Learn research

Skill gal-Tab/agent_knowledgebase/skills/learn-research

Use when planning or brainstorming a non-trivial task and you want a thorough, one-shot sweep of the agent's past learnings before committing to an approach — best run as a dispatched subagent so the scan happens in its own token budget and returns only a synthesized, cited brief. Read-only. For quick interactive lookups during a conversation, use learn-recall instead.From its SKILL.md

Install

npx -y skills add gal-Tab/agent_knowledgebase --skill learn-research

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.9 KB, as published. Nobody here has run it

Compound Learnings — Researcher (planning subagent)

Sweep the compound-learnings store at planning time and return a short, cited brief of the lessons that bear on the task at hand — past corrections to respect, playbooks to reuse, insights/patterns to factor in. This is the heavier, comprehensive counterpart to learn-recall.

Run this as a dispatched subagent. The point is isolation: the subagent does the grepping and body-reading in its own context budget and hands back only the synthesized findings, so the planner's context stays lean. It is read-only — it never writes, edits, archives, or captures.

When to Use This Skill

You are about to plan or brainstorm a feature, migration, or fix and want to front-load relevant past lessons.
A planning step asks "what have we learned that applies here?" across a broad topic, where a single interactive lookup would be too shallow.

When NOT to Use This Skill

Quick, in-conversation recall → use learn-recall (inline, cheaper).
Domain questions about ingested sources → wiki-query.
Capturing or updating a lesson → /learn-capture. This skill never writes.
No store exists → there is nothing to research; say so and stop.

The Two Stores

Merge both tiers; project shadows global on id collision.

Scope	Root
project	`<repo>/.compound/`
global	`$COMPOUND_KNOWLEDGE_HOME` or `~/.claude/compound-knowledge/`

Each store: a compact index.md plus per-type bodies under insights/ playbooks/ corrections/ patterns/. Index line schema: - [CODE] {id} | {tags} | {headline} | {confidence} | {date}. Type codes → dirs: C→corrections/, P→playbooks/, I→insights/, Pa→patterns/.

Research Procedure

Derive search terms from the task: key nouns, tools, error classes, domains. Lowercase/hyphenate to match the tag column.

Grep both indexes, never load them whole. For each term:

grep -i "<term>" .compound/index.md "$COMPOUND_KNOWLEDGE_HOME/index.md" ~/.claude/compound-knowledge/index.md 2>/dev/null

If neither index exists, report "no learnings captured" and stop.

Rank candidates. Merge tiers (project over global). Order corrections first (mistakes not to repeat), then playbooks, then insights/patterns; break ties by tag-overlap with the task and recency.
Read bodies on demand, capped. Read at most ~7 bodies (<root>/<type-dir>/<id>.md), prioritising the top-ranked. Skip any whose headline already tells you what you need. Log if you hit the cap so the planner knows coverage was bounded.
Return a synthesized, cited brief — findings only. Do not dump file contents. Structure it as:
- Corrections to respect — id (scope): the lesson + how it constrains the plan.
- Playbooks to reuse — id (scope): the procedure + when it applies.
- Insights/patterns — id (scope): the relevant takeaway.
- Net guidance for this task — 1–3 sentences tying the above to the plan.
If nothing relevant matched, say so plainly in one line.

Guardrails

Read-only. Never write, edit, archive, or capture. (Read, Glob, Grep only.)
Index-first, bodies on demand, ≤~7 bodies — never load a store wholesale.
Return synthesis, not raw files — the planner's budget is the whole point.
Cite ids + scope for every lesson, and surface corrections first.
Honour the two tiers; project shadows global on id collision.