Derive thesis

Skill jperrello/C0BALT_CUT/.claude/skills/derive-thesis

Investigation on using Claude Code to automatically generate profitable YouTube videos.

Install

npx -y skills add jperrello/C0BALT_CUT --skill derive-thesis

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

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What its author says it does

Copied from the file, not written here

Name the CENTRAL SUBJECT (spine) of a long-form source so selection can tell on-theme moments from clip-shaped tangents. Claude reads the topic chapter list (topics.json) + source duration and emits thesis.json = {subject, thesis_sentence, key_threads[]} — the one durable source-level "what is this video about" artifact the pipeline lacked. Runs AFTER segment-topics and BEFORE pick-segments; pick-segments feeds it as a theme prior + scores each pick's theme_fit against it, and the confidence-floor backfill is gated on it so a high-standalone but off-spine tangent can no longer be auto-injected. Deterministic fallback (subject from the longest topic) on any failure. Non-fatal, idempotent, DERIVE_THESIS=0 skips.

SKILL.md

2.5 KB, as published. Nobody here has run it

derive-thesis

A 2-hour talk wanders through one core throughline plus dozens of entertaining tangents. Nothing in the pipeline named the throughline, so pick-segments maximized standalone virality per span and shipped clip-shaped asides (a beetle anecdote, a DoorDash story) that misrepresent the source. derive-thesis computes that missing source-level subject once, as a durable JSON artifact both pick-segments (theme prior + theme_fit score + backfill gate) and selection-report read.

Invoke

.claude/skills/derive-thesis/derive-thesis.sh <transcript.json> [topics.json] [out.json]

transcript: full-source transcript (for duration; the digest is the topic list)
topics: segment-topics output (auto-discovered next to the transcript)
out: thesis.json (auto-discovered next to the transcript)

Output

thesis.json:

{
  "subject": "Donald Hoffman on consciousness and reality",
  "thesis_sentence": "Perception is a species-specific interface, not a window onto objective reality.",
  "key_threads": ["conscious agents", "fitness beats truth", "spacetime is not fundamental", "the hard problem"]
}

On any Claude/parse failure it writes a deterministic fallback (subject from the longest topic's title, key_threads from the leading topic titles, "fallback": true) so downstream always has a subject.

How

build_prompt.py renders the whole chapter list and asks Claude to name the SPINE (not summarize every chapter) — the guest + core theme, the throughline sentence, and the on-spine sub-themes. parse_reply.py validates and clamps, falling back to a topic-derived subject when the reply is empty/unparseable. Idempotent via mtime (topics.json → thesis.json); DERIVE_THESIS=0 skips.