Personal Clipper

Personal clipper playbook

Turn one long-form video / stream / podcast into a handful of short vertical clips. The agent reads the source's word-level transcript, picks the strongest self-contained windows, and cuts each into a 9:16 clip through the ralphy clip verb — then captions, renders, evaluates, and packages the survivors. This is the personal-clipper content mode (#436), a supported first-class route. NOT a magic "viral moment detector": the windows are an agent decision grounded in the transcript, and the verb only executes the cut.

Sub-docs (read on demand)

When this mode fires

A brief that points at one long-form source and asks for short cuts: "cut my stream into shorts", "clip the best moments out of this podcast", "make 5 TikToks from this talk", "turn my 40-minute VOD into clips", "extract the highlights". The deterministic classifier (classifyContentMode) scores these to personal-clipper. It is a SUPPORTED route — promise it.

Contrast with the neighbours:

• A long-form video built ON TOP of the audio (overlays, faceless explainer) is podcast-video (the audio-explainer skill), not clip extraction.
• A generated talking-head short is ugc-review / tutorial-ugc, not a cut from an existing source.

Source requirements + limits

• Source: a video the user owns or has the right to clip (a VOD, stream export, webinar, talk, long podcast with video). Pull a URL with ralphy ref pull <url>; a local file is fine too.
• Minimum source length: roughly > 3 min — below that, just trim with ralphy clip directly, there is nothing to "select".
• Clip target duration: 15-90s each (the short-form sweet spot). Default to ~30-60s.
• Clip count: only as many as the source actually supports. Do NOT pad to a requested number with weak windows (see the stop rule below).
• Reference gate (AGENTS #3): the source is the user's own footage — no model-reference gate fires. The gate only fires if a generated overlay later introduces a named real entity.

The flow (one beat at a time, checkpoints between)

1. Ingest the source. ralphy ref pull <url> (yt-dlp behind the verb) or point at the local file. Never shell out to yt-dlp / ffmpeg directly (AGENTS #2).
2. Transcribe. ralphy ref transcribe <slug> --language <lang> → a word-level transcript (transcript.json). Confirm the language with the user first when non-English.
3. Select highlight windows (the agent's craft). Read the transcript. Pick self-contained windows: a complete thought with a hook in the first ~2s and a clean out. Each window is a [from, to) pair (word-level startMs → seconds). This is judgement grounded in the transcript text — NOT a detector the verb runs. Present the candidate windows (timestamp + the quoted line) and wait for the user's go before cutting.
4. Cut each window. ralphy clip <source> --from <ts> --to <ts> --vertical --project <id> per window. --vertical centre-crops to 9:16 (1080x1920); omit it to keep the source aspect. Output lands in <project>/artifacts/videos/ (append-only, auto-versioned). Run the cuts in parallel — they are independent ffmpeg processes.
5. Captions. ralphy generate captions on each clip (Scribe word-level), then bake / overlay per the editor playbook. Snap caption timing to startMs (AGENTS #16) — never hand-write it.
6. Render / bake. ralphy render <id> is the only render path when a clip needs a HyperFrames composition (caption overlay, hook card). A bare crop+caption bake can stay an artifacts/videos/ file promoted into a Unit.
7. Evaluate readiness. ralphy project scorecard <id> (#427) for the deterministic ship/repair verdict per clip; /evaluator for a deeper scroll-stop / hook pass. A failed gate refuses (AGENTS #4) — do not ship over it.
8. Form + distribute Units. ralphy unit create <id> --slug <clip-slug> --format video --from "artifacts/videos/<clip>.mp4" per surviving clip, then ralphy unit package <id> <slug> (#423) for the platform-spec'd distribution pack. ralphy unit caption for the post copy.

The "no good clips found" outcome (mandatory)

If the transcript yields no self-contained, hook-bearing windows — a meandering stream with no quotable moments, an interview with no punchy beats, a source that is one long unbroken explanation — STOP and tell the user. Do NOT force weak clips to hit a count. The honest output is "I read the transcript and there are no clips here worth cutting; here is why" plus the closest alternative (e.g. "this reads better as a podcast-video overlay edit" or "give me a richer source"). A handful of strong clips beats a dozen forgettable ones — padding the count is the failure this mode exists to avoid.

ralphy clip flag surface

ralphy clip <source> --from <ts> --to <ts> [--vertical] [--out <path>] [--project <id>]

The cut is re-encoded for frame-accurate boundaries, so a window chosen from a transcript lands on the spoken word rather than the nearest keyframe.

Common failure modes

• Treating ralphy clip as a detector — it is not; the agent picks the windows from the transcript and calls the verb per window.
• Hand-writing timestamps — derive every --from / --to from the transcript's word-level startMs.
• Padding to a requested clip count — invoke the stop rule instead.
• Raw ffmpeg / yt-dlp — every step is a ralphy verb (AGENTS #2).
• Vertical-cropping a clip whose subject lives at the frame edges — a centre crop loses it; keep source aspect (drop --vertical) or reframe in the editor.