
Turns meeting transcripts into a queryable entity graph so you can ask questions text search can't answer: who's been mentioned alongside Sarah, first time pricing came up, co-occurrence between hiring and specific people. It builds a JSON index from meeting frontmatter (people, topics, dates) on first run, then queries are instant. The default path is fast and deterministic. If you need companies or products that aren't in frontmatter, there's an opt-in deep extraction mode that scans transcripts with an LLM pass. Useful for relationship intelligence, due diligence, or any cross-meeting question where you need structured data instead of grep. It layers on top of the existing minutes CLI for queries the CLI can't handle.
$ npx -y skills add silverstein/minutes --skill minutes-graph --agent claude-codeInstalls into .claude/skills of the current project.
Minutes builds relationship rankings, exact person profiles, and commitments from one supervised, process-private SQLite projection of stable policy-authorized Markdown plus confirmed identity corrections. One ordered snapshot authority spans corpus and corrections, and the worker is hard-limited for memory, output, and wall time. Topic research uses the separately bounded live-source search boundary. Both paths re-attest policy before returning facts. Do not fall back to a retired durable index or read meeting files directly.
graph_build.py or read ~/.minutes/graph/index.json; those are retired legacy surfaces.--include-restricted. Restricted meetings are intentionally absent from this agent-facing skill.minutes people --json — bounded relationship rankings and losing-touch signals.minutes commitments --json — bounded graph commitments.minutes people merge <canonical> <alias...> — confirm an identity correction in the local vocabulary; uncertain names are never merged automatically.minutes person "<name>" — bounded person profile.minutes research "<topic>" — bounded topic research.minutes people merge; a wrong merge is worse than no merge.Return only the bounded native result. Never invent rankings, commitments, or relationship signals from raw files.
minutes research "<topic>" for bounded company, product, or topic research. If it fails, report the source unavailable; never fall back to raw corpus reads.Sponsors