learning

name	learning
description	Compounding knowledge across projects and teams. Captures, searches, and promotes institutional learnings via tiered backends (local/qmd/agent-fs).

Learning

You are managing institutional knowledge — capturing insights, searching prior learnings, and promoting important patterns into CLAUDE.md for permanent reference.

When to Use

This skill activates when:

• User invokes /learning command
• Another skill references desplega:learning
• User says "let's capture this," "what did we learn about X," or similar knowledge-management phrases

Subcommand Routing

Parse the first argument to determine the flow:

Argument	Flow
(no args) or status	Setup / Status
capture [insight]	Capture
recall <topic>	Recall
promote [id-or-topic]	Promote
review	Review

---

Setup / Status (/learning with no args)

1. Check for config: Read ~/.agentic-learnings.json
2. If not found (first run):
   • Explain the learning system briefly: "The learning skill captures, searches, and promotes institutional knowledge across sessions and projects. It stores learnings as markdown files with structured frontmatter."
   • Create ~/.agentic-learnings.json with the default schema (see Config File Schema below), using the local backend
   • Use AskUserQuestion: "Would you like to configure additional backends? (qmd for semantic search, agent-fs for remote team storage)" with options: ["Local only (default)", "Configure qmd", "Configure agent-fs", "Configure both"]
   • If qmd selected:
     • Check if qmd MCP tools are available (try mcp__qmd__status). Note this in config as "accessMode": "mcp" or "cli".
     • Use AskUserQuestion: "Which qmd collections should be searched for learnings? (enter comma-separated names, e.g., 'ai-toolbox, my-notes')"
   • If agent-fs selected, walk through configuration step by step:
     • Use AskUserQuestion: "Do you have an agent-fs API key configured?" with options: ["Yes, it's set in my environment (AGENT_FS_API_KEY)", "Yes, I'll provide it now", "No, I need to set one up first"]
     • If providing now: use AskUserQuestion: "Enter your agent-fs API key"
     • If needs setup: explain how to get one and pause
     • Use AskUserQuestion: "Which agent-fs organization should learnings be stored in?" with options: ["Use existing org (I'll provide the name)", "Create a new org for learnings"]
     • If existing: use AskUserQuestion: "Enter the organization name"
     • If new: use AskUserQuestion: "What should the new organization be called?" — then run agent-fs org create <name> via Bash
     • Use AskUserQuestion: "Which drive should learnings be stored in?" with options: ["Use existing drive (I'll provide the name)", "Create a new 'learnings' drive"]
     • If existing: use AskUserQuestion: "Enter the drive name"
     • If new: run agent-fs drive create learnings --org <org> via Bash
   • CLAUDE.md Bootstrap: After config creation, use AskUserQuestion: "Would you like me to add a Learning System section to your project CLAUDE.md so future sessions know about the learning system?" with options: ["Yes, add to project CLAUDE.md", "Yes, add to global ~/.claude/CLAUDE.md", "No, skip"]
   • If yes, append the following section to the chosen CLAUDE.md:

     ## Learning System
     Use `/learning recall <topic>` before research/planning to check for prior learnings.
     Use `/learning capture` to record significant insights, decisions, and gotchas.
     Config: `~/.agentic-learnings.json`
     

3. If found (returning user):
   • Show configured backends and their status (enabled/disabled)
   • Count learnings per backend (local: Glob count, agent-fs: agent-fs ls count)
   • Show 5 most recent learnings (filename + topic from frontmatter)

---

Capture (/learning capture)

Step 1: Get the Insight

• If invoked with inline text (e.g., /learning capture "qmd requires manual update"): use that as the insight
• If no text provided: use AskUserQuestion: "What insight or learning would you like to capture?"

Step 2: Significance Check

Before proceeding, evaluate using these default heuristics:

1. Would this help someone else in a future session? (primary test)
2. Is this already documented in CLAUDE.md or code comments? (skip if yes)
3. Did the user correct the agent's approach? (always capture)
4. Was something surprisingly difficult or broken? (usually capture)

Override: If a ## Learning Capture Rules section exists in project or global CLAUDE.md, those rules take precedence over the defaults above.

