본문으로 건너뛰기
cccskills
무료GitHub에 공개

parallel-investigation

Coordinates parallel investigation threads to simultaneously explore multiple hypotheses or root causes across different system areas. Use when debugging production incidents, slow API performance, multi-system integration failures, or complex bugs where the root cause is unclear and multiple plausible theories exist; when serial troubleshooting is too slow; or when multiple investigators can divide root-cause analysis work. Provides structured phases for problem decomposition, thread assignment, sync points with Continue/Pivot/Converge decisions, and final report synthesis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md5.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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:

## 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:

## 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

## 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 StatusAction
All exploringContinue parallel
One hot leadValidate lead, others support
Multiple leadsPrioritize by evidence strength
All dead endsReframe problem, new threads
Confirmed causeConverge, 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

# 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 person

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Guides 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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

rohitg00/skillkit1.6천2026년 6월 2일 업데이트

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'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

rohitg00/skillkit1.6천2026년 6월 2일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

rohitg00/skillkit1.6천2026년 6월 2일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

rohitg00/skillkit1.6천2026년 6월 2일 업데이트

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'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

rohitg00/skillkit1.6천2026년 6월 2일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

rohitg00/skillkit1.6천2026년 6월 2일 업데이트

rohitg00의 스킬 모두 보기

이 스킬의 문제 신고