generate-title
One title per clip. Third-person, names the subject, promises a specific
moment, ALL CAPS, ≤7 words. Designed for the title-transition skill.
Invoke
.claude/skills/generate-title/generate-title.sh <clip_transcript.json> <ingest.json> <out.txt> [title-context.json]
clip_transcript: clip-local transcript with words[] and/or segments[]ingest: the source video's ingest.json (provides title, uploader, url —
helps name the subject when the transcript doesn't say it explicitly)out: text file with the title (single line, ALL CAPS, ≤7 words)title-context (optional): pick-segments' per-span judgment —
{topic, rationale, title_suggestion} — sliced out of segments.json by
the orchestrator. The clip-local transcript alone can't carry the
speaker's register, so a line read literally can invert the meaning (an
ironic "what he refuses to think about" titled as a sincere confession).
When present, the model reads tone from this context FIRST (Step 0:
sincere / ironic / joking / provocative) and titles the actual point, not
the surface words. Never shown to the viewer; omitting it falls back to
the old clip-only behavior.
Output
A single-line text file. Example contents:
WATCH SPEED RAGE AT STREAM SNIPERS
Prompt principles (enforced)
- Third person — never "I", "me", "my", "we".
- Name the subject (inferred from clip transcript + ingest.json).
- Promise ONE specific moment, behavior, or reaction — not a vague topic.
- ≤7 words, ALL CAPS, no emoji, no clickbait punctuation (no "?" / "!" / "..." / quotes).
- No "Watch X..." filler when it doesn't add a hook; only use opener verbs
when they sharpen the promise.
Fallback
If claude -p fails or the model returns something unusable (empty, >7 words,
contains banned punctuation), parse_reply.py falls back to the first 5
non-filler words from the clip transcript, uppercased.
1---2name: generate-title3description: Generate a per-clip third-person engagement-driven title card text. Claude reads the clip's transcript, the source ingest.json metadata, and (optionally) pick-segments' per-span judgment (topic, rationale, suggested title) so it reads the speaker's register — sincere vs ironic vs joking — before titling. Emits a hook-driven ALL-CAPS title (<=7 words).4---56# generate-title78One title per clip. Third-person, names the subject, promises a specific9moment, ALL CAPS, ≤7 words. Designed for the title-transition skill.1011## Invoke1213```14.claude/skills/generate-title/generate-title.sh <clip_transcript.json> <ingest.json> <out.txt> [title-context.json]15```1617- `clip_transcript`: clip-local transcript with `words[]` and/or `segments[]`18- `ingest`: the source video's ingest.json (provides title, uploader, url —19 helps name the subject when the transcript doesn't say it explicitly)20- `out`: text file with the title (single line, ALL CAPS, ≤7 words)21- `title-context` (optional): pick-segments' per-span judgment —22 `{topic, rationale, title_suggestion}` — sliced out of `segments.json` by23 the orchestrator. The clip-local transcript alone can't carry the24 speaker's register, so a line read literally can invert the meaning (an25 ironic "what he refuses to think about" titled as a sincere confession).26 When present, the model reads tone from this context FIRST (Step 0:27 sincere / ironic / joking / provocative) and titles the actual point, not28 the surface words. Never shown to the viewer; omitting it falls back to29 the old clip-only behavior.3031## Output3233A single-line text file. Example contents:3435```36WATCH SPEED RAGE AT STREAM SNIPERS37```3839## Prompt principles (enforced)4041- Third person — never "I", "me", "my", "we".42- Name the subject (inferred from clip transcript + ingest.json).43- Promise ONE specific moment, behavior, or reaction — not a vague topic.44- ≤7 words, ALL CAPS, no emoji, no clickbait punctuation (no "?" / "!" / "..." / quotes).45- No "Watch X..." filler when it doesn't add a hook; only use opener verbs46 when they sharpen the promise.4748## Fallback4950If `claude -p` fails or the model returns something unusable (empty, >7 words,51contains banned punctuation), `parse_reply.py` falls back to the first 552non-filler words from the clip transcript, uppercased.
jperrello