--- name: cause-and-effect-b9ab0a345dc8 description: Systematic Fishbone analysis exploring problem causes across six categories argument-hint: Optional problem description to analyze --- # Cause and Effect Analysis Apply Fishbone (Ishikawa) diagram analysis to systematically explore all potential causes of a problem across multiple categories. ## Description Systematically examine potential causes across six categories: People, Process, Technology, Environment, Methods, and Materials. Creates structured "fishbone" view identifying contributing factors. ## Usage `/cause-and-effect [problem_description]` ## Variables - PROBLEM: Issue to analyze (default: prompt for input) - CATEGORIES: Categories to explore (default: all six) ## Steps 1. State the problem clearly (the "head" of the fish) 2. For each category, brainstorm potential causes: - **People**: Skills, training, communication, team dynamics - **Process**: Workflows, procedures, standards, reviews - **Technology**: Tools, infrastructure, dependencies, configuration - **Environment**: Workspace, deployment targets, external factors - **Methods**: Approaches, patterns, architectures, practices - **Materials**: Data, dependencies, third-party services, resources 3. For each potential cause, ask "why" to dig deeper 4. Identify which causes are contributing vs. root causes 5. Prioritize causes by impact and likelihood 6. Propose solutions for highest-priority causes ## Examples ### Example 1: API Response Latency ``` Problem: API responses take 3+ seconds (target: <500ms) PEOPLE ├─ Team unfamiliar with performance optimization ├─ No one owns performance monitoring └─ Frontend team doesn't understand backend constraints PROCESS ├─ No performance testing in CI/CD ├─ No SLA defined for response times └─ Performance regression not caught in code review TECHNOLOGY ├─ Database queries not optimized │ └─ Why: No query analysis tools in place ├─ N+1 queries in ORM │ └─ Why: Eager loading not configured ├─ No caching layer │ └─ Why: Redis not in tech stack └─ Synchronous external API calls └─ Why: No async architecture in place ENVIRONMENT ├─ Production uses smaller database instance than needed ├─ No CDN for static assets └─ Single region deployment (high latency for distant users) METHODS ├─ REST API design requires multiple round trips ├─ No pagination on large datasets └─ Full object serialization instead of selective fields MATERIALS ├─ Large JSON payloads (unnecessary data) ├─ Uncompressed responses └─ Third-party API (payment gateway) is slow └─ Why: Free tier with rate limiting ROOT CAUSES: - No performance requirements defined (Process) - Missing performance monitoring tooling (Technology) - Architecture doesn't support caching/async (Methods) SOLUTIONS (Priority Order): 1. Add database indexes (quick win, high impact) 2. Implement Redis caching layer (medium effort, high impact) 3. Make external API calls async with webhooks (high effort, high impact) 4. Define and monitor performance SLAs (low effort, prevents regression) ``` ### Example 2: Flaky Test Suite ``` Problem: 15% of test runs fail, passing on retry PEOPLE ├─ Test-writing skills vary across team ├─ New developers copy existing flaky patterns └─ No one assigned to fix flaky tests PROCESS ├─ Flaky tests marked as "known issue" and ignored ├─ No policy against merging with flaky tests └─ Test failures don't block deployments TECHNOLOGY ├─ Race conditions in async test setup ├─ Tests share global state ├─ Test database not isolated per test ├─ setTimeout used instead of proper waiting └─ CI environment inconsistent (different CPU/memory) ENVIRONMENT ├─ CI runner under heavy load ├─ Network timing varies (external API mocks flaky) └─ Timezone differences between local and CI METHODS ├─ Integration tests not properly isolated ├─ No retry logic for legitimate timing issues └─ Tests depend on execution order MATERIALS ├─ Test data fixtures overlap ├─ Shared test database polluted └─ Mock data doesn't match production patterns ROOT CAUSES: - No test isolation strategy (Methods + Technology) - Process accepts flaky tests (Process) - Async timing not handled properly (Technology) SOLUTIONS: 1. Implement per-test database isolation (high impact) 2. Replace setTimeout with proper async/await patterns (medium impact) 3. Add pre-commit hook blocking flaky test patterns (prevents new issues) 4. Enforce policy: flaky test = block merge (process change) ``` ### Example 3: Feature Takes 3 Months Instead of 3 Weeks ``` Problem: Simple CRUD feature took 12 weeks vs. 3 week estimate PEOPLE ├─ Developer unfamiliar with codebase ├─ Key architect on vacation during critical phase └─ Designer changed requirements mid-development PROCESS ├─ Requirements not finalized before starting ├─ No code review for first 6 weeks (large diff) ├─ Multiple rounds of design revision └─ QA started late (found issues in week 10) TECHNOLOGY ├─ Codebase has high coupling (change ripple effects) ├─ No automated tests (manual testing slow) ├─ Legacy code required refactoring first └─ Development environment setup took 2 weeks ENVIRONMENT ├─ Staging environment broken for 3 weeks ├─ Production data needed for testing (compliance delay) └─ Dependencies blocked by another team METHODS ├─ No incremental delivery (big bang approach) ├─ Over-engineering (added future features "while we're at it") └─ No design doc (discovered issues during implementation) MATERIALS ├─ Third-party API changed during development ├─ Production data model different than staging └─ Missing design assets (waited for designer) ROOT CAUSES: - No requirements lock-down before start (Process) - Architecture prevents incremental changes (Technology) - Big bang approach vs. iterative (Methods) - Development environment not automated (Technology) SOLUTIONS: 1. Require design doc + finalized requirements before starting (Process) 2. Implement feature flags for incremental delivery (Methods) 3. Automate dev environment setup (Technology) 4. Refactor high-coupling areas (Technology, long-term) ``` ## Notes - Fishbone reveals systemic issues across domains - Multiple causes often combine to create problems - Don't stop at first cause in each category—dig deeper - Some causes span multiple categories (mark them) - Root causes usually in Process or Methods (not just Technology) - Use with `/why` command for deeper analysis of specific causes - Prioritize solutions by: impact × feasibility ÷ effort - Address root causes, not just symptoms --- Source attribution: NeoLabHQ Original project: https://github.com/NeoLabHQ/context-engineering-kit License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: NeoLabHQ Original project: https://github.com/NeoLabHQ/context-engineering-kit Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Development & Engineering, Operations, Product Management, Project & Program Management Roles: Business Process Analyst, Data Analyst, Product Manager, Project Manager, QA / Test Engineer Specialties: Specialty not specified Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: abd486316efcd5d44e28e07783742d0999266d4e456e2ccd2d37c2d804e2db4c