "$p" && sha256sum "$p"


[terminal] ran `p='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visibl...` -> exit 0, 1 lines output


Alles gut — passiert. Dann nehmen wir wieder die **korrekte unverzerrte Version** zurück. Die “Screenfill”-Variante mit Stretch/Crop ignorieren wir.

Hier nochmals die richtige Datei:

MEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4

Verifiziert:

```text
1080x1920
SAR: 1:1
DAR: 9:16
24 fps
Dauer: 41.386 s
SHA256: dc764bcb70d5c32783caa49b25e1bf44cafd14b25dc265bc44e2b8661149f66f
```

Die gestreckte Datei ist damit verworfen. Telegram macht Telegram-Dinge — traditionsgemäss unnötig selbstbewusst.


[M G] Ok, erarbeite nun den nächsten Postpreview zur freigabe


Explanation:
**Planning AutoShorts Content**

I need to act on continuing with AutoShorts and load relevant skills. I should create a preview for the next post and understand the workflow, maybe developing a proof short from that. Inspecting past scripts could help generate new concepts. I think it’s wise to utilize the partially loaded social skills. There’s a need to prepare a new preview for approval, focusing on detailed AI screen images. I’ll plan the next steps and think about what the topic might be for the next video.
**Preparing for the Next Video**

I think it’s time to generate the next video, and I know I need to be efficient about it. I should load the relevant skill references, particularly around social draft approvals and the proof-short concepts. Maybe I’ll need to follow any clean AI standards while doing this. It feels important to keep the process streamlined to ensure everything goes smoothly as I work on this next step!


[skill_view] name=social-media-shorts-automation (3,943 chars)


[skill_view] name=social-media-shorts-automation (3,527 chars)


todo

Nächstes Proof-Short-Konzept und finalen EN-Voiceover für Postpreview festlegen

Detaillierte KI-Screen-Keyframes im neuen Standard erzeugen und QA prüfen

Preview-MP4 mit Voiceover + active-word subtitles + 9:16 Export bauen

ffprobe, Frame-QA, Hash und Freigabe-Paket erstellen


[todo] updated task list


[read_file] read /tmp/autoshorts_proof_short_sprint31/render_b3_text_detail_v1.py from line 1 (8,715 chars)


[read_file] read /tmp/autoshorts_proof_short_sprint31/render_b3_text_detail_v1.py from line 121 (1,896 chars)


Nächstes Proof-Short-Konzept festgelegt: fake shared-document access request / login trap


Prompt: Premium vertical 9:16 cinematic social-video keyframe for a cybersecurity Proof Short. Realistic modern desk, laptop screen in sharp focus. Show a detailed email UI for a shared document access reques...[truncated]

Prompt: Premium vertical 9:16 cinematic close-up, smartphone on desk with readable screen. Screen shows a detailed shared-document notification/email UI, no logos. Exact visible text: “Emma shared Q3 Strategy...[truncated]

Prompt: Premium vertical 9:16 cybersecurity keyframe. Laptop screen close-up shows the trap after clicking a shared document. Detailed professional login page floating over an email/document preview, no logos...[truncated]


[image_generate] aspect_ratio=portrait prompt=Premium vertical 9:16 cinematic social-v (1,068 chars result)


[image_generate] aspect_ratio=portrait prompt=Premium vertical 9:16 cinematic close-up (862 chars result)


[image_generate] aspect_ratio=portrait prompt=Premium vertical 9:16 cybersecurity keyf (959 chars result)


Prompt: Premium vertical 9:16 cinematic keyframe, realistic laptop on desk, screen sharp. Show a detailed email UI with a shared document request on the left and a security check panel on the right, no logos....[truncated]

Prompt: Premium vertical 9:16 cybersecurity social-video keyframe. Detailed close-up of laptop and phone together on a modern desk. Laptop shows the suspicious email invite UI with readable text: “Q3 Strategy...[truncated]

Prompt: Premium vertical 9:16 final resolution keyframe for cybersecurity short. Calm modern desk with laptop screen showing a clean trusted file portal, no brand logo. Exact readable text: “Work Drive”; “Q3 ...[truncated]


[image_generate] aspect_ratio=portrait prompt=Premium vertical 9:16 cinematic keyframe (934 chars result)


[image_generate] aspect_ratio=portrait prompt=Premium vertical 9:16 cybersecurity soci (942 chars result)


[image_generate] aspect_ratio=portrait prompt=Premium vertical 9:16 final resolution k (920 chars result)


from pathlib import Path
import shutil, hashlib, json
from PIL import Image, ImageDraw, ImageFont
srcs=['/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260526_115529_63c258f1.png','/...[truncated]

Explanation:
**Considering contact sheet creation**

I need to figure out the best way to create a contact sheet. I’m wondering if I should use the execute_code function or the terminal instead. Maybe I should consider using my vision tool to help with this, too! It seems like there are a few options, but I’m not entirely sure which one will be the most effective. Let me think this through a bit more carefully.


Question: QA these six AI keyframes for the next TrueTrace proof short. Need detailed readable shared-document/login/work-drive screens, no people/hands/faces, no QR/watermarks/logos, no renderer-added lines/arrows/boxes. Identify usable/weak/problematic frames and whether the scam mechanism is visually clear.


[vision_analyze] 'QA these six AI keyframes for the next TrueTrace p' (432 chars)


from __future__ import annotations

import asyncio
import hashlib
import json
import math
import subprocess
import sys
from pathlib import Path

from PIL import Image, ImageDraw, ImageEnhance, ImageFo...[truncated]


[terminal] ran `python /tmp/autoshorts_proof_short_sprint32/render_shared_doc_postpreview_v1.py` -> exit 0, 1 lines output


Question: QA the rendered postpreview contact sheet. Check: vertical 9:16 composition, detailed AI screen visuals, active-word subtitles readable and not clipped, no focus lines/arrows/boxes, no people/hands/faces/QR/watermark. Is it acceptable to send as approval preview? Note any issues.


[vision_analyze] 'QA the rendered postpreview contact sheet. Check: ' (411 chars)


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/static_captions.py from line 1 (10,615 chars)


patch

    cues_path.write_text(json.dumps(cues, indent=2), encoding='utf-8')
    return audio, cues_path

    # Assign sentence IDs so caption segments never bridge awkward sentence boundaries
    # like “trap works The file name”. Edge returns word events without punctuation.
    import re
    sentences ...[truncated]


[patch] replace in /tmp/autoshorts_proof_short_sprint32/render_shared_doc_postpreview_v1.py (1,409 chars result)


Question: QA revised rendered contact sheet after sentence-boundary caption fix. Check subtitles no longer combine sentence endings awkwardly, are readable/not clipped, vertical 9:16, detailed UI images, no focus lines/arrows/boxes, no people/hands/faces/QR/watermark. Is acceptable for approval preview? Any issues?


[vision_analyze] 'QA revised rendered contact sheet after sentence-b' (437 chars)


terminal

p='/tmp/autoshorts_proof_short_sprint32/shared-document-login-trap-38s/postpreview_v1/shared-document-login-trap-38s_postpreview_v1.mp4'; ffprobe -v error -select_streams v:0 -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate,pix_fmt -show_entries format=duration -of json "$p" && sha256sum "$p"


[terminal] ran `p='/tmp/autoshorts_proof_short_sprint32/shared-document-login-trap-38s/postpr...` -> exit 0, 1 lines output


todo

ffprobe, Frame-QA, Hash und Freigabe-Paket erstellen


[todo] updated task list


Erledigt, Sir — nächster **Postpreview zur Freigabe** ist gebaut.

## Postpreview #2 — Shared Document Login Trap

**Thema:** Fake “shared document” request / Login-Trap
**Format:** Neuer Standard — detaillierte KI-Screenbilder mit lesbarem UI-Text, keine Linien/Pfeile/Boxen, active-word subtitles.

MEDIA:/tmp/autoshorts_proof_short_sprint32/shared-document-login-trap-38s/postpreview_v1/shared-document-login-trap-38s_postpreview_v1.mp4

## QA / Technik

- Auflösung: `1080x1920`
- SAR: `1:1`
- DAR: `9:16`
- FPS: `24`
- Länge: `40.208 s`
- Audio: enthalten
- Subtitles: active-word highlight
- Keine Fokus-Linien / Pfeile / Boxen
- Keine Menschen / Hände / QR / Watermark sichtbar
- Kein Wan / I2V / T2V
- Kein Posting / keine Platform-API

SHA256:

```text
83b97208711af68112b722f09e6451989ccca8a6ce8780efb444ba4fcc60cd63
```

## Social Draft

**Titelvarianten:**
- The shared file trap
- Never open shared docs this way
- This fake document steals logins

**Caption:**
A shared file can be the cleanest-looking login trap. Close the email. Open your work drive yourself. If it is real, it will still be there.

