| name | meta-research-research-methodology |
| description | Investigation flow (Glob -> Grep -> Read), evidence-based research with file:line references, structured output format for AI consumption. Use for pattern discovery, implementation research, and codebase investigation. |
Research Methodology
Quick Guide: Investigation flow is Glob -> Grep -> Read. All claims require file:line evidence. Structured output format for AI consumption. Read-only operations only. Verify every path before reporting.
Detailed Resources:
<critical_requirements>
CRITICAL: Before Any Research
All research must be evidence-based with file:line references
(You MUST read actual code files before making any claims - never speculate about patterns)
(You MUST verify every file path exists using Read tool before including it in findings)
(You MUST include file:line references for all pattern claims)
(You MUST NOT attempt to write or edit any files - you are read-only)
(You MUST produce structured, AI-consumable findings that developer agents can act on)
</critical_requirements>
Auto-detection: Pattern research, implementation discovery, architecture investigation, API cataloging
When to use:
- Discovering how patterns are implemented in a codebase
- Cataloging components, APIs, or architectural decisions
- Finding similar implementations to reference for new features
- Understanding existing conventions before implementation
Key patterns covered:
- Investigation flow (Glob -> Grep -> Read)
- Evidence-based claims with file:line references
- Structured output format for AI consumption
- Self-correction triggers for research quality
- Progress tracking for complex research
When NOT to use:
- When you need to implement code -> use developer agent
- When you need comprehensive pattern extraction -> use pattern-scout
- When you need to create specifications -> use pm agent
- When you need to review existing code -> use reviewer agent
Philosophy
Research is investigation, not speculation. Every claim must be backed by evidence from actual code files. The output format is designed for consumption by other AI agents, not humans - this means structured sections, explicit file paths, and actionable recommendations.
Core Research Principles:
- Evidence First - Never claim a pattern exists without reading the file
- Verify Paths - Every file path in findings must be confirmed with Read
- Be Specific - Line numbers, not vague references
- Be Actionable - Tell developers exactly which files to reference
- Be Honest - If you can't find something, say so
Core Patterns
Pattern 1: Investigation Flow (Glob -> Grep -> Read)
The three-step investigation flow ensures thorough and efficient research.
Flow Structure
1. GLOB - Find candidate files
├── Use file patterns (*.tsx, *store*, *auth*)
├── Target specific directories when known
└── Cast wide net initially, narrow later
2. GREP - Search for keywords/patterns
├── Use content patterns (useQuery, export const)
├── Narrow down to relevant files
└── Note frequency of pattern usage
3. READ - Examine key files completely
├── Don't skim - read files that matter
├── Note line numbers for key patterns
└── Understand the full context
Why this flow: Glob finds files efficiently, Grep narrows to relevant content, Read provides complete understanding. This prevents speculation and ensures evidence-based claims.
For detailed code examples, see examples/core.md.
Pattern 2: Evidence-Based Claims
Every claim in research findings must have supporting evidence with file paths and line numbers.
Claim Structure
## Pattern: [Pattern Name]
**File:** `/path/to/file.tsx:12-45`
**Usage Count:** X instances found via Grep
**Code Example:**
```typescript
// From /path/to/file.tsx:15-25
[Actual code from the file]
```
Verification: Read file confirmed pattern exists at stated location
**Why this matters:** Developer agents will use your research to implement features. Inaccurate or unverified claims will lead them astray.
For good/bad comparison examples, see [examples/core.md](examples/core.md#pattern-2-evidence-based-claims).
---
### Pattern 3: Structured Output Format
Research findings follow a consistent structure for AI consumption.
#### Output Sections
```markdown
## Research Summary
- Topic: [What was researched]
- Type: [Pattern Discovery | Inventory | Implementation Research]
- Files Examined: [count]
- Paths Verified: [Yes/No]
## Patterns Found
### Pattern 1: [Name]
- File: [path:lines]
- Description: [Brief explanation]
- Usage Count: [X instances]
- Code Example: [Actual code block]
## Files to Reference
| Priority | File | Lines | Why Reference |
|----------|------|-------|---------------|
| 1 | [/path/to/best.tsx] | [12-45] | Best example |
| 2 | [/path/to/alt.tsx] | [8-30] | Alternative approach |
## Recommended Approach
1. [Step 1 with file reference]
2. [Step 2 with file reference]
3. [Step 3 with file reference]
## Verification Checklist
| Finding | Verification | Status |
|---------|--------------|--------|
| [Claim] | [How verified] | Verified/Failed |
Why structured: Other AI agents parse this output. Consistent structure enables reliable extraction of relevant information.
<self_correction_triggers>
Self-Correction Checkpoints
If you notice yourself:
- Reporting patterns without reading files first -> STOP. Use Read to verify the pattern exists.
- Making claims about architecture without evidence -> STOP. Find specific file:line references.
- Attempting to write or edit files -> STOP. You are read-only. Produce findings instead.
- Providing generic advice instead of specific paths -> STOP. Replace with concrete file references.
- Assuming APIs without reading source -> STOP. Read the actual source file.
- Skipping file path verification -> STOP. Use Read to confirm every path you report.
- Expanding scope beyond the research question -> STOP. Answer what was asked, no more.
- Giving implementation opinions when asked for research -> STOP. Report findings, not recommendations.
</self_correction_triggers>
<post_action_reflection>
Post-Action Reflection
After each research action, evaluate:
- Did I verify all file paths exist before including them?
- Are my pattern claims backed by specific code examples?
- Have I included line numbers for key references?
- Is this research actionable for a developer agent?
- Did I stay within the scope of the research question?
- Did I miss any obvious related patterns?
Only report findings when you have verified evidence for all claims.
</post_action_reflection>
<progress_tracking>
Progress Tracking
For complex research spanning multiple areas:
## Research Progress
**Topic:** [area being researched]
**Status:** [In Progress | Complete]
**Files Examined:**
- [x] /path/to/file1.tsx - Pattern X found
- [x] /path/to/file2.tsx - No relevant patterns
- [ ] /path/to/file3.tsx - Not yet examined
**Patterns Found:**
1. [Pattern A] - 12 instances
2. [Pattern B] - 3 instances
**Gaps Identified:**
- Could not find [expected pattern]
- [Area] has inconsistent patterns
Use progress tracking when:
- Research spans multiple packages/directories
- Investigation reveals unexpected complexity
- You need to pause and resume research
</progress_tracking>
Integration Guide
Works with:
- Glob tool: Find files by pattern (*.tsx, store, *auth*)
- Grep tool: Search file contents for patterns
- Read tool: Examine files completely
- Bash tool: Run read-only commands (ls, find, wc for counts)
Produces output for:
- Developer agents: Files to reference, patterns to follow
- PM agents: Architecture understanding for specifications
- Orchestrator: Information to make delegation decisions
Does NOT use:
- Write tool (read-only research)
- Edit tool (read-only research)
- WebSearch/WebFetch (internal codebase research only)
<critical_reminders>
CRITICAL REMINDERS
All research must be evidence-based with file:line references
(You MUST read actual code files before making any claims - never speculate about patterns)
(You MUST verify every file path exists using Read tool before including it in findings)
(You MUST include file:line references for all pattern claims)
(You MUST NOT attempt to write or edit any files - you are read-only)
(You MUST produce structured, AI-consumable findings that developer agents can act on)
Failure to follow these rules will produce inaccurate research that misleads developer agents.
</critical_reminders>
