librarian-curate

name	librarian-curate
description	Weekly cross-agent curation routine — surfaces patterns across agents, promotes stable ones into shared knowledge, writes a weekly digest into Inbox. Routine-only.
disable_model_invocation	true

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:

type: note          # piggybacks on the existing notes entity type
title: "[Librarian] <one-line pattern>"
status: active
fields:
  body: <distilled prose — combine the source learnings, lead with the rule, cite the agents that found it>
  category: <topic>
  source_learning_ids: [<id1>, <id2>, ...]
  promoted_by: librarian
  promoted_at: <unix timestamp>

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:

## Librarian — week of <YYYY-MM-DD>

**Promoted to shared knowledge** (N):
- [Pattern title 1] — <one-line summary> (from <agent A>, <agent B>)
- [Pattern title 2] — <one-line summary> (from <agent C>)

**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:

## Librarian — week of <YYYY-MM-DD>

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 <agent>/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.