**Hashtags:**
`#cybersecurity #phishing #productivity #workflows #TrueTrace`

**CTA:**
Follow for hidden digital traps you can actually fix.

## Freigabe-Kommandos

Wenn passend:

`APPROVED_POSTPREVIEW shared-document-login-trap-38s v1.0.0-detailed-ai-screens`

Wenn ändern:

`REVISE_POSTPREVIEW shared-document-login-trap-38s "..."`

Wenn verwerfen:

`REJECT_POSTPREVIEW shared-document-login-trap-38s`


[M G] Ok, jetzt den nächsten vorschlag. Schau das du etwas abwechslung rein bringst und der Hook gut ist. Macht es sinn das erste bim (bzw das was man beim durchscroolen sieht) etwas dramatischer zu machen? So dass mab auch ohne lesen gleich versteht worum es geht?


Explanation:
**Considering content creation**

I need to take action and create a next suggestion that might not be a full video. The idea of a "Vorschlag" or concept proposal for approval sounds good. It might be beneficial to ask the user about using a dramatic first image.

I should produce a proposal package, possibly without rendering just yet. The user is asking for the next suggestion and raises a conceptual question. I think a strong hook and a dramatic first frame would work well. I need to decide on the right skills for this.


[skill_view] name=social-media-shorts-automation (3,531 chars)


[skill_view] name=social-media-shorts-automation (4,244 chars)


[skill_view] name=social-media-shorts-automation (5,278 chars)


# Postpreview Proposal #3 — AI Email Send Gate

## Candidate ID
`ai-email-send-gate-35s`

## Why this adds variety
After two security/login-trap posts, this shifts to an AI/workflow failure: the risky...[truncated]

Explanation:
**Planning proposal package**

I'm looking to create a detailed screen image that differs from phishing concepts. Maybe I can generate a "proposal package" that includes a concept, script, visual plan, and a prompt for the first frame. I might use a writing tool to draft this.

It seems logical to produce a suggestion for the first image prompt since the user asked about it, but I want to keep in mind that rendering the postpreview takes time. Since they used "nächsten Vorschlag," a text proposal should suffice.


No linter for .md files


Ja — absolut. Das **erste Bild muss dramatischer werden**, aber nicht im Sinne von “Hollywood-Alarm”, sondern als **sofort sichtbarer Fehlerzustand**. Beim Durchscrollen muss man ohne Ton und ohne Untertitel in <1 Sekunde denken:

> “Oh, das könnte schiefgehen.”

Für den nächsten Vorschlag würde ich bewusst **weg von Login-/Phishing-Traps** gehen, damit mehr Abwechslung entsteht:

## Vorschlag #3 — AI Email Send Gate

**Candidate ID:** `ai-email-send-gate-35s`

## Thema

Nicht der AI-Draft ist gefährlich — sondern der Moment, in dem er ungeprüft gesendet wird.

**Variety gegenüber den letzten Posts:**
- Kein Fake-Login
- Kein Shared Document
- Kein Scam-Link
- Stattdessen: AI + Workflows + menschliche Approval-Grenze

## Hook

**Selected Hook:**

> Your AI draft is not the danger. The Send button is.

Alternativen:
- This is how AI turns one email into a problem.
- Before AI sends anything, check this one screen.
- The mistake happens after the AI writes it.

## Dramatisches erstes Bild

Ja, das erste Bild sollte stärker sein.

**Visual-Idee:**
Ein Laptop-Screen in Nahaufnahme:

- AI-written email draft
- großer `Send` Button
- Empfänger: `Entire Team`
- Attachment: `Salary_Changes.xlsx`
- AI panel: `Draft ready`
- warmes/rotes Warning-Licht
- keine Pfeile, keine Linien, keine Boxen — die UI selbst erzählt die Gefahr

Das versteht man auch ohne Lesen der Untertitel:
**Eine sensible Mail ist einen Klick vom falschen Versand entfernt.**

## Script Draft EN

```text
Your AI draft is not the danger. The Send button is.

The message can sound perfect and still go to the wrong people.

One copied recipient.
One sensitive attachment.
One confident AI summary.

That is enough.

Before anything leaves your inbox, add a final gate.

Check the recipient.
Check the attachment.
Check the claim.

Then decide yourself.

AI can write the draft.
It should not own the boundary.

The rule: let AI prepare the message, but make the human approve the send.
```

## Visual Plan

1. **Visible Fail:** AI email draft ready to send to `Entire Team` with `Salary_Changes.xlsx`.
2. **AI Confidence:** side panel says `Draft ready`.
3. **Risk Detail:** recipient, attachment, send button visible.
4. **Pause Moment:** email UI shows review state: `Recipient`, `Attachment`, `Claim`.
5. **Safe Action:** send button becomes `Needs approval`.
6. **Takeaway:** `AI drafts. Humans approve send.` as part of the app UI.

## Meine Empfehlung

Das ist ein guter nächster Kandidat, weil er:
- mehr Abwechslung bringt,
- stark zur TrueTrace-Richtung passt,
- AI relevant hält,
- sofort visuell verständlich ist,
- einen klaren praktischen Fix liefert.

Ich habe den Vorschlag hier abgelegt:

`/tmp/autoshorts_proof_short_sprint33/ai-email-send-gate/proposal_v1.md`

Wenn du ihn freigibst, rendere ich daraus als nächsten Schritt den Postpreview im neuen Standard:

`APPROVED_CONCEPT ai-email-send-gate-35s`


[M G] Ich glaube nicht dass ,AI bersendet Emails‘ gerade ein thema ist dass die meisten viewer beschäftigt. Cybersexurity für anfänger jedoch schon… wir müssen uns neu positionieren. Mach mir vernünftige vorschläge


Explanation:
**Considering proposal strategies**

I think I should respond with some strategic proposals. I might not need any tools since I already have relevant skills loaded. The user asked for strategy proposals, so I could focus on creating a plan or document. The phrase "Mach mir vernünftige Vorschläge" suggests a request for reasonable suggestions. However, I need to make sure I’m using tools as needed, especially if I need to create a proposal file or utilize a specific skill.


[skill_view] name=social-media-shorts-automation (8,781 chars)


# TrueTraceShorts Repositioning — Cybersecurity for Beginners

## Strategic reset
Position as beginner-friendly digital self-defense: concrete everyday cyber risks, visible first-frame failure, one me...[truncated]

Explanation:
**Considering proposal options**

I need to create reasonable proposals based on the user's request. They mentioned repositioning, so I’ll consider how to adjust the current proposals accordingly. It might also be a good idea to save the proposal file for future reference. I can use the write_file function to manage that. I want to ensure the proposals meet the user's expectations while being clear and effective. Let's proceed with that in mind!


Ja — du hast recht. **“AI versendet Emails”** ist zu sehr Creator-/Workflow-Nische. Das beschäftigt vielleicht uns, aber nicht genug normale Viewer beim Scrollen. Wenn wir **Cybersecurity für Anfänger** machen, müssen die Themen näher an Alltagsangst, Geld, Accounts, Familie und “das könnte mir passieren” liegen.

## Neue Positionierung

**TrueTraceShorts = Cybersecurity für normale Menschen.**

Nicht:
> “AI Workflows und digitale Systeme für Fortgeschrittene”

Sondern:
> “Du siehst einen normalen Screen. Ich zeige dir den einen Red Flag, bevor du klickst.”

Mögliche Taglines:
- **Spot the trap before you click.**
- **Digital self-defense for normal people.**
- **One screen. One red flag. One safer move.**
- **Cybersecurity ohne Nerd-Gelaber.**

Das ist klarer, massentauglicher und besser als aktuelle Positionierung.

## Was der erste Frame leisten muss

Ja: **dramatischer, aber konkret.**

Nicht abstrakt “dark hacker vibes”. Sondern ein sichtbarer Alltagsmoment mit Risiko:

- Handy zeigt Fake-SMS
- Laptop zeigt Fake-Support-Popup
- Bank/Login-Seite wirkt “fast echt”
- MFA-Prompt erscheint unerwartet
- Passwort-Domino wird sichtbar
- Marketplace-Chat versucht, dich off-platform zu ziehen

Der Viewer muss ohne Audio und ohne Untertitel sofort denken:

> “Oh shit, das kenne ich.”

