--- name: Learn slug: learn-2 category: Automation description: Learn researches a topic and grows your knowledge graph. It uses Exa deep researcher, web search, or basic search to investigate topics, files results with full provenance, and chains to the processing pipeline. github: "https://github.com/agenticnotetaking/arscontexta/tree/main/skill-sources/learn" language: Shell stars: 3473 forks: 219 install: "npx degit https://github.com/agenticnotetaking/arscontexta/tree/main/skill-sources/learn ~/.claude/skills/learn" installs_to: ~/.claude/skills/learn source_path: skill-sources/learn/SKILL.md collection_size: 25 category_size: 1523 collection_url: "https://dirskills.com/collections/agenticnotetaking/arscontexta" added: 2026-08-16T07:02:28.498Z last_synced: 2026-08-16T07:02:28.498Z canonical_url: "https://dirskills.com/skills/learn-2" --- # Learn Learn researches a topic and grows your knowledge graph. It uses Exa deep researcher, web search, or basic search to investigate topics, files results with full provenance, and chains to the processing pipeline. **Install:** ```bash npx degit https://github.com/agenticnotetaking/arscontexta/tree/main/skill-sources/learn ~/.claude/skills/learn ``` ## README ## EXECUTE NOW **Topic: $ARGUMENTS** Parse immediately: - If topic provided: research that topic - If topic empty: read `self/goals.md` for highest-priority unexplored direction and propose it - If topic includes `--deep`/`--light`/`--moderate`: force that depth, strip flag from topic - If no topic and no goals.md: ask "What would you like to research?" **Steps:** 1. **Read config** — tool preferences, depth, domain vocabulary 2. **Determine depth** — from flags, config default, or fallback to moderate 3. **Research** — tool cascade: primary → fallback → last resort 4. **File to inbox** — with full provenance metadata 5. **Chain to processing** — next step based on pipeline chaining mode 6. **Update goals.md** — append new research directions discovered **START NOW.** Reference below explains methodology. --- ## Step 1: Read Configuration ``` ops/config.yaml — research tools, depth, pipeline chaining ops/derivation-manifest.md — domain vocabulary (inbox folder, reduce skill name) ``` **From config.yaml** (defaults if missing): ```yaml research: primary: exa-deep-research # exa-deep-research | exa-web-search | web-search fallback: exa-web-search last_resort: web-search default_depth: moderate # light | moderate | deep pipeline: chaining: suggested # manual | suggested | automatic ``` **From derivation-manifest.md** (universal defaults if missing): - Inbox folder: `inbox/` (could be `journal/`, `encounters/`, etc.) - Reduce skill name: `/reduce` (could be `/surface`, `/break-down`, etc.) - Domain name and hub MOC name --- ## Step 2: Determine Depth Priority: explicit flag > config default > `moderate` | Depth | Tool | Sources | Duration | Use When | |-------|------|---------|----------|----------| | light | WebSearch | 2-3 | ~5s | Checking a specific fact | | moderate | mcp__exa__web_search_exa | 5-8 | ~10-30s | Exploring a subtopic | | deep | mcp__exa__deep_researcher_start | Comprehensive | 15s-3min | Major research direction | --- ## Step 3: Research — Tool Cascade Output header: ``` Researching: [topic] Depth: [depth] Using: [tool name] ``` Try tools in config priority order. If a tool fails (MCP unavailable, error, empty results), fall to next tier. If ALL tiers fail: ``` FAIL: Research failed — no research tools available Tried: 1. [primary] — [error] 2. [fallback] — [error] 3. WebSearch — [error] Try again later or manually add research to [inbox-folder]/ ``` ### Tool Invocation Patterns **exa-deep-research:** ``` mcp__exa__deep_researcher_start instructions: "Research comprehensively: [topic]. Focus on practical findings, key patterns, recent developments, and actionable insights." model: "exa-research-fast" (moderate) | "exa-research" (deep) ``` Poll with `mcp__exa__deep_researcher_check` until `completed`. Output during wait: ``` Research ID: [id] Waiting for results... ``` **exa-web-search:** ``` mcp__exa__web_search_exa query: "[topic]" numResults: 