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video-recap

The video-recap skill orchestrates a five-stage pipeline that transforms raw video into a narrated recap by chaining video understanding, script generation, cutting, voiceover synthesis, and assembly. Use it when you need to automatically analyze video content, generate custom narration, optionally edit scenes to a target duration, and produce a final dubbed recap video with optional subtitles, resuming safely after manual narration approval.

Instalar en Claude Code
Copiar
git clone --depth 1 https://github.com/worldwonderer/video-recap-skills /tmp/video-recap && cp -r /tmp/video-recap/skills/video-recap ~/.claude/skills/video-recap
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

## What this is

A thin orchestrator over five independent, self-contained skills (each in `skills/`, sharing only
JSON/MP4 artifacts in a `work_dir` — no shared code):

```
video-understanding ─▶ (agent writes narration.json per video-script) ─▶ [video-cut] ─▶ video-voiceover ─▶ video-assemble
```

It is resume-safe: rerun the same command after writing `narration.json` to continue.
Phase B validates `recap_run_manifest.json` so an old `work_dir` from another source video or
different run settings is rejected instead of silently reusing stale narration. Understanding
artifacts are reused only when their provenance matches. For per-stage detail, read each skill's own SKILL.md.

## Install / env

```bash
# ffmpeg: brew install ffmpeg | apt install ffmpeg | choco install ffmpeg
export MIMO_API_KEY=***          # ONE key drives ASR + VLM + TTS (all MiMo)
```

The whole pipeline runs on ffmpeg + a single MiMo key: ASR (`mimo-v2.5-asr`), VLM (`mimo-v2.5`),
TTS (`mimo-v2.5-tts`). `tp-*` Token Plan keys default to the cn cluster (`MIMO_TOKEN_PLAN_CLUSTER`).
Optional MiMo scene-chunk video understanding: `--mimo-video-overview`.

Overridable defaults (zero-config otherwise): see `references/config-playbook.md`.

## Use

### 0. Research first (recommended)

If you can identify the source (show, film, topic), research it **before** analyzing and write
`work_dir/background_research.json` (see `video-understanding/references/research-guide.md`).
video-understanding folds it into the VLM context, so scene analysis can name characters and read
scenes with plot knowledge instead of labelling everyone "黑衣男子". Skip it when you can't research.

### 1. Analyze → pause for narration

```bash
python3 scripts/recap.py <video> --work-dir <work_dir> --context "背景"
```

Runs video-understanding (using `background_research.json` if you wrote it), writes
`agent_narration_brief.md`, and pauses. Then **write `work_dir/narration.json`** following the
**video-script** skill (read the brief first).
Cut mode (`--edit-mode cut --target-duration 10m`) also requires `clip_plan.json`.

### 2. Continue → produce the recap

Rerun the **same command** (narration.json now exists):

```bash
python3 scripts/recap.py <video> --work-dir <work_dir>          # [--edit-mode cut] [--no-burn-subtitles]
```

This validates the narration, (cut: builds `edited_source.mp4`), synthesizes the voiceover, and
assembles `recap_<name>.mp4`.

### Dub mode — English→Chinese, original voice (`--edit-mode dub`)

Translates an English video into Chinese and **replaces** the speech with the ORIGINAL
speaker's cloned voice (`mimo-v2.5-tts-voiceclone`, same MiMo key) — distinct from recap/解说,
which overlays Chinese commentary on ducked audio. Same one-pause shape:

```bash
python3 scripts/recap.py <video> --edit-mode dub --work-dir <work_dir>     # prepare → pauses
```

Prepare transcribes the English audio in timed windows and pulls one reference clip, then writes
`dub_brief.md` + `dub_transcript.json`. The agent does all the judgment (like recap's narration):
**write `work_dir/dub_script.json`** = `[{"start": s, "end": s, "zh": "译文"}, …]` (ascending by
`start`) — translate **every** utterance faithfully on the source timeline and give each its source
`[start, end]` so the dub tracks the original's rhythm (don't drop a hook, merge, or condense; if
the original repeats, the dub repeats in sync). Keep each line speakable within its span (~5
chars/s). Rerun the same command to render `dub_<name>.mp4` — each line is cloned in the original
voice and time-fit to its `[start, end]` (placed at its start; only sped up if it would overrun the
next line, never globally — so the voice tracks the picture). v1: single speaker, full-track
replace (no background-music separation).

### Self-check

```bash
python3 scripts/recap.py --doctor
```

## Output

- `recap_<video>.mp4` — final video · `subtitles.srt` / `.ass` — subtitles
- `work_dir/` — all intermediate artifacts (the inter-skill contract; see `references/data-schema.md`)

## Options (passed through to the stage skills)
`--context`, `--scene-threshold`, `--style`, `--edit-mode {full,cut,dub}`, `--target-duration`,
`--skip-asr`, `--mimo-video-overview`, `--consolidate`, `--consolidate-asr`, `--mimo-tts-voice`,
`--no-burn-subtitles` (burn is on by default), `--output-dir`.

## What this skill does NOT do
- Does NOT write narration.json / clip_plan.json — the agent authors those (see the video-script skill).
- Does NOT hard-block on the narration review (advisory; validate.py is the hard gate).
- Is NOT an unattended scheduler — it is human-in-the-loop and posts to no channel.
- Shares NO code between stage skills — they communicate only through work_dir artifacts.