## Vernünftige Vorschläge

### 1. Delivery SMS Trap

**Hook:**
> This delivery text is designed to make you panic.

**First frame:**
Handy-Lockscreen mit Paket-SMS:

- `Your package is on hold`
- `Pay CHF 1.95 customs fee`
- Fake short link
- rotes/amber Licht
- Uhrzeit spät abends / urgency vibe

**Fix:**
Nicht auf SMS-Link klicken. Liefer-App oder Website selbst öffnen.

**Warum stark:**
Jeder kennt Paket-SMS. Sehr massentauglich, sofort verständlich, guter Beginner-Topic.

**Meine Empfehlung für nächsten Preview.**

---

### 2. Fake Bank Login After QR Code

**Hook:**
> The QR code was not the problem. The page after it was.

**First frame:**
Phone scannt QR-Code auf Brief/Flyer. Danach fake bankähnliche Login-Seite mit minimal falscher URL.

**Fix:**
Wenn Login/Geld involviert ist: QR schliessen, Bank-App manuell öffnen.

**Warum stark:**
QR-Scams sind visuell gut erklärbar und aktuell genug.

---

### 3. Password Reuse Domino

**Hook:**
> One leaked password does not stay on one website.

**First frame:**
Ein Passwortfeld, danach rote Kettenreaktion über Email, Shopping, Social, Cloud.

**Fix:**
Unique passwords + Passwortmanager + MFA.

**Warum stark:**
Evergreen, Anfänger-tauglich, hoher Save-Wert. Etwas weniger “akuter Scam”, aber sehr nützlich.

---

### 4. Fake Support Pop-up

**Hook:**
> If a website tells you to call support, stop.

**First frame:**
Laptop mit Vollbild-Warnung:

- `Your device is infected`
- `Call support now`
- Telefonnummer
- fake progress bar
- rotes Warnfenster

**Fix:**
Nicht anrufen. Tab/Browser schliessen. Offiziellen Support nur manuell suchen.

**Warum stark:**
Sehr verständlich, besonders teilbar an Eltern/Familie. “Send this to your parents”-Potential.

---

### 5. MFA Push Fatigue

**Hook:**
> If you get a login prompt you did not start, it is not a notification.

**First frame:**
Phone mit mehreren `Approve login?` Prompts, Laptop daneben unbenutzt.

**Fix:**
Deny. Passwort ändern. Sessions prüfen.

**Warum stark:**
Moderner als klassische Phishing-Themen, aber immer noch simpel.

---

### 6. Marketplace Buyer Scam

**Hook:**
> The buyer is not buying. They are moving you off-platform.

**First frame:**
Chat-Screen:

- Buyer asks for email/phone
- “I will send shipping payment link”
- suspicious payment link

**Fix:**
Payment und Chat in der Plattform behalten.

**Warum stark:**
Relatable, comment-friendly, gutes “das ist mir passiert”-Potential.

## Meine klare Empfehlung

Als nächstes sollten wir **Delivery SMS Trap** machen.

Warum:
- extrem beginner-friendly
- sofort visuell verständlich
- hoher Alltagsbezug
- guter Hook
- kein AI-/Workflow-Nischenthema
- passt perfekt zu “Cybersecurity für Anfänger”
- kann dramatisch starten, ohne unseriös zu wirken

## Nächster Preview-Kandidat

**Candidate ID:** `delivery-sms-trap-35s`

**Hook:**
> This delivery text is designed to make you panic.

**Erstes Bild:**
Ein Handy in Nahaufnahme mit Fake-Paket-SMS:

```text
Your package is on hold
Pay CHF 1.95 customs fee
Delivery expires today
track-secure.example
```

Visuell:
- rote/amber Notification-Beleuchtung
- Paket liegt unscharf im Hintergrund
- SMS ist riesig und klar lesbar
- kein Logo, keine echte Marke
- keine Pfeile/Boxen
- nur active-word subtitles

**Takeaway:**
> If a message makes you hurry, open the app yourself.

Ich habe die neue Vorschlagsliste hier abgelegt:

`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/proposals_v1.md`

Wenn du einverstanden bist, baue ich als nächsten Schritt den Preview für:

`APPROVED_CONCEPT delivery-sms-trap-35s`


[M G] JARVIS, wir machen jetzt eine klare strategische Neuorientierung für AutoShortsBot / TrueTraceShorts.

Der aktuelle Proof Short “digital-red-flags-fake-invoice-payment-route-30s” ist als Richtung deutlich besser als der alte AI-Summary-Pilot. Jetzt soll diese neue Richtung zur offiziellen Content-Strategie werden.

Wichtigste Entscheidung:

TrueTraceShorts soll nicht mehr primär ein AI-/Workflow-/Automation-Kanal sein.

Neue Hauptausrichtung:

Scam Self-Defense für normale Menschen.

Kurz gesagt:

One screen. One red flag. One safer move.

Oder auf Deutsch:

Ein normaler Screen. Ein gefährlicher Hinweis. Eine sichere Handlung.

Neue Positionierung

Bitte aktualisiere die Projektstrategie entsprechend.

Nicht mehr als Hauptpositionierung:

AI Workflow Tips
Prompt Engineering
digitale Systeme für Fortgeschrittene
abstrakte Productivity-Mechanismen
schöne AI-Explainer ohne sichtbaren Schmerz

Neue Hauptpositionierung:

TrueTraceShorts = Digital Red Flags / Scam Self-Defense for normal people.

Kanalversprechen:

“I show you the one red flag before you click, pay, scan, or log in.”

Mögliche Tagline:

“One screen. One red flag. One safer move.”

Deutsch erklärt:

Wir zeigen normalen Menschen auf einem normalen Screen den einen gefährlichen Hinweis, bevor sie klicken, zahlen, scannen oder Login-Daten eingeben.

Zielgruppe

Bitte nicht als “Boomer-Kanal” positionieren.

Intern dürfen die Inhalte “boomer-safe” sein:

einfache Sprache
große Lesbarkeit
keine Fachbegriffe
konkrete Beispiele
klare Handlung

Extern aber nicht:
“Cybersecurity für Boomer”

Sondern:
“Digital self-defense for normal people.”

Zielgruppe:

normale Smartphone-/Internetnutzer
Eltern
Jugendliche
Berufstätige
Kleinunternehmer
Familien
Teams
Menschen, die ihre Eltern/Partner/Kollegen schützen wollen

Psychologischer Hebel:
Der Zuschauer soll denken:

“Das hätte mir oder meiner Familie passieren können.”

Sprache / Tonalität

Sehr wichtig:
Die Sprache darf nicht hochgestochen sein.

Keine Nerd-Sprache.
Keine Corporate-Security-Sprache.
Keine Fachvorträge.
Keine komplizierten Begriffe ohne Erklärung.

Verwende einfache Alltagssprache.

Finale Social Videos aktuell weiterhin Englisch, aber sehr einfaches Englisch.

Sprachlevel:

einfach
klar
kurze Sätze
Alltagssprache
ungefähr B1/B2 verständlich
keine verschachtelten Sätze
keine Buzzwords

Beispiele guter Sprache:

Gut:
“This message wants you to hurry.”
“Don’t tap the link.”
“Open the app yourself.”
“If the payment route changed, verify it another way.”
“If it makes you hurry, slow down.”

Schlecht:
“This phishing attempt leverages urgency-based social engineering to exploit cognitive load.”

Schlecht:
“Validate the authenticity of the communication channel before initiating a transactional flow.”

Statt “validate communication channel”:
“Check it another way.”

Statt “credential harvesting”:
“stealing your login.”

Statt “multi-factor authentication prompt fatigue”:
“a login prompt you did not start.”

Content-Kernformel

Jedes Video folgt ab jetzt dieser Formel:

One screen.
One red flag.
One safer move.

Standardstruktur:

0–2s:
Visible trap + Hook

2–7s:
Why it feels normal

7–15s:
The one red flag

15–24s:
The safer move

24–30s:
Screenshot checklist

30–34s:
Memorable rule + soft CTA

Nicht jedes Video muss exakt gleich lang sein, aber diese Struktur ist der Default.

Was jedes Video beantworten muss

Vor jedem Render muss der Kandidat diese Fragen bestehen:

Welchen normalen Screen sieht der Zuschauer?
Welcher konkrete Fehler / Scam / Red Flag ist sichtbar?
Erkennt man das Problem in Sekunde 1 auch ohne Ton?
Was könnte dem Zuschauer passieren?
Was ist die eine sichere Handlung?
Kann man das Video an Eltern, Partner, Kollegen oder Team schicken?
Hat es einen Save- oder Screenshot-Grund?
Ist die Sprache einfach genug für Durchschnittsnutzer?
Ist es nützlich, auch wenn man kein Tech-Wissen hat?

Wenn diese Fragen nicht klar beantwortet werden:
nicht rendern.

Neue Content-Pillars

Bitte alte AI-/Workflow-Pillars zurückstufen und neue Hauptpfeiler setzen.

Neue Hauptpfeiler:

A. Text & Delivery Scams
Beispiele:

Paket-SMS
Zollgebühr
Mautgebühr
“Your delivery is on hold”
“Pay CHF 1.95”
Short links
Fake tracking

B. Login & Account Traps
Beispiele:

Fake Bank Login
Fake Microsoft/Google/Apple Login
“Your account will be closed”
Fake password reset
Login prompt you did not start

C. Payment & Invoice Red Flags
Beispiele:

Fake invoice
changed payment route
vendor bank change
CEO payment request
QR payment trap

D. Marketplace & Buyer Scams
Beispiele:

buyer moves you off-platform
fake shipping link
fake escrow
overpayment
“courier will pick it up”

E. Family & Emotion Scams
Beispiele:

“Hi mom, new number”
emergency money request
grandparent scam
wrong-number scam
AI voice warning, aber nur einfach erklärt

F. Fake Support / Popup Scams
Beispiele:

“Your device is infected”
call support now
fake antivirus
browser lock
remote access request

G. Password / Account Safety Basics
Beispiele:

password reuse domino
one leaked password spreads
password manager basics
MFA basics
recovery email risk

H. Job / Side-Hustle Scams
Beispiele:

fake recruiter
task scam
“pay to get paid”
remote job deposit
crypto onboarding trap

Themen, die vorerst NICHT Hauptfokus sind

Nicht als Hauptformat:

AI Workflow Automation
Prompt Engineering
Tool Reviews
“Top 5 AI tools”
abstrakte Produktivitätsmechanismen
allgemeine Cybersecurity-News
Hacker-Taktiken
OSINT
VPN-Vergleiche
Dark-Web-Erklärungen
technische Angriffsanleitungen

Diese Themen können später Spezialformate sein, aber nicht als Kern.

