Agent skill

Cause And Effect

by NeoLabHQ in NeoLabHQ/context-engineering-kit

Systematic Fishbone analysis exploring problem causes across six categories

GPL-3.0Auto-check passed

Install Cause And Effect

skills CLI
$ npx skills add NeoLabHQ/context-engineering-kit --skill cause-and-effect -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NeoLabHQ/context-engineering-kit cause-and-effect --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NeoLabHQ/context-engineering-kit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cause-and-effect .claude/skills/cause-and-effect && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
cause-and-effect
GitHub stars
1.8k
Token cost
~1.6k tokens
SKILL.md length
228 words
Files
1
Skills in repo
57
Repo updated
First seen
Licence
GPL-3.0

At a glance

Systematic Fishbone analysis exploring problem causes across six categories

  • Works in 6 steps: State the problem clearly (the "head" of… → For each category, brainstorm potential… → For each potential cause, ask "why" to… → …
  • SKILL.md covers Description, Usage, Variables and Steps, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Cause And Effect is an agent skill from NeoLabHQ/context-engineering-kit. Systematic Fishbone analysis exploring problem causes across six categories

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Hand-crafted Claude Code Skills focused on improving agent results quality. Compatible with OpenCode, Cursor, Antigravity, Gemini CLI, and others. Includes CodeRabbit open-source… The licence is GPL-3.0.

Example prompts

  • “/cause-and-effect”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. State the problem clearly (the "head" of the fish)
  2. For each category, brainstorm potential causes
  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

What it can do on your machine

Read from SKILL.md and the folder at commit 23e2428. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cause And Effect loads about 1.6k tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 228 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~23
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NeoLabHQ/context-engineering-kit at commit 23e2428, republished under its GPL-3.0 licence (© NeoLabHQ). 228 words, ~1,621 tokens.

Download SKILL.mdSave it as .claude/skills/cause-and-effect/SKILL.md (or your agent's skills folder).
name
cause-and-effect
description
Systematic Fishbone analysis exploring problem causes across six categories

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

© NeoLabHQ, GPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/cause-and-effect of NeoLabHQ/context-engineering-kit.

Open the folder on GitHubat commit 23e2428

Compare with similar skills

Cause And Effect next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Cause And Effect compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Cause And Effect this skillNeoLabHQ/context-engineering-kit1.8k—~1.6kAutomated safety check: PassGPL-3.0
Explorersupabase/supabase111k—~845Automated safety check: PassApache-2.0
Add Effectremotion-dev/remotion63k—~2.8kAutomated safety check: PassCustom licence
Is This A Problemanthropics/claude-for-legal9.6k2 repos~2.3kAutomated safety check: PassApache-2.0
Systematic ExplorationA-EVO-Lab/a-evolve809—~383Automated safety check: PassNone
Effect V4ComposioHQ/composio30k—~1kAutomated safety check: PassMIT

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Questions about Cause And Effect

What does Cause And Effect do?

Systematic Fishbone analysis exploring problem causes across six categories. Cause And Effect is an agent skill from NeoLabHQ/context-engineering-kit.

How do I install Cause And Effect in Claude Code?

Run `npx skills add NeoLabHQ/context-engineering-kit --skill cause-and-effect -a claude-code`. Or copy the skill folder (skills/cause-and-effect in NeoLabHQ/context-engineering-kit) into .claude/skills/cause-and-effect in your project. Claude Code loads it when a task matches its description.

How do I install Cause And Effect in Codex?

Run `npx skills add NeoLabHQ/context-engineering-kit --skill cause-and-effect -a codex`. Or copy the skill folder (skills/cause-and-effect in NeoLabHQ/context-engineering-kit) into .agents/skills/cause-and-effect in your project. Codex loads it when a task matches its description.

Can I use Cause And Effect in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NeoLabHQ/context-engineering-kit --skill cause-and-effect -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cause-and-effect, .gemini/skills/cause-and-effect, .github/skills/cause-and-effect and .opencode/skills/cause-and-effect in your project.

What does Cause And Effect need to run?

SKILL.md names no scripts, command-line tools or credentials: Cause And Effect is instructions for the agent only.

Does Cause And Effect access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Cause And Effect safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Cause And Effect use?

Cause And Effect is published under the GPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cause And Effect use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Cause And Effect?

Skills that share tags, products or a category with Cause And Effect: Explorer (supabase/supabase, 111k stars), Add Effect (remotion-dev/remotion, 63k stars), Is This A Problem (anthropics/claude-for-legal, 9.6k stars) and Systematic Exploration (A-EVO-Lab/a-evolve, 809 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cause And Effect?

NeoLabHQ (a GitHub organization) maintains it in NeoLabHQ/context-engineering-kit, which has 1,750 GitHub stars. The repository holds 57 skills in this directory. The repository was last updated on August 26, 2026.

Source: NeoLabHQ/context-engineering-kit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.