8 ``` **web-search (last resort, also used for light depth):** ``` WebSearch query: "[topic]" ``` On completion: `Research complete — [source count] sources analyzed` --- ## Step 4: File Results to Inbox **Filename:** `YYYY-MM-DD-[slugified-topic].md` — lowercase, spaces to hyphens, no special chars. **Write to** the domain inbox folder (from derivation-manifest, default `inbox/`). Create folder if missing. ### Provenance Frontmatter Every field serves the provenance chain. The `exa_prompt` field is most critical — it captures the intellectual context that shaped the research. ```yaml --- description: [1-2 sentence summary of key findings] source_type: exa-deep-research | exa-web-search | web-search exa_prompt: "[full query/instruction string sent to the research tool]" exa_research_id: "[deep researcher ID, omit for web search]" exa_model: "[exa-research-fast | exa-research, omit for web search]" exa_tool: "[mcp tool name, omit for deep researcher]" generated: [ISO 8601 timestamp — run: date -u +"%Y-%m-%dT%H:%M:%SZ"] domain: "[domain name from derivation-manifest]" topics: ["[[domain-hub-moc]]"] --- ``` Include only the fields relevant to the tool used: - Deep researcher: `source_type`, `exa_prompt`, `exa_research_id`, `exa_model`, `generated`, `domain`, `topics` - Exa web search: `source_type`, `exa_prompt`, `exa_tool`, `generated`, `domain`, `topics` - WebSearch: `source_type`, `exa_prompt`, `exa_tool`, `generated`, `domain`, `topics` ### Body Structure Format for downstream reduce extraction — findings as clear propositions, not raw dumps: ```markdown # [Topic Title] ## Key Findings [Synthesized findings organized by theme, not by source. Each finding should be a clear proposition the reduce phase can extract as an atomic insight.] ## Sources [List of sources with titles and URLs] ## Research Directions [New questions, unexplored angles, follow-up topics. These feed goals.md.] ``` --- ## Step 5: Chain to Processing Read chaining mode from config (default: `suggested`). ``` Research complete Filed to: [inbox-folder]/[filename] Next: /[reduce-skill-name] [inbox-folder]/[filename] ``` Append based on mode: - **manual:** (nothing extra) - **suggested:** `Ready for processing when you are.` - **automatic:** Replace "Next" line with `Queued for /[reduce-skill-name] -- processing will begin automatically.` --- ## Step 6: Update goals.md If `self/goals.md` exists AND the research uncovered meaningful new directions: 1. Read goals.md, match existing format 2. Append under the appropriate section: ``` - [New direction] (discovered via /learn: [original topic]) ``` Skip silently if goals.md missing or no meaningful directions found. Do not add filler. --- ## Output Summary Clean output wrapping the full flow: ``` ars contexta Researching: [topic] Depth: [depth] Using: [tool name] [Research ID: abc-123] Research complete -- [N] sources analyzed Filed to: [inbox-folder]/[filename] Next: /[reduce-skill-name] [inbox-folder]/[filename] [chaining context] [goals.md updated with N new research directions] ``` --- ## Error Handling | Error | Behavior | |-------|----------| | No topic, no goals.md | Ask: "What would you like to research?" | | Exa MCP unavailable | Fall through cascade to WebSearch | | All tools fail | Report failures with FAIL status, suggest manual inbox filing | | Deep researcher timeout (>5 min) | Report timeout, suggest `--moderate` | | Empty results | Report "No results found", suggest refining topic | | Config files missing | Use defaults silently | | Inbox folder missing | Create it before writing | --- ## Skill Selection Routing After /learn, the self-building loop continues: | Phase | Skill | Purpose | |-------|-------|---------| | Extract insights | /[reduce-name] | Mine research for atomic propositions | | Find connections | /[reflect-name] | Link new insights to existing graph | | Update old notes | /[reweave-name] | Backward pass on touched notes | | Quality check | /[verify-name] | Description quality, schema, links | /learn is the entry point. Each run feeds the graph, and the graph feeds the next direction through goals.md.