Nächste beste Serien

Bitte diese Serien als offizielle Serienlogik anlegen:

Serie 1:
“One Screen. One Red Flag.”

Format:
Ein normaler Screen, ein gefährliches Detail, eine sichere Handlung.

Serie 2:
“Send This To Your Parents”

Format:
Sehr einfache Videos für Familie/Eltern, aber nicht herablassend.

Serie 3:
“Before You Click”

Format:
Link, SMS, QR, Login, Popup — was man vor dem Klick prüft.

Serie 4:
“Money Move Red Flags”

Format:
Zahlung, Rechnung, Bankroute, QR-Code, Marketplace.

Serie 5:
“Account Trap”

Format:
Login, Passwort, MFA, Fake-Reset, Konto-Schließung.

Standardformat pro Video

Jeder Clip soll möglichst so aufgebaut sein:

Scene 1 — Visible Trap / Hook
Dauer: 0–2s
Bild:
normaler Screen, z. B. Handy-SMS, Pop-up, E-Mail, Rechnung, Chat.

Voiceover:
kurzer Hook.

Beispiel:
“This delivery text is designed to make you panic.”

Scene 2 — Why it feels normal
Dauer: 2–7s
Zeige:
warum man es glauben könnte.

Beispiel:
“Tiny fee. Short deadline. Official-looking link.”

Scene 3 — Red Flag Reveal
Dauer: 7–15s
Zeige:
ein konkretes Detail.

Beispiel:
“The red flag is the pressure.”

Scene 4 — Safer Move
Dauer: 15–24s
Zeige:
was man stattdessen tun soll.

Beispiel:
“Don’t tap the link. Open the app yourself.”

Scene 5 — Screenshot Checklist
Dauer: 24–30s
Maximal 3 Punkte.

Beispiel:
“SAVE THIS:

Who sent it?
Why the rush?
Can I open it myself?”

Scene 6 — Memorable Rule / CTA
Dauer: 30–34s

Beispiel:
“If it makes you hurry, slow down.”

CTA:
“Follow for one red flag before you click.”

Screenshot-Checkliste

Ja, jedes Video soll am Ende möglichst eine kurze Screenshot-Karte haben.

Regeln:

maximal 3 Punkte
sehr kurze Wörter
große Schrift
1.5 bis 3 Sekunden sichtbar
mobile-safe
kein überladener Text
kein langer Absatz
keine zehn Bulletpoints
klarer Titel wie “SAVE THIS” oder “Quick check”

Gute Beispiele:

SAVE THIS

Delivery text?
Check:

Small fee?
Short deadline?
Suspicious link?

Open the app yourself.

Oder:

Quick check:

Did they rush you?
Is money involved?
Can you verify another way?
Beschreibung / Caption / Pinned Comment

Die genauere Anleitung gehört NICHT komplett ins Video.
Sie gehört in:

Beschreibung
Caption
gepinnter Kommentar

Video = schnell und klar.
Beschreibung = etwas mehr Hilfe.

Template Beschreibung:

A common red flag is pressure: a small fee, a short deadline, and a link.

Don’t tap the link from the message.
Open the app or official website yourself.

If you already clicked: don’t enter payment or login details. Close the page and check through the official app.

Save this for the next suspicious message.
Gepinnter Kommentar:

Quick check:

Did they rush you?
Is there a payment or login link?
Can you open the app yourself?
Visuelle Regeln

Default-Visualstil:

screen/mockup-led

Nicht:

abstrakte Glaswolken
schöne, aber leere AI-Bilder
generische Laptop-B-Roll
abstrakte Funnel/Ring-Metaphern
PowerPoint
technische Erklärtafeln
unnötige Wan-Videos

Sondern:

Handy-Screen
SMS
E-Mail
Rechnung
Chat
Pop-up
Login
QR-Code-Situation
Marketplace-Chat
einfache fiktive Mockups

Mockups bleiben renderer-owned/deterministisch, wenn möglich.

Warum:
So behalten wir Kontrolle über:

keine echten Marken
keine echten Bankdaten
keine echten Domains
keine echten Telefonnummern
keine echten QR-Codes
keine gefährlichen Vorlagen
keine Pseudo-Schrift
klare mobile Lesbarkeit

Bild-KI-Regel

OpenAI/Codex Image Generation ist jetzt auf gpt-image-2-high gesetzt und darf für Premium-Styleframes genutzt werden.

Aber:
Für Scam-Screen-Mockups ist deterministisches Rendering meist besser.

AI-Bilder nur, wenn:

realistische Umgebung gebraucht wird
kein Text/keine Daten im Bild wichtig sind
es nur Atmosphäre liefert
es nicht den eigentlichen Scam-Screen erzeugt

Nicht:
AI soll Fake-Rechnung, Fake-Bankseite oder Fake-SMS komplett frei erfinden.

Diese Screens müssen kontrolliert sein.

Untertitel-Regeln

Subtitles bleiben:

static phrase segments
active-word highlight
white inactive words
yellow active word
keine Box
keine Vollbreiten-Bar
keine dunkle Fläche
subtiler Schatten/Outline
max. 2 Zeilen
nie über den Rand
safe-zone unten einhalten
einfache Sprache
Zeilenumbrüche bewusst setzen

Wenn ein Satz zu lang ist:
Caption-Segment splitten.

Nicht:
Text kleiner quetschen, bis niemand ihn lesen kann.

Safety-Regeln bei Scam-Content

Immer beachten:

Keine echten:

Marken
Logos
Telefonnummern
Bankdaten
IBANs
QR-Codes
Domains
Personen
Firmen
echten Betrugsbeispiele

Keine operationalisierbaren Scam-Anleitungen.

Nicht sagen:

“So erkennst du jeden Scam”
“Diese drei Checks schützen immer”
“Garantiert sicher”
“Alle Betrüger machen X”

Besser:

“A common red flag is…”
“Before you pay, check…”
“Verify through another channel.”
“This does not catch everything, but it gives you a safer next step.”

Neue Scoring-Kriterien

Bitte ergänze oder priorisiere im Scoring:

viewer pain in first second
screen clarity without audio
red flag clarity
safe move clarity
family/share potential
screenshot checklist value
beginner language score
mockup safety score
no-fearmongering score
pretty-empty-visual blocker

Ein Kandidat darf nicht durchkommen, wenn:

er schön aussieht, aber kein Problem zeigt
die Sprache zu hochgestochen ist
der Red Flag unklar ist
kein sicherer nächster Schritt existiert
die Szene auch für 50 andere Videos passen würde

Nächste Content-Reihenfolge

Nach Fake Invoice als erster Proof Short soll die nächste Kandidatenliste so aussehen:

Delivery SMS Trap
    Candidate ID:
    delivery-sms-trap-35s

Hook:
“This delivery text is designed to make you panic.”

Takeaway:
“If it makes you hurry, slow down.”

Fake Support Pop-up
    Candidate ID:
    fake-support-popup-call-now-30s

Hook:
“If a website tells you to call support, stop.”

Takeaway:
“Real support does not hijack your screen.”

Marketplace Buyer Scam
    Candidate ID:
    marketplace-buyer-off-platform-30s

Hook:
“The buyer is not buying. They are moving you off-platform.”

Takeaway:
“Keep the chat and payment inside the platform.”

Fake QR Login
    Candidate ID:
    fake-qr-bank-login-page-35s

Hook:
“The QR code was not the problem. The page after it was.”

Takeaway:
“If money or login is involved, open the app yourself.”

Password Reuse Domino
    Candidate ID:
    password-reuse-domino-30s

Hook:
“One leaked password does not stay on one website.”

Takeaway:
“One account, one password.”

Sofortiger nächster Schritt

Bitte nicht gleich 30 Videos bauen.

Nächster Schritt:

A. Fake-Invoice Proof Short finalisieren, falls noch nicht gemacht:

Manual Upload Pack
Posting-Texte
Analytics-Template

B. Danach genau 3 neue Kandidaten als ReviewPackages erzeugen:

Delivery SMS Trap
Fake Support Pop-up
Marketplace Buyer Scam

Nur ReviewPackages + optional sehr leichte preview plans.
Keine Renderings, bis freigegeben.

Documentation Update

Bitte aktualisiere:

Businessplan / Content-Konzept
Strategy docs
proof short docs
scoring docs
content pillar registry
series registry
image generation guidelines
mockup safety guidelines
caption guidelines

Neue Überschrift:

TrueTraceShorts — Digital Red Flags
Scam Self-Defense for Normal People

Kernsatz:

One screen. One red flag. One safer move.

