Add a new knowledge domain to your existing system. Derives domain-specific configuration through conversation, generates domain folders, templates, and vocabulary while preserving and connecting to your existing architecture.
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Research a topic and grow your knowledge graph. Uses Exa deep researcher, web search, or basic search to investigate topics, files results with full provenance, and chains to processing pipeline. Triggers on "/learn", "/learn [topic]", "research this", "find out about".
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Topic: $ARGUMENTS
Parse immediately:
self/goals.md for highest-priority unexplored direction and propose it--deep/--light/--moderate: force that depth, strip flag from topicSteps:
START NOW. Reference below explains methodology.
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):
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/ (could be journal/, encounters/, etc.)/reduce (could be /surface, /break-down, etc.)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 |
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]/
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
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.
Every field serves the provenance chain. The exa_prompt field is most critical — it captures the intellectual context that shaped the research.
---
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:
source_type, exa_prompt, exa_research_id, exa_model, generated, domain, topicssource_type, exa_prompt, exa_tool, generated, domain, topicssource_type, exa_prompt, exa_tool, generated, domain, topicsFormat for downstream reduce extraction — findings as clear propositions, not raw dumps:
# [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.]
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:
Ready for processing when you are.Queued for /[reduce-skill-name] -- processing will begin automatically.If self/goals.md exists AND the research uncovered meaningful new directions:
- [New direction] (discovered via /learn: [original topic])
Skip silently if goals.md missing or no meaningful directions found. Do not add filler.
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 | 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 |
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.
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Add a new knowledge domain to your existing system. Derives domain-specific configuration through conversation, generates domain folders, templates, and vocabulary while preserving and connecting to your existing architecture.
中文简介正在准备中。当前显示原文说明。
Research-backed evolution advice for your knowledge system. Analyzes health reports, friction patterns, and derivation history to propose specific changes with research justification. Never auto-implements — proposals require your approval.
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Query the bundled research knowledge graph for methodology guidance. Routes questions through a 3-tier knowledge base — WHY (research claims), HOW (guidance docs), WHAT IT LOOKS LIKE (domain examples) — plus structured reference documents. Returns research-backed answers grounded in specific claims with practical application to the user's system. Triggers on "/ask", "/ask [question]", "why does my system...", "how should I...".
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Interactive knowledge graph analysis. Routes natural language questions to graph scripts, interprets results in domain vocabulary, and suggests concrete actions. Triggers on "/graph", "/graph health", "/graph triangles", "find synthesis opportunities", "graph analysis".
中文简介正在准备中。当前显示原文说明。
Run condition-based vault health diagnostics. 8 categories — schema compliance, orphan detection, link health, description quality, three-space boundaries, processing throughput, stale notes, MOC coherence. 3 modes — quick (schema+orphans+links), full (all 8), three-space (boundary violations only). Returns actionable FAIL/WARN/PASS report with specific fixes ranked by impact. Triggers on "/health", "check vault health", "maintenance report", "what needs fixing".
中文简介正在准备中。当前显示原文说明。
Contextual guidance and command discovery. Three modes — narrative (first-time), contextual (mid-task), compact (quick reference). Shows available commands, active skills, and intelligent suggestions based on vault state. Triggers on "/help", "what can I do", "show commands", "how does this work".
中文简介正在准备中。当前显示原文说明。