parallel-investigation
This Claude Code skill coordinates simultaneous investigation of multiple hypotheses across different system areas during complex troubleshooting. Use it when debugging production incidents, performance regressions, or multi-system failures where root causes are unclear and serial investigation is too slow. It structures work through problem decomposition, parallel thread execution with independent scopes, regular sync points for decision-making (Continue, Pivot, or Converge), and final convergence to validate findings and synthesize reports.
git clone --depth 1 https://github.com/rohitg00/skillkit /tmp/parallel-investigation && cp -r /tmp/parallel-investigation/packages/core/src/methodology/packs/collaboration/parallel-investigation ~/.claude/skills/parallel-investigationSKILL.md
# Parallel Investigation
Coordinate parallel investigation threads to explore multiple hypotheses simultaneously. Most effective for production incidents, performance regressions, or integration failures where the root cause is unclear.
## Core Principle
**When uncertain, explore multiple paths in parallel. Converge when evidence points to an answer.**
Parallel investigation reduces time-to-solution by eliminating serial bottlenecks.
## Investigation Structure
### Phase 1: Problem Decomposition
Break the problem into independent investigation threads:
```
Problem: API responses are slow
Investigation Threads:
├── Thread A: Database performance
│ └── Check slow queries, indexes, connection pool
├── Thread B: Application code
│ └── Profile endpoint handlers, check for N+1
├── Thread C: Infrastructure
│ └── Check CPU, memory, network latency
└── Thread D: External services
└── Check third-party API response times
```
Each thread should be independent (no blocking dependencies), focused (clear scope), and time-boxed.
### Phase 2: Thread Assignment
Assign threads with clear ownership:
```markdown
## Thread A: Database Performance
**Investigator:** [Name/Agent A]
**Duration:** 30 minutes
**Scope:**
- Query execution times
- Index utilization
- Connection pool metrics
**Report Format:** Summary + evidence
```
### Phase 3: Parallel Execution
Each thread follows this pattern:
1. Gather evidence specific to thread scope
2. Document findings as you go
3. Identify if thread is a lead or dead end
4. Prepare summary for sync point
**Thread Log Template:**
```markdown
## Thread: [Name]
**Start:** [Time]
### Findings
- [Timestamp] [Finding]
### Evidence
- [Log/Metric/Screenshot]
### Preliminary Conclusion
[What this thread suggests about the problem]
```
### Phase 4: Sync Points
Regular convergence to share findings:
```
Sync Point Agenda:
1. Each thread report (2 min each)
2. Discussion & correlation (5 min)
3. Decision: Continue, Pivot, or Converge (3 min)
```
**Sync Point Decisions:**
- **Continue**: Threads are progressing, maintain parallel execution
- **Pivot**: Redirect threads based on new evidence
- **Converge**: One thread found the answer, others join to validate
### Phase 5: Convergence
When a thread identifies the likely cause:
1. **Validate** — Other threads verify the finding
2. **Deep dive** — Focused investigation on identified cause
3. **Document** — Compile findings from all threads
## Coordination Patterns
**Hub and Spoke**: One coordinator assigns threads, tracks progress, calls sync points, and makes convergence decisions. Best when one person has the most context.
**Peer Network**: Equal investigators post findings to a shared channel and self-organize convergence when a pattern emerges. Best when investigators have similar expertise.
## Communication Protocol
### During Investigation
```
[Thread A] [Status] Starting query analysis
[Thread B] [Finding] No N+1 patterns in user endpoint
[Thread A] [Finding] Slow query: SELECT * FROM orders WHERE...
[Thread C] [Dead End] CPU and memory within normal
[Thread A] [Hot Lead] Missing index on orders.user_id
```
### At Sync Point
```markdown
## Thread A Summary
**Status:** Hot Lead
**Key Finding:** Missing index on orders.user_id
**Evidence:** Query taking 3.2s, explain shows full table scan
**Recommendation:** Likely root cause — suggest converge
```
## Decision Framework
| Thread Status | Action |
|---------------|--------|
| All exploring | Continue parallel |
| One hot lead | Validate lead, others support |
| Multiple leads | Prioritize by evidence strength |
| All dead ends | Reframe problem, new threads |
| Confirmed cause | Converge, begin fix |
## Time Management
A typical two-hour investigation:
```
0:00 Problem decomposition & thread assignment
0:15 Parallel investigation begins
0:45 Sync point #1 → Continue/Pivot/Converge decision
1:30 Sync point #2 (if continuing)
1:35 Final convergence & documentation
```
Adjust sync point cadence based on incident severity — every 20 minutes for critical outages, every 45 minutes for lower-urgency investigations.
