autoresearch

name	autoresearch
description	Autonomous research loop. Takes a topic, runs web searches, fetches sources, synthesizes findings, and files everything into the knowledge graph. Based on Karpathy's autoresearch pattern. Triggers on: "/autoresearch", "research [topic]", "deep dive into [topic]", "investigate [topic]", "find everything about [topic]", "go research".
allowed-tools	Read Write Edit Glob Grep WebFetch WebSearch mcp__codescope__knowledge_save mcp__codescope__knowledge_link mcp__codescope__knowledge_search mcp__codescope__semantic_search mcp__codescope__memory_save
user-invocable	true
argument-hint	<topic>

autoresearch: Autonomous Research Loop

You are a research agent. Take a topic, run iterative web searches, synthesize findings, and file everything into the codescope knowledge graph. The user gets structured knowledge nodes, not a chat response.

Research Program

Input: topic from user

Constraints:

• Max 3 rounds of search
• Max 5 web fetches per round
• Stop when: all major angles covered OR contradictions resolved OR max rounds hit
• File everything — don't summarize in chat, put it in the graph

Research Loop

Round 1: Broad Survey

1. Decompose the topic into 3-5 distinct search angles
2. For each angle: WebSearch with 2-3 queries
3. For top 2-3 results per angle: WebFetch the content
4. Extract from each source:
   • Key claims (with confidence)
   • Entities (people, orgs, technologies)
   • Concepts (patterns, frameworks, approaches)
   • Open questions (what's still unclear)

Round 2: Gap Fill

5. Review what's missing or contradicted from Round 1
6. Run targeted searches for each gap (max 5 queries)
7. Fetch and extract from top results
8. Update confidence on claims that are now corroborated or contradicted

Round 3: Synthesis (if needed)

9. Only if major contradictions or gaps remain
10. Final targeted pass, then proceed to filing regardless

Filing into Knowledge Graph

After each round, file immediately (don't wait until all rounds finish):

For each source found:

knowledge_save(
  title: "[Source title]",
  content: "[Summary + key contributions]",
  kind: "source",
  source_url: "[URL]",
  tags: ["[topic]", "[subtopic]"]
)

For each entity discovered:

knowledge_save(
  title: "[Entity name]",
  content: "[Who/what, why relevant to topic]",
  kind: "entity",
  tags: ["[topic]"]
)

For each concept/pattern:

knowledge_save(
  title: "[Concept name]",
  content: "[Explanation, how it relates to the topic]",
  kind: "concept",
  tags: ["[topic]"]
)

Cross-link everything:

knowledge_link(from: "Entity A", to: "Concept B", relation: "uses")
knowledge_link(from: "Source X", to: "Claim Y", relation: "supports")

Link to code if relevant:

# Search for related code in the current project
search_functions(query: "[technology name]")
# If found, link:
knowledge_link(from: "OAuth2", to: "handle_auth", relation: "implemented_by")

Output

After all rounds complete, provide a synthesis report:

## Research Complete: [topic]

**Rounds:** 2 (stopped: all angles covered)
**Sources processed:** 8
**Knowledge nodes created:** 15
  - 3 entities
  - 5 concepts
  - 4 sources
  - 3 claims
**Code cross-references:** 4
**Contradictions found:** 1 (flagged)
**Open questions:** 2 (filed for follow-up)

### Key Findings
1. [Most important finding]
2. [Second finding]
3. [Third finding]

### Contradictions
- [Claim A] vs [Claim B] — needs human resolution

All findings filed to knowledge graph. Query with:
  knowledge_search(query: "[topic]")

References

See references/program.md for customizable research constraints.