If the insight doesn't pass the significance threshold, mention this to the user but still offer to capture it — the user has final say.

Step 3: Categorize

Use AskUserQuestion: "What category best fits this learning?" with options:

• product-decisions — architectural choices, trade-offs, why we chose X over Y
• technical-gotchas — bugs, footguns, surprising behavior, workarounds
• human-nudges — user corrections, workflow preferences, communication style
• patterns — reusable approaches, conventions, best practices discovered
• mistakes — things that went wrong and what to do differently

Step 4: Scope

Use AskUserQuestion: "Should this learning be personal or shared?" with options:

• Personal — saved to thoughts/{user}/learnings/, visible only in this user's context
• Shared — saved to thoughts/shared/learnings/, git-tracked, visible to all collaborators (human and agent)

Step 5: Write the Learning

1. Generate filename: YYYY-MM-DD-<slug>.md
   • Slug: derive from topic, lowercase, hyphens, max 50 chars
   • When capturing to shared scope, include author name in slug to avoid multi-user conflicts (e.g., 2026-03-19-taras-qmd-indexing.md)
2. Fill the template from cc-plugin/base/skills/learning/template.md with the collected information
3. Write using the Write tool to the appropriate path
4. Sync backends:
   • If qmd backend enabled: run qmd update via Bash
   • If agent-fs backend enabled: run agent-fs write /learnings/<filename> --content "<content>" via Bash
5. Confirm capture with file path

---

Recall (/learning recall)

Step 1: Get the Topic

• If invoked with a topic (e.g., /learning recall qmd indexing): use that
• If no topic: use AskUserQuestion: "What topic would you like to search for?"

Step 2: Search All Configured Backends in Parallel

Query all enabled backends simultaneously:

• Local: Grep for the topic in thoughts/{user}/learnings/ and thoughts/shared/learnings/
• qmd: Use mcp__qmd__query with [{type:'lex', query:'<topic>'}, {type:'vec', query:'<topic>'}], scoped to configured collections
• agent-fs: Run agent-fs search --query "<topic>" or agent-fs fts --query "<topic>" via Bash

Step 3: Present Results

• Rank results by relevance (exact matches first, semantic matches second)
• Show each result with: date, category, topic, and a 1-line summary
• Include file:line references for local results
• Offer to read the full learning or take action (promote, archive)

---

Promote (/learning promote)

Step 1: Select a Learning

• If given a file path: read that learning directly
• If given a topic: run recall first, then let the user select which learning to promote
• If no argument: run recall with no topic (list recent), let user select

Step 2: Choose Target

Use AskUserQuestion: "Where should this learning be promoted to?" with options:

• Project CLAUDE.md — add to the current project's CLAUDE.md
• Global ~/.claude/CLAUDE.md — add to the global CLAUDE.md

Step 3: Format and Append

1. Read the full learning file
2. Format it as a concise rule (1-3 lines) suitable for CLAUDE.md
3. Use AskUserQuestion to confirm the formatted rule before writing
4. Append to the chosen CLAUDE.md under an appropriate section (create a section if needed)
5. Update the learning file's frontmatter: set promoted_to: to the target path (e.g., promoted_to: "CLAUDE.md" or promoted_to: "~/.claude/CLAUDE.md")

---

Review (/learning review)

Step 1: List All Learnings

• List all learnings from the default backend (most recent first)
• Show 1-line summaries: date, category, topic, promoted status
• Use local backend: Glob thoughts/{user}/learnings/*.md + thoughts/shared/learnings/*.md

Step 2: Select for Review

Use AskUserQuestion (multiSelect): "Which learnings would you like to review?" with the list of learnings

Step 3: Process Each Selected Learning

For each selected learning:

1. Show the full content
2. Use AskUserQuestion: "What would you like to do with this learning?" with options:
   • Keep — no changes
   • Promote — run the Promote flow for this learning
   • Archive — move to thoughts/{user}/learnings/archive/ (or shared equivalent)
   • Delete — remove the file entirely

Step 4: Alternative — File Review

If file-review is available (check if the command exists), offer it as an alternative for batch review:

• "Would you like to use file-review for a visual batch review instead?"
• If yes: create a temporary summary file and launch file-review

Step 5: Report

After processing all selected learnings, report: "Kept N, promoted N, archived N, deleted N."

