professional-brain
Maintain a durable, local markdown memory ('brain') of your product context, decisions, hypotheses, and stakeholders that other skills read from and write back to. Use when asked to set up a brain, ingest notes/artifacts into memory, recall what's known about a topic, log a decision with provenance, or run a weekly brain review. Produces a structured brain/ folder (knowledge, decisions, hypotheses, stakeholders, entities, source) with provenance-tagged facts, plus ingest/recall/record/review operations with approval-gated, append-only write-back.
npx skills add mohitagw15856/pm-claude-skills --skill professional-brain --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Professional Brain Skill > 🚀 **New to this? Start with the [5-minute Quickstart](../../BRAIN_QUICKSTART.md)** — a folder + one file, with a worked example. This file is the full reference. Most skills start cold — you paste the same context every time, and decisions made six weeks ago lose the *why*. This skill gives the library a **memory**: a plain-markdown `brain/` folder on disk that skills read before they answer and write to after. No vector DB, no cloud — just grep-able files you (and Claude) can audit and edit. This is the **state layer** of an AI teammate. Pair it with the action layer (skills that file tickets / open PRs) and you get a loop: *recall → do the work → record the decision → review.* ## What This Skill Produces - A scaffolded **`brain/` folder** with a fixed schema (see below). - **Provenance-tagged** knowledge — every claim says where it came from and how strong it is. - Four operations you can invoke: **init**, **ingest**, **recall**, **review**. - A standing contract other skills follow: *read the relevant brain files first; write durable outcomes (decisions, new facts, stakeholder asks) back.* ## Required Inputs Ask for these only if they aren't already
- What This Skill Produces
- Required Inputs
- The Brain Schema
- Provenance Tags (the trust mechanism)
- Operations
- Programmatic Helper
- The contract for other skills
- Output Format
- Ingested: [artifact]
- Recall: [query]
- Quality Checks
- Anti-Patterns
Find what the brain knows about "activation", newest-first, as text python3 scripts/brain_query.py ./brain "activation" JSON for chaining into another step python3 scripts/brain_query.py ./brain "enterprise SSO" --json Preview what would be written (changes nothing): python3 scripts/brain_write.py ./brain decisions "Prioritise mobile" --tag data --body "68% of churn is mobile" --source "Q3 analytics" Write it after approval: python3 scripts/brain_write.py ./brain decisions "Prioritise mobile" --tag data --body "…" --source "Q3 analytics" --commit
What does the professional-brain skill do?
Maintain a durable, local markdown memory ('brain') of your product context, decisions, hypotheses, and stakeholders that other skills read from and write back to. Use when asked to set up a brain, ingest notes/artifacts into memory, recall what's known about a topic, log a decision with provenance, or run a weekly brain review. Produces a structured brain/ folder (knowledge, decisions, hypotheses, stakeholders, entities, source) with provenance-tagged facts, plus ingest/recall/record/review operations with approval-gated, append-only write-back.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill professional-brain --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.