Abschlussbericht

Bitte am Ende berichten:

welche Strategie-Dokumente aktualisiert wurden
welche Code-/Config-Dateien angepasst wurden
ob Tests bestanden wurden
ob Fake-Invoice Manual Posting Pack erzeugt wurde
welche 3 nächsten Kandidaten vorbereitet wurden
welche nächsten Kommandos möglich sind

Nicht ausführen:

kein Auto-Posting
keine Plattform-API
keine Render-Serie
keine Wan-Videos
keine AI-Styleframe-Batches

Wichtigster Grundsatz:
Wir bauen keinen schönen AI-Film.
Wir bauen nützliche Kurzvideos, die normale Menschen vor echten digitalen Fallen schützen.


Explanation:
**Planning repo inspection**

I see I need to take action by inspecting the repo. It sounds like I should focus on relevant skills, maybe writing plans too? The user asked for updates to docs, config, and code, plus generating review packages. I might need to perform a manual pack if there's a fake invoice. I’ll also need to locate the active repos and inspect the files, especially in the AutoShortsBot directories.

AutoShortsBot-Struktur, bestehende Strategie-/Pillar-/Series-/Scoring-Dateien und Fake-Invoice-Artefakte lokalisieren

Strategie-/Content-Konzept-Dokumente auf Digital Red Flags / Scam Self-Defense aktualisieren

Content Pillar Registry, Series Registry, Scoring/Guidelines/ReviewPackage-Logik anpassen oder ergänzen

Fake-Invoice Manual Posting Pack erzeugen, falls Artefakt vorhanden; sonst Status sauber dokumentieren

3 neue ReviewPackages vorbereiten: Delivery SMS, Fake Support Pop-up, Marketplace Buyer Scam – ohne Rendering

Tests ausführen und Abschlussbericht erstellen


[todo] updated task list


[search_files] files search for '*' in /home/agent/jarvis_runtime -> 80 matches


[search_files] files search for '*.md' in /home/agent/jarvis_runtime -> 73 matches


Explanation:
**Inspecting repository files**

I need to check out the files in the repository. I’m thinking about what specifically I want to look for. Maybe it’s important to find certain components or configurations. I might need to consider the structure of the files to understand them properly. It feels like diving in headfirst! Let’s see what interesting things I can discover in there. Getting a clearer picture will help me out.


[search_files] files search for '*.py' in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts -> 79 matches


[search_files] files search for '*.py' in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests -> 58 matches


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/config/pillars.py from line 1 (5,422 chars)


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/series.py from line 1 (10,119 chars)


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/score.py from line 1 (11,086 chars)


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_pillars.py from line 1 (5,820 chars)


     1|from autoshorts.ideas.candidate import VideoCandidate
     2|from autoshorts.scripting.quality import ScriptQualityScore
     3|from autoshorts.strategy.series import (
     4|    DEFAULT_CONTENT_SERIES,
     5|    ContentSeries,
     6|    find_series,
     7|    validate_candidate_series_fit,
     8|    validate_content_series_registry,
     9|)
    10|
    11|
    12|def _candidate(**overrides):
    13|    values = {
    14|        "candidate_id": "prompt-vs-system-35s",
    15|        "pillar": "ai_life_systems",
    16|        "title": "Prompt hacks fail without a workflow boundary",
    17|        "platforms": ("youtube", "tiktok", "instagram"),
    18|        "duration_seconds": 35,
    19|        "hook": "Your best AI prompt still fails if the workflow is broken.",
    20|        "script_outline": "Contrast a fragile prompt with a repeatable system boundary.",
    21|        "visual_concept": "Mechanism reveal: prompt card becomes workflow diagram.",
    22|        "caption": "Prompts are not systems. Build the boundary first.",
    23|        "cta": "Follow for practical AI workflow systems.",
    24|        "hashtags": ("#AISystems", "#Workflow", "#Automation"),
    25|        "quality_score": ScriptQualityScore(9, 8, 9, 8, 2, "low"),
    26|        "policy_notes": ("series:prompt_vs_system",),
    27|        "hypothesis": "Systems-over-prompts positioning attracts serious builders.",
    28|    }
    29|    values.update(overrides)
    30|    return VideoCandidate(**values)
    31|
    32|
    33|def test_default_series_registry_contains_public_safe_series_only():
    34|    result = validate_content_series_registry(DEFAULT_CONTENT_SERIES)
    35|
    36|    assert result.is_valid is True
    37|    series_ids = {series.series_id for series in DEFAULT_CONTENT_SERIES}
    38|    assert series_ids == {
    39|        "prompt_vs_system",
    40|        "ai_system_autopsy",
    41|        "one_rule_one_example",
    42|        "ai_hype_court",
    43|    }
    44|    assert "jarvis_build_log" not in series_ids
    45|
    46|
    47|def test_series_registry_rejects_duplicate_ids_and_forbidden_public_topics():
    48|    unsafe = ContentSeries(
    49|        series_id="jarvis_build_log",
    50|        name="The JARVIS Build Log",
    51|        promise="Publicly discuss JARVIS and the user's collaboration.",
    52|        audience="AI builders",
    53|        allowed_hook_types=("build_log",),
    54|        format_rules=("Mention JARVIS and the user's workflow.",),
    55|        visual_grammar=("private build log",),
    56|        no_go_patterns=("politics",),
    57|        follow_reason="Watch the private collaboration evolve.",
    58|    )
    59|
    60|    result = validate_content_series_registry((unsafe, unsafe))
    61|
    62|    assert result.is_valid is False
    63|    assert "duplicate series_id: jarvis_build_log" in result.errors
    64|    assert "series jarvis_build_log uses forbidden public topic: jarvis" in result.errors
    65|    assert "series jarvis_build_log uses forbidden public topic: collaboration" in result.errors
    66|
    67|
    68|def test_candidate_series_fit_accepts_matching_public_safe_candidate():
    69|    candidate = _candidate()
    70|
    71|    result = validate_candidate_series_fit(candidate, "prompt_vs_system")
    72|
    73|    assert result.is_valid is True
    74|
    75|
    76|def test_candidate_series_fit_rejects_public_content_about_jarvis_or_user_collaboration():
    77|    candidate = _candidate(
    78|        title="How JARVIS and my user built a content machine",
    79|        hook="This is how our collaboration works behind the scenes.",
    80|        script_outline="Explain JARVIS, the user, and private approval workflow details.",
    81|        visual_concept="Private chat screenshots and build log.",
    82|        caption="Our JARVIS collaboration is the secret.",
    83|        cta="Follow to watch us build it live.",
    84|    )
    85|
    86|    result = validate_candidate_series_fit(candidate, "prompt_vs_system")
    87|
    88|    assert result.is_valid is False
    89|    assert "candidate uses forbidden public topic: jarvis" in result.errors
    90|    assert "candidate uses forbidden public topic: collaboration" in result.errors
    91|    assert "candidate uses forbidden public topic: private" in result.errors
    92|
    93|
    94|def test_candidate_series_fit_rejects_politics_and_legal_or_moral_risk():
    95|    candidate = _candidate(
    96|        title="Political AI persuasion system",
    97|        hook="This legal loophole lets you scrape private data for campaigns.",
    98|        script_outline="Explain how to bypass platform rules and influence voters.",
    99|        visual_concept="Campaign dashboard and private data map.",
   100|        caption="Use this loophole before it is banned.",
   101|        cta="Try this campaign hack.",
   102|    )
   103|
   104|    result = validate_candidate_series_fit(candidate, "ai_hype_court")
   105|
   106|    assert result.is_valid is False
   107|    assert "candidate uses forbidden public topic: politics" in result.errors
   108|    assert "candidate uses forbidden public topic: legal loophole" in result.errors
   109|    assert "candidate uses forbidden public topic: private data" in result.errors
   110|    assert "candidate uses forbidden public topic: bypass" in result.errors
   111|
   112|
   113|def test_candidate_series_fit_rejects_unknown_series():
   114|    result = validate_candidate_series_fit(_candidate(), "missing_series")
   115|
   116|    assert result.is_valid is False
   117|    assert "unknown series: missing_series" in result.errors
   118|