## Documentation
### Final Report Structure
```markdown
# Investigation: [Problem]
## Summary
[Brief description and resolution]
## Threads Explored
### Thread A: [Area]
- Investigator: [Name]
- Findings: [Summary]
- Outcome: [Lead / Dead End / Root Cause]
## Root Cause
[Detailed explanation of what was found]
## Evidence
- [Evidence 1]
- [Evidence 2]
## Resolution
[What was done to fix]
## Lessons Learned
- [Learning 1]
```
## Integration with Other Skills
- **debugging/root-cause-analysis**: Each thread follows RCA principles
- **debugging/hypothesis-testing**: Threads test specific hypotheses
- **handoff-protocols**: When passing a thread to another personGuides the creation of technical design documents before writing code, producing architecture diagrams, data models, API interface definitions, implementation plans, and multi-option trade-off analyses. Use when the user asks to plan a feature, architect a system, design an API, explore implementation approaches, or requests a technical design or spec before coding — especially for complex features involving multiple components, ambiguous requirements, or significant architectural changes.
Discovers, searches, and installs skills from multiple AI agent skill marketplaces (400K+ skills) using the SkillKit CLI. Supports browsing official partner collections (Anthropic, Vercel, Supabase, Stripe, and more) and community repositories, searching by domain or technology, and installing specific skills from GitHub. Use when the user wants to find, browse, or install new agent skills, plugins, extensions, or add-ons; asks 'is there a skill for X' or 'find a skill for X'; wants to explore a skill store or marketplace; needs to extend agent capabilities in areas like React, testing, DevOps, security, or APIs; or says 'browse skills', 'search skill marketplace', 'install a skill', or 'what skills are available'.
Manages work transitions between team members or agents by creating structured handoff documents, summarizing project status, documenting key decisions, blockers, and open questions, and generating onboarding briefs. Use when someone needs to hand off, hand over, or transition a project; pass work to another person or agent; brief a colleague taking over; prepare a shift change summary; or onboard someone mid-task. Produces ready-to-use handoff documents covering current status, next steps, known issues, technical context, and communication templates for both planned and unplanned transfers.
Applies the scientific method to debugging by helping users form specific, testable hypotheses, design targeted experiments, and systematically confirm or reject theories to find root causes. Use when a user says their code isn't working, they're getting an error, something broke, they want to troubleshoot a bug, or they're trying to figure out what's causing an issue. Concrete actions include isolating failing components, forming and testing hypotheses, analyzing error messages, tracing execution paths, and interpreting test results to narrow down root causes.
Guides the red-green-refactor TDD workflow: write a failing test first, implement the minimum code to make it pass, then refactor while keeping tests green. Use when a user asks to practice TDD, write tests first, follow red-green-refactor, do test-driven development, write failing tests before code, or phrases like 'make the test pass', 'test coverage', or 'unit tests before implementation'.
Performs systematic root cause analysis to identify the true source of bugs, errors, and unexpected behavior through structured investigation phases — not just treating symptoms. Use when a user reports a bug, crash, error, or broken behavior and needs to debug, troubleshoot, or investigate why something is not working; especially for complex or intermittent issues across multiple components. Applies the Five Whys method, hypothesis-driven testing, stack trace analysis, git blame/log evidence gathering, and causal chain documentation to isolate and confirm root causes before applying any fix.
Creates and structures SKILL.md files for AI coding agents, including YAML frontmatter, trigger phrases, directive instructions, decision trees, code examples, and verification checklists. Use when the user asks to write a new skill, create a skill file, author agent capabilities, generate skill documentation, or define a skill template for Claude Code agents.
Performs a structured five-stage code review covering requirements compliance, correctness, code quality, testing, and security/performance. Each stage uses targeted checklists and categorized feedback (Blocker/Major/Minor/Nit) with actionable suggestions and rationale. Use when the user asks for code review, PR feedback, pull request review, or wants their code checked for bugs, style issues, or vulnerabilities — triggered by phrases like "review my code", "check this PR", "review my changes", "pull request review", or "code feedback".