# Librarian Curate > Weekly cross-agent curation routine — surfaces patterns across agents, promotes stable ones into shared knowledge, writes a weekly digest into Inbox. Routine-only. - Skill: `jezweb/librarian-curate` (Agent Skill) - Install (CLI): `npx skillmds@latest add jezweb/librarian-curate` - Raw SKILL.md: https://api.skillmd.com/api/skills/jezweb/librarian-curate/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: jezweb (https://skillmd.com/u/jezweb) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/jezweb/librarian-curate --- # Librarian curate Weekly cross-pollination. Read across every agent's learnings, surface patterns that recur in multiple places, promote stable ones into the user's shared knowledge store, and digest the week into one Inbox post. > Goanna's coaching/curation review is the librarian's standing duty. Here, the same skill runs in any AssistantAgent acting as librarian-for-the-week. Don't add a new agent class — wear the role for one fire. ## When to run - Once per week. Friday afternoon or Sunday evening — quiet times where reflection won't compete with active work. - After several reflect cycles have accumulated learnings (running this on day 1 with no learnings is a no-op). - Skip if no learnings have been added since the last librarian fire. ## Steps ### 1. Survey learnings across all the user's agents Call `entity_list` with `type: 'learning'` and `limit: 100`. The list is user-scoped, so you see every agent's graduated patterns in one query. Bucket each learning by: - **Agent identity** — `fields.agentName` (or `fields.agentClass` as fallback) - **Created in the last 7 days** vs older - **Body theme** — read the body, group by topic (auth / data / ux / cost / perf / process / etc.) ### 2. Identify cross-cutting patterns A cross-cutting pattern is a learning that appears in 2+ agents' work, OR a single agent's learning that's stable enough to graduate to the user's shared knowledge. Filter to candidates: | Signal | Action | |---|---| | 2+ learnings from different agents on the same topic | Strong candidate — promote to shared knowledge | | 1 learning, recurrenceCount on its source finding ≥ 3 | Stable enough — promote | | 1 learning, recent, single agent, hasn't recurred | Leave it — let next week's review re-evaluate | | Multiple learnings disagree (one says "always X", another "never X") | Surface the conflict in the digest, don't auto-promote | ### 3. Promote stable patterns into shared knowledge For each cross-cutting candidate, call `entity_create` with: ```yaml type: note # piggybacks on the existing notes entity type title: "[Librarian] " status: active fields: body: category: source_learning_ids: [, , ...] promoted_by: librarian promoted_at: ``` Distillation discipline: - **Lead with the rule**, not the discovery story - **Cite source agents** so future readers can trace lineage - **Cap at ~300 words** — knowledge entities are reference, not narrative - **Use the same shape** every week so the user can scan multiple weeks side-by-side ### 4. Write the weekly digest into Inbox Call `inbox_add` with importance medium and a body shaped like: ```markdown ## Librarian — week of **Promoted to shared knowledge** (N): - [Pattern title 1] — (from , ) - [Pattern title 2] — (from ) **New learnings this week** (N): one-line per learning, grouped by agent **Cross-agent themes**: 1-2 sentences on what stood out across the team **Conflicts to resolve**: anything where agents disagreed (skip if none) ``` If nothing graduated this week, the digest can be brief: ```markdown ## Librarian — week of Quiet week. 3 new learnings across 1 agent, none stable enough to graduate yet. Will re-evaluate next week. ``` ### 5. End with a SessionEnd summary Two-three sentences summarising the curation pass. Goes into routine_runs.outputSummary so the next-fire run-tail context can see "what happened last week without re-reading the digest." ## Anti-patterns - **Promoting on the first occurrence** — that's the reflect skill's territory (finding → learning). Librarian operates one level higher: learnings → shared knowledge. - **Polluting the user's notes with low-signal entries** — the `[Librarian]` prefix and tag let users filter; use them. Aim for high-signal sparse output, not week-by-week noise. - **Cross-promoting other users' learnings** — every entity_list query is user-scoped. You can't see other users' work and shouldn't try to. - **Converting reflections into knowledge** — reflection summaries are agent_runs.outputSummary, not learnings. Don't try to mine them; the reflect skill already filed what mattered. ## Output By the time librarian-curate is done: - Every cross-cutting learning that earned promotion has a `[Librarian]`-prefixed shared knowledge entity - The Inbox has one weekly digest entry summarising the cycle - A 2-3 sentence SessionEnd summary captures the pass Target: 10-15 minutes of work, longer if many learnings to weigh. Don't expand the cycle to fill more time. ## Goanna lineage Adapted from goanna's `goanna-coaching-review` skill at `~/Documents/goanna/skills/coaching-review/`. Goanna's version is `librarian/` reading `/findings/` and writing to `~/goanna/den/knowledge/`; this version is the AssistantAgent acting AS librarian for one fire, reading entity_list and writing notes. When this routine has earned its keep over weeks of dogfooding, consider graduating to a dedicated `LibrarianAgent` class with this skill baked in. Until then, role-by-skill is sufficient.