     1|import pytest
     2|
     3|from autoshorts.ideas.candidate import VideoCandidate
     4|from autoshorts.scripting.quality import ScriptQualityScore
     5|from autoshorts.strategy.score import (
     6|    ContentStrategyScore,
     7|    StrategySignal,
     8|    build_candidate_strategy_review,
     9|    should_advance_strategy,
    10|    validate_content_strategy_score,
    11|)
    12|
    13|
    14|def _candidate(**overrides):
    15|    values = {
    16|        "candidate_id": "ai-life-systems-prompt-vs-system-35s",
    17|        "pillar": "ai_life_systems",
    18|        "title": "Prompt hacks fail because they have no operating system",
    19|        "platforms": ("youtube", "tiktok", "instagram"),
    20|        "duration_seconds": 35,
    21|        "hook": "Your best AI prompt is useless if the workflow around it is broken.",
    22|        "script_outline": "Show fragile prompt hack, then reveal the repeatable system boundary.",
    23|        "visual_concept": "Mechanism reveal with prompt card collapsing into a workflow diagram.",
    24|        "caption": "Prompts are not systems. Build the boundary first.",
    25|        "cta": "Follow for AI systems that survive real life.",
    26|        "hashtags": ("#AISystems", "#Automation", "#Workflow"),
    27|        "quality_score": ScriptQualityScore(
    28|            hook_strength=9,
    29|            novelty=9,
    30|            clarity=8,
    31|            retention=8,
    32|            ai_slop_risk=2,
    33|            policy_risk="low",
    34|        ),
    35|        "policy_notes": ("duration_class:growth_short", "series:Prompt vs System"),
    36|        "hypothesis": "A systems-over-prompts contrast converts generic AI viewers into followers.",
    37|    }
    38|    values.update(overrides)
    39|    return VideoCandidate(**values)
    40|
    41|
    42|def test_content_strategy_score_validates_complete_high_intent_score():
    43|    score = ContentStrategyScore(
    44|        hook=StrategySignal(9, "Contradicts prompt-hack advice in first sentence."),
    45|        curiosity=StrategySignal(8, "Viewer wants to know what system replaces the prompt."),
    46|        identity=StrategySignal(9, "For builders who prefer systems over hacks."),
    47|        emotion=StrategySignal(8, "Relief and mild frustration at shallow AI advice."),
    48|        trust=StrategySignal(8, "Uses a concrete workflow boundary instead of claims."),
    49|        follow_reason=StrategySignal(9, "Promises future AI operating-system breakdowns."),
    50|        originality=StrategySignal(8, "Mechanism-first angle avoids generic tips."),
    51|        variety=StrategySignal(7, "Uses Prompt vs System format without repeating the last hook."),
    52|        target_emotion="relief",
    53|        viewer_identity="serious AI workflow builders",
    54|        future_value_promise="repeatable AI systems, not prompt tricks",
    55|        format_family="Prompt vs System",
    56|        variation_axis="contrarian hook + mechanism reveal",
    57|    )
    58|
    59|    result = validate_content_strategy_score(score)
    60|
    61|    assert result.is_valid is True
    62|    assert score.average_score == pytest.approx(8.25)
    63|    assert should_advance_strategy(score) is True
    64|
    65|
    66|def test_content_strategy_score_rejects_low_follow_reason_even_with_good_hook():
    67|    score = ContentStrategyScore(
    68|        hook=StrategySignal(9, "Strong contradiction."),
    69|        curiosity=StrategySignal(8, "Specific open loop."),
    70|        identity=StrategySignal(7, "Somewhat targeted."),
    71|        emotion=StrategySignal(8, "Relief."),
    72|        trust=StrategySignal(8, "Specific example."),
    73|        follow_reason=StrategySignal(4, "Could belong to any AI tips account."),
    74|        originality=StrategySignal(8, "Specific."),
    75|        variety=StrategySignal(8, "Fresh enough."),
    76|        target_emotion="relief",
    77|        viewer_identity="AI users",
    78|        future_value_promise="more tips",
    79|        format_family="One Rule, One Example",
    80|        variation_axis="generic listicle",
    81|    )
    82|
    83|    result = validate_content_strategy_score(score)
    84|
    85|    assert result.is_valid is False
    86|    assert "follow_reason must be at least 7" in result.errors
    87|    assert should_advance_strategy(score) is False
    88|
    89|
    90|def test_build_candidate_strategy_review_blocks_generic_ai_slop_language():
    91|    candidate = _candidate(
    92|        title="5 AI tools you need right now",
    93|        hook="In this video I will show you the best AI tools to unlock your potential.",
    94|        script_outline="Here are 5 amazing AI tools everyone should use.",
    95|        visual_concept="Generic AI stock footage and floating robot icons.",
    96|        caption="Unlock your full potential with these AI tools.",
    97|        cta="Follow for more AI tips.",
    98|        policy_notes=("duration_class:growth_short",),
    99|    )
   100|
   101|    review = build_candidate_strategy_review(
   102|        candidate,
   103|        recent_format_families=("AI Tools List", "AI Tools List"),
   104|    )
   105|
   106|    assert review.render_allowed is False
   107|    assert "generic AI-slop language detected" in review.reject_reasons
   108|    assert "future value promise is not distinctive enough" in review.reject_reasons
   109|    assert "recent format repetition risk" in review.reject_reasons
   110|
   111|
   112|def test_build_candidate_strategy_review_blocks_forbidden_public_topics():
   113|    candidate = _candidate(
   114|        title="How JARVIS and the user built this approval workflow",
   115|        hook="This is the private collaboration behind the content machine.",
   116|        script_outline="Show our JARVIS build log and personal workflow.",
   117|        visual_concept="Private screenshots and internal chat timeline.",
   118|        caption="Our collaboration is the moat.",
   119|        cta="Follow for the next JARVIS build log.",
   120|        policy_notes=("series:jarvis_build_log",),
   121|    )
   122|
   123|    review = build_candidate_strategy_review(candidate)
   124|
   125|    assert review.render_allowed is False
   126|    assert "candidate uses forbidden public topic: jarvis" in review.reject_reasons
   127|    assert "candidate uses forbidden public topic: collaboration" in review.reject_reasons
   128|    assert "candidate uses forbidden public topic: private" in review.reject_reasons
   129|
   130|
   131|def test_build_candidate_strategy_review_allows_distinctive_systems_candidate():
   132|    candidate = _candidate()
   133|
   134|    review = build_candidate_strategy_review(
   135|        candidate,
   136|        recent_format_families=("AI Hype Court", "One Rule, One Example"),
   137|    )
   138|
   139|    assert review.render_allowed is True
   140|    assert review.strategy_score.format_family == "Prompt vs System"
   141|    assert review.strategy_score.viewer_identity == "serious AI workflow builders"
   142|    assert review.reject_reasons == ()
   143|    assert review.improvement_suggestions == ()
   144|


Explanation:
**Considering updates and modules**

I need to look for updates and possibly check the documentation. I wonder if I should create a new module for the strategy or if it might be simpler to patch the existing one. I'm thinking about generating review packages as well and maybe writing a script for that. Also, I need to search in the /tmp directory for a fake invoice pack. There's a lot to consider here!

digital-red-flags-fake-invoice-payment-route-30s|fake invoice|changed payment route|payment route


     1|"""Video candidate domain model and validation."""
     2|
     3|from __future__ import annotations
     4|
     5|from dataclasses import dataclass
     6|
     7|from autoshorts.review.package import ReviewPackage
     8|from autoshorts.scripting.quality import ScriptQualityScore
     9|
    10|SUPPORTED_PLATFORMS = ("youtube", "tiktok", "instagram")
    11|
    12|
    13|@dataclass(frozen=True)
    14|class ValidationResult:
    15|    is_valid: bool
    16|    errors: tuple[str, ...] = ()
    17|
    18|
    19|@dataclass(frozen=True)
    20|class VideoCandidate:
    21|    candidate_id: str
    22|    pillar: str
    23|    title: str
    24|    platforms: tuple[str, ...]
    25|    duration_seconds: int
    26|    hook: str
    27|    script_outline: str
    28|    visual_concept: str
    29|    caption: str
    30|    cta: str
    31|    hashtags: tuple[str, ...]
    32|    quality_score: ScriptQualityScore
    33|    policy_notes: tuple[str, ...]
    34|    hypothesis: str
    35|
    36|    def to_review_package(self, preview_path: str | None = None) -> ReviewPackage:
    37|        return ReviewPackage(
    38|            candidate_id=self.candidate_id,
    39|            title=self.title,
    40|            pillar=self.pillar,
    41|            duration_seconds=self.duration_seconds,
    42|            hook=self.hook,
    43|            script_summary=self.script_outline,
    44|            visual_concept=self.visual_concept,
    45|            caption=self.caption,
    46|            hashtags=self.hashtags,
    47|            platforms=self.platforms,
    48|            quality_score=self.quality_score,
    49|            policy_notes=self.policy_notes,
    50|            preview_path=preview_path,
    51|        )
    52|
    53|
    54|def _is_blank(value: str) -> bool:
    55|    return not (value or "").strip()
    56|
    57|
    58|def validate_candidate(candidate: VideoCandidate) -> ValidationResult:
    59|    errors: list[str] = []
    60|
    61|    required_fields = {
    62|        "candidate_id": candidate.candidate_id,
    63|        "pillar": candidate.pillar,
    64|        "title": candidate.title,
    65|        "hook": candidate.hook,
    66|        "script_outline": candidate.script_outline,
    67|        "visual_concept": candidate.visual_concept,
    68|        "caption": candidate.caption,
    69|        "cta": candidate.cta,
    70|        "hypothesis": candidate.hypothesis,
    71|    }
    72|    for field_name, value in required_fields.items():
    73|        if _is_blank(value):
    74|            errors.append(f"{field_name} is required")
    75|
    76|    if not 10 <= candidate.duration_seconds <= 180:
    77|        errors.append("duration_seconds must be between 10 and 180")
    78|
    79|    if not candidate.platforms:
    80|        errors.append("at least one platform is required")
    81|    for platform in candidate.platforms:
    82|        if platform not in SUPPORTED_PLATFORMS:
    83|            errors.append(f"unsupported platform: {platform}")
    84|
    85|    return ValidationResult(is_valid=not errors, errors=tuple(errors))
    86|


