AgentSkillsCN

research:orchestration

多代理研究编排,用于收集技术、架构、UX、安全以及竞争方面的洞见。可在头脑风暴前、规划期间,或故障排查时使用。

SKILL.md
--- frontmatter
name: research:orchestration
description: Multi-agent research orchestration for gathering technical, architectural, UX, security, and competitive insights. Use before brainstorm, during planning, or for troubleshooting.
triggers:
  - /research
  - research orchestration
  - gather research
  - before brainstorm research

Research Orchestration

Execute parallel research agents to gather comprehensive insights on a topic, then synthesize into actionable reports.

Shared Orchestration Patterns

This skill builds on shared orchestration patterns. See:

  • includes/orchestration/parallel-execution.md - Agent dispatch and concurrency
  • includes/orchestration/result-synthesis.md - Collecting and merging results
  • includes/orchestration/context-management.md - Context folding and memory
  • includes/orchestration/checkpoint-patterns.md - Save/resume workflows

The patterns below are research-specific extensions.

Research-Specific Workflow

Phase 1: Topic Analysis

  1. Parse user query for research scope
  2. Determine which agents to dispatch:
    • Always: technical, architectural
    • If UI/UX involved: ux
    • If security implications: security
    • If strategic/market question: competitive

Phase 2: Parallel Research

Dispatch selected agents with:

  • Topic context
  • Current year requirement (2026)
  • Confidence scoring requirement
  • Citation requirement

Phase 3: Synthesis

After all agents complete:

  1. Launch research:synthesis agent
  2. Cross-validate findings
  3. Generate consolidated report
  4. Write to docs/.claude/research/{topic}/

Agent Selection Matrix

Query ContainsAgents to Dispatch
"how to implement"technical, architectural
"best practice"technical, security
"UI", "UX", "user"technical, ux
"secure", "auth"technical, security
"compare", "vs"technical, competitive
"market", "trend"competitive
Defaulttechnical, architectural

Output Structure

code
docs/.claude/research/{topic-slug}/
├── README.md          # Executive summary
├── technical.md       # Technical findings
├── architectural.md   # Architecture analysis
├── ux.md             # UX considerations (if applicable)
├── security.md       # Security analysis (if applicable)
├── competitive.md    # Market context (if applicable)
└── sources.md        # All citations

Critical Requirements

Year Validation

ALL web searches MUST include current year:

code
WebSearch(query: "{topic} best practices 2026")

Confidence Scoring

Every finding must include:

  • HIGH: Multiple 2025-2026 sources agree
  • MEDIUM: Some sources, needs verification
  • LOW: Single source or older

Citations

Every finding must cite:

  • Source URL
  • Access date
  • Relevance to query

Integration Points

Before Brainstorm

code
/brainstorm "add feature X"
  -> Automatically triggers /research "feature X" first
  -> Brainstorm receives research context

During Troubleshooting

code
Error encountered
  -> /research:debug "error message"
  -> Technical + architectural agents analyze

Command Variants

CommandAgentsUse Case
/research "topic"Auto-selectedGeneral research
/research:quick "topic"technical onlyFast answer
/research --agents tech,security "topic"SpecifiedTargeted
/research:debug "error"tech, archTroubleshooting

Consensus Review (Required)

After synthesis completes, run consensus review:

Review Agents

  • documentation-reviewer - Technical accuracy, completeness
  • code-quality-reviewer - Code example correctness (if applicable)

Pass Criteria

RequirementThreshold
Consensus Score>= 70%
Critical Findings0
High Findings0

Re-Synthesis Loop

If consensus < 70% OR critical/high findings exist:

  1. Collect reviewer feedback
  2. Re-run synthesis agent with feedback
  3. Re-run consensus review
  4. Maximum 3 iterations
  5. Report partial success if max reached

See includes/orchestration/consensus-review.md for detailed patterns.

Anti-Patterns

  • Searching without current year -> outdated results
  • Skipping synthesis -> disconnected findings
  • Not checking codebase -> recommendations don't fit
  • Single source confidence HIGH -> inaccurate scoring
  • Skipping consensus review -> quality not validated