investigating-code-patterns
Systematically trace code flows, locate implementations, diagnose performance issues, and map system architecture. Use when understanding how existing systems work, researching concepts, exploring code structure, or answering "how/where/why is X implemented?" questions.
Install
mkdir -p .claude/skills/investigating-code-patterns && curl -L -o skill.zip "https://mcp.directory/api/skills/download/3906" && unzip -o skill.zip -d .claude/skills/investigating-code-patterns && rm skill.zipInstalls to .claude/skills/investigating-code-patterns
About this skill
Investigating Code Patterns
When to Use This Skill
- Understanding how existing systems work end-to-end
- Researching concepts or technologies in your codebase
- Exploring code structure, patterns, and dependencies
- Answering "how/where/what" questions about implementations
- NOT for building new features (use implementation skills for that)
Core Investigation Types
1. Code Flow: "How does X work?"
Trace execution end-to-end from entry point to outcome.
Steps:
- Find the entry point (API endpoint, component, function)
- Trace function calls and data transformations
- Follow imports and dependencies
- Identify key decision points and error handling
2. Code Location: "Where is X implemented?"
Find where functionality lives in the codebase.
Steps:
- Search for relevant keywords using grep
- Check related files and modules
- Identify main implementation and supporting files
- Verify entry points and usage patterns
3. Performance Analysis: "Why is X slow?"
Diagnose bottlenecks using 3-phase approach.
Phase 1: Locate bottleneck
- Find entry point where slowness occurs
- Trace execution path
- Identify all operations (DB queries, API calls, compute, I/O)
- Look for obvious issues: N+1 queries, nested loops, large payloads
Phase 2: Root cause (if unclear)
- Generate hypotheses ranked by likelihood
- Validate with evidence from code
Phase 3: Fix or instrument
- If fix clear → implement optimization
- If uncertain → add logging/profiling for user testing
Common performance patterns:
| Pattern | Symptoms | Fix Direction |
|---|---|---|
| N+1 queries | Sequential DB calls | Batch/eager loading |
| Algorithmic complexity | Grows with data | Optimize algorithm |
| Large payload | Network time high | Pagination/filtering |
| Missing cache | Same data fetched repeatedly | Add caching |
| Sequential operations | Waits in series | Parallelize |
4. Architecture Mapping: "How is X structured?"
Understand system organization, components, and integration points.
Steps:
- Map component boundaries and responsibilities
- Identify data flow patterns between layers
- Document integration points and dependencies
- Trace external service interactions
Investigation Strategy
Start with Documentation
Read in this order:
docs/product-requirements.md— project overview and features (F-##)docs/feature-spec/F-##-*.md— technical detailsdocs/system-design.md— architecturedocs/api-contracts.yaml— API reference
Choose Your Tools
For known file paths:
Read— examine specific files directly
For pattern searches:
Grep— find exact text matches (function names, imports, error messages)Glob— discover files by name pattern
For semantic queries:
- Launch
Exploreagent for complex, multi-file pattern discovery - Use
senior-engineeragent for subtle performance bottlenecks
Parallel Investigation
Launch 2-4 independent agents for large codebases:
Full-stack flow:
- Agent 1: Frontend (UI, state, API calls)
- Agent 2: Backend (endpoints, services, database)
- Agent 3: Integration (tests, config, external APIs)
Multi-service architecture:
- Agent 1: Auth (authentication, tokens)
- Agent 2: User service (profile, sync)
- Agent 3: Authorization (permissions, RBAC)
Performance issues:
- Agent 1: Frontend perf (renders, bundle, assets)
- Agent 2: API/network (queries, payloads, caching)
- Agent 3: Backend perf (algorithms, database, services)
Documenting Findings
Code Flow Template
## How [Feature] Works
### Purpose
[Brief description and why it exists]
### High-Level Flow
1. User action triggers [component/function] (file:line)
2. [Step 2 with file:line reference]
3. [Step 3 with file:line reference]
4. Final outcome
### Key Files
- `path/to/file.ts:123` - [Purpose]
- `path/to/other.ts:45` - [Purpose]
### Important Details
- Error handling: [Approach with file references]
- Edge cases: [How handled]
- Security: [Considerations if applicable]
Code Location Template
## Location: [Functionality]
### Main Implementation
`path/to/file.ts:45-120` - [Purpose]
### Related Files
- `path/component.tsx` - UI layer
- `path/service.ts` - Business logic
- `path/api.ts` - API integration
### Entry Points
1. [How users trigger this]
2. [System-initiated triggers]
Performance Analysis Template
## Performance Analysis: [Feature]
### Symptoms
- Slow when: [Condition]
- Observed: [X] seconds
- Expected: [Y] seconds
### Root Cause
[What's causing it with file:line evidence]
### Fix Options
**Option A: [Name]**
- Change: [What to do]
- Impact: [Expected improvement]
- Effort: [Time estimate]
**Recommendation:** [Which option and why]
Architecture Mapping Template
## Architecture: [System/Feature]
### Overview
[High-level description]
### Component Breakdown
**[Layer/Module Name]:**
- Responsibility: [What it does]
- Key files: [File paths]
- Dependencies: [What it needs]
### Data Flow
[Step-by-step or diagram]
### Integration Points
- [External services]
- [Related features]
Investigation Workflow
- Clarify the question — What are you trying to understand?
- Read project docs — Start with overview, feature specs, architecture
- Choose investigation type — Flow, location, performance, or architecture
- Search strategically — Use direct tools or delegate to agents
- Document with file references — Use
file:lineformat for all findings - Offer next steps — Deeper investigation, documentation, or implementation
Key Principles
- File references matter — Always cite
file:linefor evidence - Evidence-based — Quote actual code, don't guess
- Multi-domain issues need parallel agents — Frontend + backend require separate investigators
- Start simple — Use direct grep/read before delegating
- Consolidate findings — Synthesize parallel agent results into coherent explanation
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