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.