# Cause And Effect

Systematic Fishbone analysis exploring problem causes across six categories

- Repository: https://github.com/NeoLabHQ/context-engineering-kit (1797 stars)
- Category: Engineering
- License: GPL-3.0
- Last updated: 2026-08-26
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add NeoLabHQ/context-engineering-kit --skill cause-and-effect -g -a claude-code -y`
Codex: `npx skills add NeoLabHQ/context-engineering-kit --skill cause-and-effect -g -a codex -y`
Cursor: `npx skills add NeoLabHQ/context-engineering-kit --skill cause-and-effect -g -a cursor -y`
Gemini CLI: `npx skills add NeoLabHQ/context-engineering-kit --skill cause-and-effect -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add NeoLabHQ/context-engineering-kit`

## Description

Systematic Fishbone analysis exploring problem causes across six categories

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/NeoLabHQ/context-engineering-kit/blob/HEAD/antigravity/skills/cause-and-effect/SKILL.md