     1|"""Telegram review-package formatting for content candidates."""
     2|
     3|from __future__ import annotations
     4|
     5|from dataclasses import dataclass
     6|
     7|from autoshorts.scripting.quality import ScriptQualityScore, should_render
     8|
     9|_PLATFORM_LABELS = {
    10|    "youtube": "YouTube",
    11|    "tiktok": "TikTok",
    12|    "instagram": "Instagram",
    13|}
    14|
    15|
    16|@dataclass(frozen=True)
    17|class ReviewPackage:
    18|    """Everything Sir needs to approve, reject, or request changes.
    19|
    20|    This structure is deterministic by design: strategy/rendering code can fill
    21|    it, Telegram code can send the rendered text, and publishing code can wait
    22|    for an explicit parsed approval decision.
    23|    """
    24|
    25|    candidate_id: str
    26|    title: str
    27|    pillar: str
    28|    duration_seconds: int
    29|    hook: str
    30|    script_summary: str
    31|    visual_concept: str
    32|    caption: str
    33|    hashtags: tuple[str, ...]
    34|    platforms: tuple[str, ...]
    35|    quality_score: ScriptQualityScore
    36|    policy_notes: tuple[str, ...] = ()
    37|    preview_path: str | None = None
    38|
    39|
    40|def _join_or_dash(items: tuple[str, ...]) -> str:
    41|    return "; ".join(items) if items else "-"
    42|
    43|
    44|def _platform_names(platforms: tuple[str, ...]) -> str:
    45|    labels = [_PLATFORM_LABELS.get(platform, platform) for platform in platforms]
    46|    return ", ".join(labels) if labels else "-"
    47|
    48|
    49|def _approval_commands(platforms: tuple[str, ...]) -> list[str]:
    50|    commands = ["Freigabe alle"]
    51|    for platform in platforms:
    52|        label = _PLATFORM_LABELS.get(platform, platform.title())
    53|        commands.append(f"Freigabe {label}")
    54|    commands.extend(["Ablehnen", "Ändern: <Wunschänderung>"])
    55|    return commands
    56|
    57|
    58|def render_telegram_review_package(package: ReviewPackage) -> str:
    59|    """Render a concise Telegram-friendly review message."""
    60|
    61|    score = package.quality_score
    62|    render_allowed = should_render(score)
    63|    gate_line = "Render gate: READY" if render_allowed else "Render gate: BLOCKED — nicht automatisch rendern"
    64|    hashtags = " ".join(package.hashtags) if package.hashtags else "-"
    65|    preview = package.preview_path or "-"
    66|    commands = "\n".join(f"- `{command}`" for command in _approval_commands(package.platforms))
    67|
    68|    return "\n".join(
    69|        [
    70|            f"## Review: {package.candidate_id}",
    71|            f"Title: {package.title}",
    72|            f"Pillar: {package.pillar}",
    73|            f"Platforms: {_platform_names(package.platforms)}",
    74|            f"Duration: {package.duration_seconds}s",
    75|            "",
    76|            f"Hook: {package.hook}",
    77|            f"Script: {package.script_summary}",
    78|            f"Visual: {package.visual_concept}",
    79|            f"Caption: {package.caption}",
    80|            f"Hashtags: {hashtags}",
    81|            "",
    82|            f"Quality: {score.average_quality:.1f}/10",
    83|            f"AI-slop risk: {score.ai_slop_risk}/10",
    84|            f"Policy: {_join_or_dash(package.policy_notes)}",
    85|            gate_line,
    86|            f"Preview: {preview}",
    87|            "",
    88|            "Approval commands:",
    89|            commands,
    90|        ]
    91|    )
    92|


     1|{
     2|  "allowed_image_provider": "openai-codex",
     3|  "preferred_image_model": "best_available_gpt_image_model",
     4|  "preferred_quality": "highest_available_reasonable",
     5|  "max_styleframes_per_scene_without_approval": 3,
     6|  "max_retries_per_styleframe": 1,
     7|  "no_random_batches": true,
     8|  "no_ai_images_for_mockups_when_deterministic_renderer_is_better": true,
     9|  "deterministic_mockup_assets": [
    10|    "fake_invoice",
    11|    "invoice_mockup",
    12|    "fake_email_screen",
    13|    "payment_card",
    14|    "ui_mockup",
    15|    "screen_mockup"
    16|  ],
    17|  "notes": "Premium image generation only for approved styleframes/keyframes. Fake invoice/email/payment-route mockups remain renderer-owned and deterministic."
    18|}
    19|


     1|# Image Generation Guidelines for AutoShortsBot
     2|
     3|These rules apply to premium styleframes/keyframes only. Renderer-owned mockups (fake invoices, emails, payment cards, UI screens) should remain deterministic unless explicitly approved otherwise.
     4|
     5|## Prompt Template
     6|
     7|```text
     8|AutoShortsBot premium keyframe, vertical 9:16 social-video frame.
     9|Scene role: <hook / fail / mechanism / fix / takeaway>.
    10|Visible real-world problem: <what is concretely broken in the first second>.
    11|Subject and environment: <specific desk/screen/room/object setup>.
    12|Composition: <camera angle, foreground/midground/background, focal point, negative space, safe margins>.
    13|Lighting and style: <realistic product-film / premium social video / clean documentary>.
    14|Motion-readiness: startframe suitable for subtle push-in or I2V; stable subject; no chaotic elements.
    15|Renderer-owned overlays: leave clean safe space for captions/labels; no baked text.
    16|Forbidden elements: no logos, brands, QR codes, real bank data, readable personal data, fake legal/banking details, watermark, pseudo text, random abstract glass clouds.
    17|Output: text-free image, 9:16, high fidelity, realistic, immediately understandable without voiceover.
    18|```
    19|
    20|## Quality Checklist
    21|
    22|- The frame shows a real viewer problem without relying on the voiceover.
    23|- The focal point is obvious within one second.
    24|- The scene is not interchangeable with 50 other AI/productivity videos.
    25|- The image is text-free or uses only intentionally blank/generic surfaces.
    26|- There is safe negative space for deterministic captions/labels.
    27|- The scene can connect to the previous and next scene through consistent objects, color, camera logic, and problem state.
    28|- No brand, logo, QR code, real company, real person, IBAN, routing number, account number, invoice number, address, or pseudo-legal document detail appears.
    29|- No AI artifacts that distract on mobile: warped hands, broken UI, nonsense writing, impossible reflections, random symbols.
    30|
    31|## Rejection Criteria
    32|
    33|Reject the styleframe if any of these are true:
    34|
    35|- Pretty Empty Visual Blocker: it looks premium but does not show a concrete visible fail.
    36|- It needs the voiceover to explain what problem exists.
    37|- It is generic laptop B-roll, abstract SaaS glassmorphism, mood-only B-roll, or a random futuristic metaphor.
    38|- It contains baked text, pseudo-writing, fake logos, QR codes, or fake-real financial/legal details.
    39|- It is not mobile-legible at 9:16.
    40|- It cannot be used as a stable startframe/endframe for scene continuity.
    41|- It violates the deterministic-mockup rule for UI/invoice/email/payment assets.
    42|
    43|## Styleframe Budget Policy
    44|
    45|- `allowed_image_provider = openai-codex`
    46|- `preferred_image_model = best_available_gpt_image_model`
    47|- `preferred_quality = highest_available_reasonable`
    48|- Use `gpt-image-2-high` for final/high-fidelity keyframes when available.
    49|- Use `gpt-image-2-medium` only for balanced exploration when high is not needed or unavailable.
    50|- Maximum styleframes per scene without explicit approval: `3`.
    51|- Maximum retries per styleframe: `1`.
    52|- No random batches. Every generated image must have a scene role, rejection criteria, and storage path.
    53|- Stop when one image passes the frame's job; do not keep generating just because the next one might be prettier. We are not mining JPEGs like a Victorian coal baron.
    54|
    55|## Scene Consistency Rules
    56|
    57|- Keep recurring objects stable: same desk, same generic document shape, same device orientation, same color family.
    58|- Use consistent camera language: e.g. close-up → route-change insert → verification action → takeaway.
    59|- Keep factual text, arrows, labels, UI words, numbers, warnings, and captions renderer-owned.
    60|- Use AI images for atmosphere, realism, and premium texture only.
    61|- For startframe/endframe planning, specify the changed state explicitly: e.g. `start: normal invoice route visible as blank field`, `end: route field visually highlighted by renderer overlay later`.
    62|
    63|## Text/Logo/Pseudo-Writing Blocker
    64|
    65|AI-generated images must not contain:
    66|
    67|- readable text intended as a claim or instruction
    68|- fake company names, fake bank details, fake invoice IDs, QR codes, barcodes
    69|- real logos, product marks, app icons, bank names, government marks
    70|- pseudo-writing that looks like real legal/financial information
    71|- watermarks or provenance labels baked visibly into the image
    72|
    73|If a scene needs text, render it deterministically after generation. For Fake-Invoice Proof Shorts, use deterministic renderer-owned mockups only; do not use AI image generation for the invoice, email, payment route, or checks.
    74|


