2d-games
無料2D game development principles. Sprites, tilemaps, physics, camera.
日本語の概要は準備中です。原文の説明を表示しています。
Deep root cause analysis using the 5 Whys and Fishbone techniques. Use when systematic debugging hasn't found the cause, or for complex systemic issues. Keywords: root cause, why, underlying, fundamental, systemic, deep, origin
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Philosophy: Don't stop at the first "why" — dig until you hit bedrock.
Use this skill when:
Do NOT use this skill when:
Before starting:
Goal: Clearly define what we're analyzing.
Steps:
State the Problem Precisely
Template:
"When [condition], the system [actual behavior]
instead of [expected behavior]."
Example:
"When a user submits a login form with special characters,
the system returns a 500 error instead of validating input."
Gather Impact Data
Document Timeline
Output: Clear problem statement with context.
Goal: Drill down to fundamental causes.
Method:
Start: Problem Statement
│
├─ Why? → First-level cause
│ │
│ ├─ Why? → Second-level cause
│ │ │
│ │ ├─ Why? → Third-level cause
│ │ │ │
│ │ │ ├─ Why? → Fourth-level cause
│ │ │ │ │
│ │ │ │ └─ Why? → ROOT CAUSE
Rules:
Example:
Problem: Login fails with special characters
Why #1: Server returns 500 error
→ Because: Unhandled exception in auth.service.ts
Why #2: Why is there an unhandled exception?
→ Because: SQL query fails with syntax error
Why #3: Why does SQL have syntax error?
→ Because: User input is concatenated directly into query
Why #4: Why is input concatenated directly?
→ Because: Developer didn't use parameterized queries
Why #5: Why didn't developer use parameterized queries?
→ Because: No code review caught it, and no security guidelines exist
ROOT CAUSE: Missing security coding standards and review process
Goal: Explore contributing factors systematically.
Categories to Examine:
┌──────────┐
┌──────────────────►│ │
│ Environment │ │
│ │ │
┌─────────┴───┐ │ PROBLEM │
│ Methods ├──────────────►│ │
└─────────────┘ │ │
│ │
┌─────────────┐ │ │
│ Machines ├──────────────►│ │
│ (Systems) │ │ │
└─────────────┘ └─────┬────┘
│
┌───────────────────────────┘
│
┌────┴────┐ ┌──────────┐ ┌──────────┐
│ People │ │Materials │ │Measurement│
│(Process)│ │ (Data) │ │ (Metrics)│
└─────────┘ └──────────┘ └──────────┘
For Each Category, Ask:
| Category | Questions to Ask |
|---|---|
| Methods | Is the process correct? Is it followed? Is it documented? |
| Machines | Is the system configured correctly? Dependencies up to date? |
| Environment | Dev vs Prod differences? External factors? |
| People/Process | Training adequate? Communication clear? Handoffs smooth? |
| Materials/Data | Data quality? Input validation? Edge cases? |
| Measurement | Are we monitoring correctly? Are we measuring the right things? |
Goal: Weight and prioritize contributing factors.
Steps:
List All Contributing Factors Combine findings from 5 Whys and Fishbone
Score Each Factor
scoring:
frequency: 1-5 (how often does this contribute?)
detectability: 1-5 (how hard to detect? 5=very hidden)
severity: 1-5 (how much impact when it contributes?)
risk_score: frequency × detectability × severity
Create Priority Matrix
High Frequency + High Severity → Address immediately
High Frequency + Low Severity → Address soon
Low Frequency + High Severity → Create safeguards
Low Frequency + Low Severity → Monitor only
Goal: Confirm the root cause is correct.
Validation Questions:
Causation Test
Completeness Test
Actionability Test
Proportionality Test
Goal: Prevent recurrence.
Recommendation Types:
Immediate Fix
Root Cause Fix
Systemic Improvement
Detection Improvement
Example Recommendations:
for_the_sql_injection_example:
immediate:
- Fix the specific query to use parameters
- Add input sanitization
root_cause:
- Establish secure coding guidelines
- Require security review for auth code
systemic:
- Enable SQL injection detection in SAST tooling
- Add security-focused code review checklist
- Security training for developers
detection:
- Add SQL injection tests to CI pipeline
- Monitor for unusual database queries
# Root Cause Analysis Report
## Problem Statement
[Clear statement of the problem]
## Timeline
- First observed: [date]
- Recent changes: [list]
- Frequency: [how often]
## 5 Whys Analysis
1. Why? → [answer]
2. Why? → [answer]
3. Why? → [answer]
4. Why? → [answer]
5. Why? → [answer]
## Contributing Factors
| Factor | Category | Risk Score | Priority |
|--------|----------|------------|----------|
| [factor] | [cat] | [score] | [priority] |
## Root Cause
[Clear statement of root cause]
## Validation
- [ ] Causation confirmed
- [ ] Complete (no other causes)
- [ ] Actionable
- [ ] Proportional
## Recommendations
### Immediate
- [action]
### Root Cause Fix
- [action]
### Systemic
- [action]
### Detection
- [action]
Before claiming analysis complete:
skills/kilo-kit/debugging/systematic/ - For initial bug identificationskills/kilo-kit/debugging/verification/ - For validating fixesskills/kilo-kit/quality/code-review/ - For review improvementsRoot Cause Analysis Skill v1.0.0 — Dig until you hit bedrock
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概要と使いどころ
2D game development principles. Sprites, tilemaps, physics, camera.
日本語の概要は準備中です。原文の説明を表示しています。
3D game development principles. Rendering, shaders, physics, cameras.
日本語の概要は準備中です。原文の説明を表示しています。
Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
日本語の概要は準備中です。原文の説明を表示しています。
Use when implementing or managing persistent, hierarchical memory systems for AI agents. Covers cross-session state, fact supersession, and self-managed memory tools to enable long-term recall and adaptive agent behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Use when monitoring, tracing, or debugging agentic workflows in production. Keywords: observability, tracing, OpenTelemetry, Langfuse, latency, token cost, loop detection, telemetry.
日本語の概要は準備中です。原文の説明を表示しています。
Use when building self-correcting retrieval systems for AI agents. Keywords: RAG, retrieval, Corrective RAG, Self-RAG, query decomposition, reranking, hallucination, grounding.
日本語の概要は準備中です。原文の説明を表示しています。