name	distill-lessons
description	Crawl every episode transcript (chain → IPFS) and distill recurring themes + standout lessons into LESSONS.md and ALL-LESSONS.md. Use when the user asks to update the lessons files, mine the transcripts, or ask "what have we learned across episodes".

Distill lessons from all episode transcripts

Four stages: fetch → extract per episode → synthesize → compose the flat list. Stages 1 and 2 are cached on disk, so re-running after new episodes only does the new work.

1. Fetch transcripts

node scripts/fetch-transcripts.mjs

Reads the episode list from the SlopComputer contract (mainnet), pulls each manifest + transcript from IPFS into transcripts/ (gitignored). Idempotent — cached by CID. Outputs per episode: raw <slug>.json (JSONL events), <slug>.txt (rendered [h:mm:ss] speaker: text, speech only), <slug>.chat.json, plus transcripts/index.json (titles, one-liners, participants, dates).

2. Extract per episode (fan out, cached)

For each <slug>.txt that has no transcripts/lessons/<slug>.md yet (or whose transcript CID changed in index.json), launch one subagent per episode — in parallel, one message. Each agent prompt:

Read transcripts/<slug>.txt — full transcript of a slop.computer livestream episode: "" (<oneLiner from index.json>). Host: austingriffith.eth (Austin Griffith); clawdbotatg.eth is Clawd, an AI cohost. Transcription is imperfect — read through the noise.
Write a markdown extraction to transcripts/lessons/<slug>.md, ≤600 words, starting with the line ## <slug>:

### Themes — 3-6 main themes, one line each.

### Lessons — every concrete lesson/insight/strong opinion/advice worth keeping (8-15 bullets), one sentence each, attributed (who, ~[h:mm:ss]). Prioritize: working with AI/coding agents, crypto x AI, building & shipping, career/philosophy. Skip pleasantries, logistics, show mechanics.

### Quotes — up to 3 memorable near-verbatim quotes with speaker + timestamp.

After writing the file, reply with one line: the theme phrases, comma-separated.

3. Synthesize LESSONS.md

Read all transcripts/lessons/*.md (they're small) and rewrite LESSONS.md at the repo root (committed — this is the deliverable).

Audience and framing (this is the key requirement): write for someone learning how to be a sloperator — how to build real things with AI agents. Not an analytical theme digest, not a dump: a teaching document. Distill the BIGGEST, most important lessons — roughly 12-18 numbered lessons, ordered as a learning path (start → workflow → craft → security → shipping → career). Each lesson: a punchy bold headline stating the lesson, then a few sentences of distilled guidance synthesized across episodes, tagged (slug) for the episodes it came from. Weight lessons by how many episodes echo them, but a single razor-sharp insight earns its place. Preserve real disagreements between guests as "both are right at different stages" guidance rather than false consensus. End with a short quote wall (best lines, speaker + slug) and a footer: episode count + date + pointer to ALL-LESSONS.md + "regenerate with the distill-lessons skill". Timestamps stay in the per-episode files only.

4. Compose ALL-LESSONS.md + pin to IPFS

python3 scripts/compose-all-lessons.py
bgipfs upload ALL-LESSONS.md --config ~/.bgipfs/credentials.json

The script concatenates every episode's Lessons bullets into ALL-LESSONS.md (committed), one flat list, oldest episode first. The bgipfs upload (CLI + credentials already set up on this machine; see https://www.bgipfs.com/SKILL.md) pins it and prints a CID — the file is then live at https://<CID>.ipfs.community.bgipfs.com/. Report the fresh CID to the user; it changes on every regeneration.

Notes

transcripts/ is gitignored (refetchable public IPFS data); LESSONS.md, this skill, and scripts/fetch-transcripts.mjs are committed.
To force a full re-extract (e.g. after changing the extraction prompt): rm -rf transcripts/lessons and re-run stage 2.
Contract address / gateway live at the top of scripts/fetch-transcripts.mjs (ETH_RPC_URL / IPFS_GATEWAY env overrides).

Distill lessons from all episode transcripts

Four stages: fetch → extract per episode → synthesize → compose the flat list. Stages 1 and 2 are cached on disk, so re-running after new episodes only does the new work.

1. Fetch transcripts

node scripts/fetch-transcripts.mjs

2. Extract per episode (fan out, cached)

Read transcripts/<slug>.txt — full transcript of a slop.computer livestream episode: "" (<oneLiner from index.json>). Host: austingriffith.eth (Austin Griffith); clawdbotatg.eth is Clawd, an AI cohost. Transcription is imperfect — read through the noise.

Write a markdown extraction to transcripts/lessons/<slug>.md, ≤600 words, starting with the line ## <slug>:

### Themes — 3-6 main themes, one line each.
### Lessons — every concrete lesson/insight/strong opinion/advice worth keeping (8-15 bullets), one sentence each, attributed (who, ~[h:mm:ss]). Prioritize: working with AI/coding agents, crypto x AI, building & shipping, career/philosophy. Skip pleasantries, logistics, show mechanics.
### Quotes — up to 3 memorable near-verbatim quotes with speaker + timestamp.

After writing the file, reply with one line: the theme phrases, comma-separated.

3. Synthesize LESSONS.md

Read all transcripts/lessons/*.md (they're small) and rewrite LESSONS.md at the repo root (committed — this is the deliverable).

4. Compose ALL-LESSONS.md + pin to IPFS

python3 scripts/compose-all-lessons.py bgipfs upload ALL-LESSONS.md --config ~/.bgipfs/credentials.json

Notes

transcripts/ is gitignored (refetchable public IPFS data); LESSONS.md, this skill, and scripts/fetch-transcripts.mjs are committed.

To force a full re-extract (e.g. after changing the extraction prompt): rm -rf transcripts/lessons and re-run stage 2.

Contract address / gateway live at the top of scripts/fetch-transcripts.mjs (ETH_RPC_URL / IPFS_GATEWAY env overrides).