research-team
Autonomous multi-source research agent group. Given an unknown bug or technology topic, research-team spawns parallel sub-agents to search StackOverflow, GitHub Issues, official docs, and changelogs. Synthesizes findings into a ranked solution report. Inspired by assafelovic/gpt-researcher closed-loop research architecture.
- License 许可
- MIT
- 最近更新
- 2026-11-13
- Stars
- ★ 13
- Forks
- 2
01 前言 Front Matter
- name
- research-team
- origin
- assafelovic/gpt-researcher (Apache 2.0) — multi-source parallel research loop
- license
- MIT
- version
- 1.0.0
- description
- Autonomous multi-source research agent group. Given an unknown bug or technology topic, research-team spawns parallel sub-agents to search StackOverflow, GitHub Issues, official docs, and changelogs. Synthesizes findings into a ranked solution report. Inspired by assafelovic/gpt-researcher closed-loop research architecture.
- compatibility
- Claude Code, any project
03 SKILL.md 正文
research-team
When to Use
- An unknown bug has no match in L1 memory — need external knowledge
- A library has released a new version and APIs may have changed
- You need to compare multiple solutions before committing to one
- Triggered by: "research this", "find a fix for", "investigate", "what's the best approach for", "look up", "research team", "autonomous research"
Do NOT use for
- Known bugs already documented in L1 (
core/memory/L1/) — read L1 first - Simple API lookups that a single WebSearch handles
- Tasks requiring code execution — hand off to
autonomous-patching-loopafter research - See
ingest-repofor structured code-level repo analysis
Team Roles
Researcher-A → searches StackOverflow + GitHub Issues for the exact error
Researcher-B → reads official docs / changelog for the library version in use
Researcher-C → searches for alternative approaches / known workarounds
Synthesizer → cross-references all 3 findings, ranks solutions by reliability
Research Loop Architecture
# Conceptual: multi-agent research pipeline (adapt to Claude Code subagents)
from dataclasses import dataclass
from typing import Callable
@dataclass
class ResearchQuery:
topic: str
context: str # error message / file / line number
library: str # e.g. "react@18.3.0"
max_sources: int = 10
def research_team(query: ResearchQuery, search_fn: Callable) -> dict:
"""
Parallel research across 3 lanes, then synthesize.
"""
# Lane A: Stack-style QA
lane_a = search_fn(f"{query.topic} site:stackoverflow.com OR site:github.com/issues")
# Lane B: Official docs + changelog
lane_b = search_fn(f"{query.library} changelog deprecation {query.topic}")
# Lane C: Alternative approaches
lane_c = search_fn(f"alternative to {query.topic} {query.library} workaround")
# Synthesizer: rank by recency + vote count + library version match
all_results = lane_a + lane_b + lane_c
ranked = sorted(
all_results,
key=lambda r: (r.get("votes", 0) + r.get("recency_score", 0)),
reverse=True
)[:query.max_sources]
return {
"query": query.topic,
"sources": len(ranked),
"top_solution": ranked[0] if ranked else None,
"alternatives": ranked[1:3],
"confidence": "high" if len(ranked) >= 5 else "medium",
}
Claude Code Subagent Pattern
# ORCHESTRATOR prompt to spawn research-team
Task: Bug in [library]@[version]: [error message]
Spawn 3 parallel research agents:
Agent-A: Search StackOverflow and GitHub Issues for exact error.
Query: "[error message] [library]"
Output: top 3 matches with URL + date + vote count → write to .claude/signals/research-a.json
Agent-B: Read official docs/changelog for [library]@[version].
Focus: breaking changes, deprecated APIs, migration guides.
Output: relevant section → write to .claude/signals/research-b.json
Agent-C: Find alternative approaches for [topic] that avoid the error.
Output: top 2 alternatives → write to .claude/signals/research-c.json
Synthesizer (after A+B+C complete):
Read all 3 signal files.
Rank solutions by: recency > vote count > version match.
Write final report to .claude/signals/research-report.md
Promote confirmed fix to L1: bash core/scripts/add-fact.sh "bug-fix" "<solution>" "high"
Output Report Format
# Research Report — [topic]
Date: [timestamp] Sources scanned: [n] Confidence: high|medium|low
## Recommended Fix
[Solution text + code snippet]
Source: [URL] ([date], [votes] votes)
## Why This Works
[1-2 sentence explanation tied to the library's behavior]
## Alternative Approaches
1. [Alt A] — [tradeoff]
2. [Alt B] — [tradeoff]
## Outdated / Rejected Solutions
- [Stack answer from 2020] — library API changed in v18, no longer applicable
## L1 Promotion
[ ] Promoted to core/memory/L1/ — bash core/scripts/add-fact.sh "bug-fix" "..." "high"
Integration with autonomous-patching-loop
research-team → finds solution
│
▼
autonomous-patching-loop → applies fix on isolated branch
│
▼
verify gate passes → merge + L1 promotion
Anti-Fake-Pass Checklist
- [ ] At least 3 sources consulted (not just 1 web search)
- [ ] Results filtered for library version match — old answers flagged as "may be outdated"
- [ ] Confidence level stated: high (≥5 sources), medium (3–4), low (<3)
- [ ] Rejected/outdated solutions documented — not silently dropped
- [ ] Confirmed fix promoted to L1 via
add-fact.sh - [ ] Report written to
.claude/signals/research-report.md— not left in chat only - [ ] Agent does NOT apply the fix directly — hands off to
autonomous-patching-loop
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