Patterns and techniques for evaluating and improving AI agent outputs.
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Use when user requests research requiring multiple sources, comprehensive analysis, or synthesis across topics - technical research, domain knowledge gathering, market analysis, or learning about complex subjects
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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。
Autonomous multi-agent research system. Dispatches parallel sub-agents, stores findings to files, synthesizes into briefs or reports.
Core principle: Planning → Parallel research agents → File-based findings → Synthesis = high quality research with minimal context usage.
digraph when_to_use {
"User requests research?" [shape=diamond];
"Quick factual lookup?" [shape=diamond];
"Use single search tool directly" [shape=box];
"Multiple sources or synthesis needed?" [shape=diamond];
"deep-research" [shape=box];
"User requests research?" -> "Quick factual lookup?" [label="yes"];
"Quick factual lookup?" -> "Use single search tool directly" [label="yes"];
"Quick factual lookup?" -> "Multiple sources or synthesis needed?" [label="no"];
"Multiple sources or synthesis needed?" -> "deep-research" [label="yes"];
}
```text
**Use for:** Technical research, domain knowledge, market analysis, architectural patterns, comparing approaches,
learning complex topics
**Don't use for:** Single fact lookups, specific URL fetches, questions answerable in one search
## The Process
```dot
digraph process {
rankdir=TB;
"Create research directory in scratchpad" -> "Dispatch Query Analyzer agent";
"Dispatch Query Analyzer agent" -> "Analyzer writes research-plan.md";
"Analyzer writes research-plan.md" -> "Read plan, dispatch N Research agents IN PARALLEL";
"Read plan, dispatch N Research agents IN PARALLEL" -> "Each agent writes findings-{thread}.md";
"Each agent writes findings-{thread}.md" -> "Wait for all agents";
"Wait for all agents" -> "Dispatch Synthesizer agent";
"Dispatch Synthesizer agent" -> "Synthesizer reads all findings, writes final-output.md";
"Synthesizer reads all findings, writes final-output.md" -> "Read final output, present summary to user";
}
```text
## Quick Reference
### Phase 1: Planning (Query Analyzer Agent)
Uses `./query-analyzer-prompt.md`. Writes `research-plan.md` containing:
- Query type: technical | domain | hybrid
- Complexity: simple (2-3 agents) | moderate (3-4) | complex (5-6)
- Research threads with source recommendations
- Output format recommendation: brief | report
### Phase 2: Parallel Research
Uses `./research-agent-prompt.md`. Each agent:
1. Invokes `exa-search` skill for source strategy
2. Executes searches (Exa-primary, see Source Selection below)
3. Writes `findings-{thread-name}.md`
**Source Selection:**
| Query Signal | Primary Source |
| ---------------------------- | -------------------------------- |
| Code, APIs, libraries | `mcp__exa__get_code_context_exa` |
| Concepts, analysis, opinions | `mcp__exa__web_search_exa` |
| Video explanations needed | `yt-transcribe` skill |
| Very recent news (< 1 week) | `WebSearch` fallback |
### Phase 3: Synthesis
Uses `./synthesizer-prompt.md`. Reads all findings files, writes `final-output.md`:
- **Actionable Brief** (~300 words): Simple query + clear consensus
- **Structured Report** (~1500 words): Complex query or conflicting findings
## Agent Dispatch Methods
**For complex queries (4+ threads):** Use Task tool with `subagent_type: "general-purpose"` for true sub-agent
isolation. Dispatch all research agents in a single message (parallel Task calls).
**For simpler queries (2-3 threads):** Parallel tool calls within same context is acceptable - make all searches
simultaneously, then write findings files.
Either way: research threads must execute in parallel, not sequentially.
## File Structure
```text
{scratchpad}/deep-research-{timestamp}/
├── research-plan.md
├── findings-*.md
└── final-output.md
```text
## Common Mistakes
| Mistake | Fix |
| ----------------------------------------------------- | --------------------------------------------------- |
| Doing research yourself instead of dispatching agents | Always use the three-phase architecture |
| Keeping findings in context instead of files | Each agent MUST write to files |
| Sequential research agents | Dispatch all research agents in PARALLEL |
| Skipping planning phase | Always run Query Analyzer first |
| Using WebSearch as default | Exa is primary; WebSearch only for very recent news |
## Red Flags - STOP
- "I'll just do a quick search myself" → Use the full process
- "I don't need to write files for this" → Files are mandatory
- "I'll research these topics one at a time" → Parallel dispatch
- "This is simple, I'll skip planning" → Always plan first
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Patterns and techniques for evaluating and improving AI agent outputs.
中文简介正在准备中。当前显示原文说明。
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations.
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AI-powered wiki generation for code repositories with commands, agents, and skills
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Use when building .NET 10 or C# 14 applications; when using minimal APIs, modular monolith patterns, or feature folders; when implementing HTTP resilience, Options pattern, Channels, or validation; when seeing outdated patterns like old extension method syntax
中文简介正在准备中。当前显示原文说明。
Implements accessible .NET UI. SemanticProperties, ARIA, AutomationPeer, testing per platform.
中文简介正在准备中。当前显示原文说明。
Adds analyzer packages to a project. Nullable, trimming, AOT compat analyzers, severity config.
中文简介正在准备中。当前显示原文说明。