     1|"""Manual platform copy packs for phase-1 human uploads."""
     2|
     3|from __future__ import annotations
     4|
     5|from dataclasses import dataclass
     6|
     7|from autoshorts.ideas.candidate import ValidationResult
     8|from autoshorts.social.draft import SocialMediaDraft, validate_social_media_draft
     9|
    10|
    11|@dataclass(frozen=True)
    12|class ManualPlatformPost:
    13|    platform: str
    14|    title: str
    15|    description: str
    16|    hashtags: tuple[str, ...]
    17|    tags: tuple[str, ...]
    18|    instructions: str
    19|
    20|
    21|@dataclass(frozen=True)
    22|class ManualPostingPack:
    23|    candidate_id: str
    24|    phase: str
    25|    video_path: str
    26|    platform_posts: tuple[ManualPlatformPost, ...]
    27|    requires_human_manual_upload: bool = True
    28|    side_effects: tuple[str, ...] = ()
    29|    external_calls: tuple[str, ...] = ()
    30|
    31|
    32|def _is_blank(value: str) -> bool:
    33|    return not (value or "").strip()
    34|
    35|
    36|def _normalize_hashtag_to_tag(hashtag: str) -> str:
    37|    return hashtag.lstrip("#").strip()
    38|
    39|
    40|def _hashtag_block(hashtags: tuple[str, ...]) -> str:
    41|    return " ".join(hashtags)
    42|
    43|
    44|def _youtube_description(draft: SocialMediaDraft) -> str:
    45|    return "\n\n".join(
    46|        part
    47|        for part in (
    48|            draft.caption.strip(),
    49|            draft.cta.strip(),
    50|            _hashtag_block(draft.hashtags),
    51|        )
    52|        if part
    53|    )
    54|
    55|
    56|def _tiktok_caption(draft: SocialMediaDraft) -> str:
    57|    caption = f"{draft.caption.strip()} {_hashtag_block(draft.hashtags)}".strip()
    58|    return caption[:2200]
    59|
    60|
    61|def _post_for_platform(draft: SocialMediaDraft, platform: str) -> ManualPlatformPost:
    62|    title = draft.title_variants[0]
    63|    if platform == "youtube":
    64|        return ManualPlatformPost(
    65|            platform="youtube",
    66|            title=title,
    67|            description=_youtube_description(draft),
    68|            hashtags=draft.hashtags,
    69|            tags=tuple(_normalize_hashtag_to_tag(hashtag) for hashtag in draft.hashtags),
    70|            instructions="Manual upload: copy this description into YouTube Shorts.",
    71|        )
    72|    if platform == "tiktok":
    73|        return ManualPlatformPost(
    74|            platform="tiktok",
    75|            title=title,
    76|            description=_tiktok_caption(draft),
    77|            hashtags=draft.hashtags,
    78|            tags=(),
    79|            instructions="Manual upload: copy this caption into TikTok.",
    80|        )
    81|    if platform == "instagram":
    82|        return ManualPlatformPost(
    83|            platform="instagram",
    84|            title=title,
    85|            description=_tiktok_caption(draft),
    86|            hashtags=draft.hashtags,
    87|            tags=(),
    88|            instructions="Manual upload: copy this caption into Instagram Reels.",
    89|        )
    90|    raise ValueError(f"unsupported platform: {platform}")
    91|
    92|
    93|def build_manual_posting_pack(draft: SocialMediaDraft, *, video_path: str) -> ManualPostingPack:
    94|    draft_validation = validate_social_media_draft(draft)
    95|    if not draft_validation.is_valid:
    96|        raise ValueError("; ".join(draft_validation.errors))
    97|    pack = ManualPostingPack(
    98|        candidate_id=draft.candidate_id,
    99|        phase="manual_posting",
   100|        video_path=video_path,
   101|        platform_posts=tuple(_post_for_platform(draft, platform) for platform in draft.platforms),
   102|        requires_human_manual_upload=True,
   103|        side_effects=(),
   104|        external_calls=(),
   105|    )
   106|    validation = validate_manual_posting_pack(pack)
   107|    if not validation.is_valid:
   108|        raise ValueError("; ".join(validation.errors))
   109|    return pack
   110|
   111|
   112|def validate_manual_posting_pack(pack: ManualPostingPack) -> ValidationResult:
   113|    errors: list[str] = []
   114|    if _is_blank(pack.candidate_id):
   115|        errors.append("candidate_id is required")
   116|    if pack.phase != "manual_posting":
   117|        errors.append("phase must be manual_posting")
   118|    if _is_blank(pack.video_path):
   119|        errors.append("video_path is required")
   120|    if not pack.platform_posts:
   121|        errors.append("at least one platform post is required")
   122|    for post in pack.platform_posts:
   123|        if _is_blank(post.platform):
   124|            errors.append("platform is required")
   125|        if _is_blank(post.title):
   126|            errors.append(f"{post.platform} title is required")
   127|        if _is_blank(post.description):
   128|            errors.append(f"{post.platform} description is required")
   129|        if not post.hashtags:
   130|            errors.append(f"{post.platform} hashtags are required")
   131|        if _is_blank(post.instructions):
   132|            errors.append(f"{post.platform} instructions are required")
   133|    if not pack.requires_human_manual_upload:
   134|        errors.append("human manual upload is required in phase 1")
   135|    if pack.side_effects:
   136|        errors.append("manual posting packs must not have side effects")
   137|    if pack.external_calls:
   138|        errors.append("manual posting packs must not call external services")
   139|    return ValidationResult(is_valid=not errors, errors=tuple(errors))
   140|
   141|
   142|def render_manual_posting_instructions(pack: ManualPostingPack) -> str:
   143|    validation = validate_manual_posting_pack(pack)
   144|    if not validation.is_valid:
   145|        raise ValueError("; ".join(validation.errors))
   146|
   147|    lines = [
   148|        "## Manual Posting Pack",
   149|        f"Candidate: {pack.candidate_id}",
   150|        f"Video file: {pack.video_path}",
   151|        "No platform upload was performed.",
   152|        "",
   153|    ]
   154|    labels = {
   155|        "youtube": "YouTube Shorts",
   156|        "tiktok": "TikTok",
   157|        "instagram": "Instagram Reels",
   158|    }
   159|    for post in pack.platform_posts:
   160|        lines.extend(
   161|            [
   162|                f"## {labels.get(post.platform, post.platform)}",
   163|                post.instructions,
   164|                f"Title: {post.title}",
   165|                "Description:",
   166|                post.description,
   167|                f"Hashtags: {' '.join(post.hashtags)}",
   168|                f"Tags: {', '.join(post.tags) if post.tags else 'n/a'}",
   169|                "",
   170|            ]
   171|        )
   172|    return "\n".join(lines).strip()
   173|


Explanation:
**Organizing documentation process**

I need to write some code, using a Python script to create various files. It seems like I should include documentation, like a README. I'm considering if I can use the write_file tool with execute_code, though I might need to repeat this for multiple files.

I also plan to create and update several documents, including safety guidelines and various other markdown files. Updating the README and image guidelines is on my list. Additionally, I'll manage packages and create manual posting packs, though it seems I need to be careful about file statuses and media. I'll report any drafts that are pending final media.


terminal

p='/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1.mp4'; ffprobe -v error -select_streams v:0 -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate,pix_fmt -show_entries format=duration -of json "$p" && sha256sum "$p"


Output:
{
    "programs": [

    ],
    "streams": [
        {
            "width": 1080,
            "height": 1920,
            "pix_fmt": "yuv420p",
            "r_frame_rate": "24/1"
        }
    ],
    "format": {
        "duration": "31.400000"
    }
}
a9073f09b80b9b43eb421a4c523e05cf745374fbe7ef33176b082e988a220dfc  /tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1.mp4


for p in /tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/*/*.mp4; do echo '---' $p; ffprobe -v error -select_streams v:0 -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate,pix_fmt -show_entries format=duration -of default=nw=1 "$p" | head -20; done


Output:
--- /tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1.mp4
width=1080
height=1920
sample_aspect_ratio=N/A
display_aspect_ratio=N/A
pix_fmt=yuv420p
r_frame_rate=24/1
duration=31.400000
--- /tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v1/digital-red-flags-fake-invoice-payment-route-30s_preview_v1.mp4
width=1080
height=1920
sample_aspect_ratio=N/A
display_aspect_ratio=N/A
pix_fmt=yuv420p
r_frame_rate=24/1
duration=31.291667
--- /tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v1/digital-red-flags-fake-invoice-payment-route-30s_preview_v1_silent.mp4
width=1080
height=1920
sample_aspect_ratio=N/A
display_aspect_ratio=N/A
pix_fmt=yuv420p
r_frame_rate=24/1
duration=31.291667
--- /tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4
width=1080
height=1920
sample_aspect_ratio=N/A
display_aspect_ratio=N/A
pix_fmt=yuv420p
r_frame_rate=24/1
duration=31.320000
--- /tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe_silent.mp4
width=1080
height=1920
sample_aspect_ratio=N/A
display_aspect_ratio=N/A
pix_fmt=yuv420p
r_frame_rate=24/1
duration=31.333333
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