---

Backend Adapter Reference

All backends support 4 operations: write, search, list, delete.

Local Backend

Operation	Implementation
write	Write tool to thoughts/{user}/learnings/ or thoughts/shared/learnings/
search	Grep/Glob for topic across both personal and shared learnings directories
list	Glob thoughts/{user}/learnings/*.md + Glob thoughts/shared/learnings/*.md
delete	Bash rm <path> (learning file is just a local file)

qmd Backend

qmd is a local search engine over markdown documents. It can be accessed via MCP tools (if configured as an MCP server) or via CLI.

• Install: npm install -g @tobilu/qmd (or bun install -g @tobilu/qmd) — github.com/tobi/qmd
• MCP access: Use mcp__qmd__query, mcp__qmd__get, mcp__qmd__multi_get tools (available when qmd MCP server is configured)
• CLI access: Run qmd query, qmd get, qmd update via Bash (always available if installed)

Operation	Implementation
write	Same as local (qmd indexes local files), then qmd update via Bash to re-index
search	MCP: mcp__qmd__query with lex+vec sub-queries, scoped to configured collections. CLI: qmd query --collection <name> "<topic>" via Bash
list	MCP: mcp__qmd__multi_get with glob pattern (e.g., learnings/*.md). CLI: qmd get "learnings/*.md" via Bash
delete	Delete local file + qmd update via Bash (qmd re-indexes, removing the entry)

Prefer MCP tools when available (richer output, no shell escaping). Fall back to CLI if MCP is not configured.

agent-fs Backend

agent-fs provides remote, team-wide file storage with semantic search. Accessed via CLI only.

• Install: bun add -g @desplega.ai/agent-fs — github.com/desplega-ai/agent-fs
• CLI access: All operations use agent-fs <command> via Bash

Operation	Implementation
write	agent-fs write /learnings/<filename> --content "<content>" via Bash
search	agent-fs search --query "<topic>" (semantic) or agent-fs fts --query "<topic>" (keyword) via Bash
list	agent-fs ls /learnings/ via Bash
delete	agent-fs rm /learnings/<filename> via Bash

---

Config File Schema

The config file lives at ~/.agentic-learnings.json:

{
  "defaultBackend": "local",
  "backends": {
    "local": { "enabled": true, "basePath": "thoughts/{user}/learnings/" },
    "qmd": { "enabled": false, "accessMode": "mcp", "collections": [] },
    "agentFs": { "enabled": false, "apiKey": "", "org": "", "drive": "" }
  }

• defaultBackend: which backend to use for writes (always "local" initially)
• backends.local.basePath: path template — {user} is replaced at runtime with the current user's name
• backends.qmd.accessMode: "mcp" (preferred, uses MCP tools) or "cli" (fallback, uses qmd CLI via Bash)
• backends.qmd.collections: list of qmd collection names to search
• backends.agentFs: agent-fs connection details (only needed if using remote team storage)
• backends.agentFs.apiKey: API key — can be set here or via AGENT_FS_API_KEY env var

What This Skill is NOT

• Not a chatbot memory — learnings are deliberate, curated knowledge, not conversation history
• Not automatic — the agent doesn't auto-capture; it nudges via sub-skill references and the user/agent decides
• Not a replacement for CLAUDE.md — learnings are the staging area; important ones get promoted to CLAUDE.md
• Not brain — brain is a separate personal knowledge tool; this skill is for project/team institutional knowledge