Humor Post-Mortem

The skill can identify jokes from transcripts and slides but CANNOT hear laughter. For every talk processed in this run, compile the humor beats detected in dimension 3 and walk through them with the speaker:

1. List every joke/humor beat identified in the analysis (verbatim quote or slide reference). For each one, ask: "Did this land? Big laugh, knowing nods, or flat?"
2. Meme slides: For meme-only or meme-with-text slides, ask if the audience visibly reacted or if the meme was more of a visual punchline the speaker talked over.
3. Spontaneous humor: Ask if there were jokes NOT on the slides that happened in the moment — audience riffs, improvised callbacks, heckler interactions, recovery humor from demo failures. These are invisible to the skill but often the best material.
4. Humor grading: For each confirmed-landed joke, tag it in the DB with humor_grade: "hit"|"nod"|"flat"|"spontaneous_hit". Over time this builds a corpus-wide humor effectiveness map — which joke TYPES land (self-deprecating, industry snark, meme-as-punchline, callback) and which fall flat.
5. Promote to portfolio: If a spontaneous joke landed well, ask the speaker if it should be promoted to a planned beat in future deliveries (like the therapy analogy from QCon London 2026).

This is particularly important for recent talks where memory is fresh. For older talks (2+ years), compress to: "Any jokes you remember landing particularly well or badly?"

Store results in humor_postmortem on the talk's DB entry and update the rhetoric summary Section 3 (Humor & Wit) with confirmed effectiveness data. Persist the DB value only through the ingress owner's update_talk_clarification mutation: name the exact talk filename, set humor_postmortem to one complete JSON object or array, and expect that field's exact current object/array or {"$missing": true}. Dry-run, review, hash-bind apply, and re-read through the owner contract; never edit the tracking database directly.