Reel Scout

English ｜ 繁體中文

Short-form video analysis CLI tool.

Crawl, transcribe, and visually analyze YouTube Shorts, Instagram Reels, and TikTok videos into structured data.

How it works

flowchart LR
  U([URL]) --> C[crawl<br/>yt-dlp]
  C --> T[transcribe<br/>Whisper]
  C --> K[keyframes<br/>ffmpeg]
  K --> V[vision<br/>VLM / agent]
  T --> A[analyze<br/>merge]
  V --> A
  A --> S[score<br/>craft rubric]
  S --> I[inspect / view<br/>interactive]
  S --> E[export<br/>json · csv · bundle]

Keyframes are ffmpeg, not a model, so the frames exist before any model runs — which is why an agent can stand in for the VLM/score stage when no local model is present (the L1 tier). Craft scores are a reference, not a verdict.

Install

pip install reel-scout
reel-scout skill install       # only if you drive it from Claude — see below

ffmpeg must be on PATH (macOS: brew install ffmpeg). yt-dlp comes with the package. Extras: whisper (faster-whisper transcription), audio (audio events

• BPM), ocr, diarize, instagram — e.g. pip install "reel-scout[whisper]".

Using it from Claude

pip install gives you the CLI. The skill is the half an agent reads: SKILL.md (the pipeline procedure and the capability/surface matrix), the /scout slash command, and the reverse-decode prompts/. reel-scout skill install copies them to ~/.claude/skills/reel-scout (--dest to put them elsewhere, --force to overwrite). Restart Claude Code, then:

/scout https://www.instagram.com/reel/XXXXXXXX/

reel-scout skill path shows where the assets are being read from.

No local model? You do not need one. Keyframe extraction is ffmpeg, so the frames exist before any model runs — the skill's L1 tier has the agent describe them and apply the craft rubric itself, then writes that back with reel-scout ingest. No API key, no cloud, no GPU. See SKILL.md.

From a clone, for development:

pip install -e ".[dev]"

Usage

reel-scout crawl "https://youtube.com/shorts/xxxxx"
reel-scout analyze "https://youtube.com/shorts/xxxxx"
reel-scout analyze --file urls.txt --skip-vision
reel-scout list
reel-scout show <video_id>
reel-scout inspect <video_id>          # interactive single-clip inspector web app
reel-scout export --format json -o ./export
reel-scout config check

inspect starts a small local web app for one clip and opens it in the browser. The video player is the single source of truth: a waveform (ffmpeg peaks, cached) with a click-to-seek playhead, a keyframe filmstrip, and the transcript all seek the player and highlight as it plays. Set IN/OUT markers on the waveform and export the trimmed window as SRT. Needs the downloaded video file on disk. (Design ported from arkiv's live inspector.) export --format html remains the offline multi-clip bundle; view is the library browsing server.

Craft scores are a reference, not a verdict — and the page shows why. The four dimensions come from a model, and the same clip scores differently across models, so the number is never the point. A collapsed re-weight panel lets you drag the weighting of the four dimensions and watch overall recompute live, your blend beside the stored default. The dimensions themselves never move — only how they are combined — so you can see exactly how much the verdict depends on what you value.

Interface language toggle (EN / 中文). The inspector and the view server carry both English and Traditional Chinese in the page: an instant client-side switch that follows your browser's language on first load and remembers your choice. Only interface labels translate — the model's own output (transcript, descriptions, decoded values) is left exactly as produced. (This is the interface language; for bilingual audio transcription, see the section below.)

Instagram links

Paste the link however Instagram gave it to you. Both forms work — the canonical one and the account-scoped one the share button produces:

https://www.instagram.com/reel/Da3UcDsudRN/                    # canonical
https://www.instagram.com/zacharywinterton/reel/Da3UcDsudRN/   # share button

Most public reels need no login. In a 24-clip batch run, 20 of 21 Instagram reels downloaded fine with no cookies at all; one hit the login wall and failed in about three seconds with Instagram sent an empty media response.

So treat cookies as a retry step, not a prerequisite — run the batch first, then re-run the handful that failed:

# 1. export cookies for the reels that failed (Chrome or Brave; Safari's
#    binarycookies file is not readable)
yt-dlp --cookies-from-browser chrome --cookies /tmp/ig_cookies.txt \
       --skip-download "https://www.instagram.com/reel/<code>/"

# 2. retry just those
IG_COOKIES_FILE=/tmp/ig_cookies.txt reel-scout analyze "<url>" --score

The cookie file contains a live Instagram session. Keep it out of the repo and out of any synced folder, and delete it when the retry is done.

Timing. An Instagram reel costs roughly the same regardless of length — there is no subtitle track to read, so every clip is transcribed locally. Budget around 3 minutes per reel whether it runs 10 seconds or 70. (YouTube is the opposite: a video that ships subtitles skips transcription entirely and finishes far faster than its runtime suggests.)

Bilingual / code-switching audio (中英對照)

Whisper large-v3 locks onto the language it detects in the opening window and, on long files, "translates" later speech of the other language back into the locked one — a code-switching interview (Chinese host + English guest) comes out with the guest's English mangled into garbled Chinese. It is a long-form drift, not a bad audio issue: the same passage transcribes perfectly when sliced out on its own.

Fix — force per-chunk language re-detection:

WHISPER_MULTILINGUAL=1 WHISPER_CHUNK_LENGTH=15 reel-scout analyze "<url>"

multilingual alone is not enough — it needs a short chunk_length (~15s) so each chunk re-detects. Verified on a 40-min ZH-host/EN-guest interview: latin-char recovery 56% → 90%. Leave OFF for single-language short-form (per-chunk detection adds cost). Other levers: WHISPER_LANGUAGE=en (force one language), WHISPER_TASK=translate (force English output).

MCP Server

An agent can drive the whole pipeline over MCP — no shell needed.

reel-scout mcp install    # register the server in the client's config (no hand-editing JSON)
reel-scout mcp path       # show where it's registered
reel-scout-mcp            # or run it directly (stdio transport)

Tools cover both sides: read — list_videos, show_video, patterns, inspire, research, and a keyframes tool so an agent with no filesystem can still see the extracted frames; write — ingest_vision / ingest_score / ingest_analysis, mark and annotate, a background batch_start / batch_status / batch_cancel, and inspect. This is how the L1 tier works: an agent that can see images supplies the visual layer and craft score itself, so results land in show / view / inspect / export instead of a chat log.

Prompt Pack (analysis layer)

Reel Scout's pipeline gets clean input into a model. The reverse-decode prompt pack in prompts/ is the analysis brain you point at that input — to reverse-engineer why a short-form video works and extract a transferable structure, with anti-hallucination guardrails (observation vs. inference, cite the timestamp). Open (MIT). See prompts/README.md.

Requirements

• Python 3.9+
• ffmpeg
• yt-dlp

Attribution

Video extraction techniques (captions-first transcription, duration-aware frame budgeting, time-range focus, on-screen-text resolution bump) are adapted from claude-video (MIT). See NOTICE.