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Analyse Problem

OrganizationPopular
NeoLabHQ
analyse-problem

Comprehensive A3 one-page problem analysis with root cause and action plan

Overview

PublisherNeoLabHQ
Repositorycontext-engineering-kit
Skill nameanalyse-problem
Stars
1.7K
Forks
159
Bundled files
Instructions only
LicenseGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by NeoLabHQ on GitHub. Read the source before you install it.

Installation

Install the Analyse Problem AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NeoLabHQ/context-engineering-kit.git /tmp/context-engineering-kit
mkdir -p .claude/skills
cp -r /tmp/context-engineering-kit/antigravity/skills/analyse-problem .claude/skills/analyse-problem
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyse Problem in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analyse Problem on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analyse Problem is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

A3 Problem Analysis

Apply A3 problem-solving format for comprehensive, single-page problem documentation and resolution planning.

Description

Structured one-page analysis format covering: Background, Current Condition, Goal, Root Cause Analysis, Countermeasures, Implementation Plan, and Follow-up. Named after A3 paper size; emphasizes concise, complete documentation.

Usage

/analyse-problem [problem_description]

Variables

  • PROBLEM: Issue to analyze (default: prompt for input)
  • OUTPUT_FORMAT: markdown or text (default: markdown)

Steps

  1. Background: Why this problem matters (context, business impact)
  2. Current Condition: What's happening now (data, metrics, examples)
  3. Goal/Target: What success looks like (specific, measurable)
  4. Root Cause Analysis: Why problem exists (use 5 Whys or Fishbone)
  5. Countermeasures: Proposed solutions addressing root causes
  6. Implementation Plan: Who, what, when, how
  7. Follow-up: How to verify success and prevent recurrence

A3 Template

═══════════════════════════════════════════════════════════════
                    A3 PROBLEM ANALYSIS
═══════════════════════════════════════════════════════════════

TITLE: [Concise problem statement]
OWNER: [Person responsible]
DATE: [YYYY-MM-DD]

┌─────────────────────────────────────────────────────────────┐
│ 1. BACKGROUND (Why this matters)                            │
├─────────────────────────────────────────────────────────────┤
│ [Context, impact, urgency, who's affected]                  │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 2. CURRENT CONDITION (What's happening)                     │
├─────────────────────────────────────────────────────────────┤
│ [Facts, data, metrics, examples - no opinions]              │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 3. GOAL/TARGET (What success looks like)                    │
├─────────────────────────────────────────────────────────────┤
│ [Specific, measurable, time-bound targets]                  │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 4. ROOT CAUSE ANALYSIS (Why problem exists)                 │
├─────────────────────────────────────────────────────────────┤
│ [5 Whys, Fishbone, data analysis]                           │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 5. COUNTERMEASURES (Solutions addressing root causes)       │
├─────────────────────────────────────────────────────────────┤
│ [Specific actions, not vague intentions]                    │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 6. IMPLEMENTATION PLAN (Who, What, When)                    │
├─────────────────────────────────────────────────────────────┤
│ [Timeline, responsibilities, dependencies, milestones]      │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 7. FOLLOW-UP (Verification & Prevention)                    │
├─────────────────────────────────────────────────────────────┤
│ [Success metrics, monitoring plan, review dates]            │
└─────────────────────────────────────────────────────────────┘

═══════════════════════════════════════════════════════════════

Examples

Example 1: Database Connection Pool Exhaustion

═══════════════════════════════════════════════════════════════
                    A3 PROBLEM ANALYSIS
═══════════════════════════════════════════════════════════════

TITLE: API Downtime Due to Connection Pool Exhaustion
OWNER: Backend Team Lead
DATE: 2024-11-14

┌─────────────────────────────────────────────────────────────┐
│ 1. BACKGROUND                                                │
├─────────────────────────────────────────────────────────────┤
│ • API goes down 2-3x per week during peak hours             │
│ • Affects 10,000+ users, average 15min downtime             │
│ • Revenue impact: ~$5K per incident                         │
│ • Customer satisfaction score dropped from 4.5 to 3.8       │
│ • Started 3 weeks ago after traffic increased 40%           │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 2. CURRENT CONDITION                                         │
├─────────────────────────────────────────────────────────────┤
│ Observations:                                                │
│ • Connection pool size: 10 (unchanged since launch)         │
│ • Peak concurrent users: 500 (was 300 three weeks ago)      │
│ • Average request time: 200ms (was 150ms)                   │
│ • Connections leaked: ~2 per hour (never released)          │
│ • Error: "Connection pool exhausted" in logs                │
│                                                              │
│ Pattern:                                                     │
│ • Occurs at 2pm-4pm daily (peak traffic)                    │
│ • Gradual degradation over 30 minutes                       │
│ • Recovery requires app restart                             │
│ • Long-running queries block pool (some 30+ seconds)        │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 3. GOAL/TARGET                                               │
├─────────────────────────────────────────────────────────────┤
│ • Zero downtime due to connection exhaustion                │
│ • Support 1000 concurrent users (2x current peak)           │
│ • All connections released within 5 seconds                 │
│ • Achieve within 1 week                                     │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 4. ROOT CAUSE ANALYSIS                                       │
├─────────────────────────────────────────────────────────────┤
│ 5 Whys:                                                      │
│ Problem: Connection pool exhausted                          │
│ Why 1: All 10 connections in use, none available            │
│ Why 2: Connections not released after requests              │
│ Why 3: Error handling doesn't close connections             │
│ Why 4: Try-catch blocks missing .finally()                  │
│ Why 5: No code review checklist for resource cleanup        │
│                                                              │
│ Contributing factors:                                        │
│ • Pool size too small for current load                      │
│ • No connection timeout configured (hangs forever)          │
│ • Slow queries hold connections longer                      │
│ • No monitoring/alerting on pool metrics                    │
│                                                              │
│ ROOT CAUSE: Systematic issue with resource cleanup +        │
│             insufficient pool sizing                         │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 5. COUNTERMEASURES                                           │
├─────────────────────────────────────────────────────────────┤
│ Immediate (This Week):                                       │
│ 1. Audit all DB code, add .finally() for connection release │
│ 2. Increase pool size: 10 → 30                              │
│ 3. Add connection timeout: 10 seconds                       │
│ 4. Add pool monitoring & alerts (>80% used)                 │
│                                                              │
│ Short-term (2 Weeks):                                        │
│ 5. Optimize slow queries (add indexes)                      │
│ 6. Implement connection pooling best practices doc          │
│ 7. Add automated test for connection leaks                  │
│                                                              │
│ Long-term (1 Month):                                         │
│ 8. Migrate to connection pool library with auto-release     │
│ 9. Add linter rule detecting missing .finally()             │
│ 10. Create PR checklist for resource management             │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 6. IMPLEMENTATION PLAN                                       │
├─────────────────────────────────────────────────────────────┤
│ Week 1 (Nov 14-18):                                          │
│ • Day 1-2: Audit & fix connection leaks [Dev Team]          │
│ • Day 2: Increase pool size, add timeout [DevOps]           │
│ • Day 3: Set up monitoring [SRE]                            │
│ • Day 4: Test under load [QA]                               │
│ • Day 5: Deploy to production [DevOps]                      │
│                                                              │
│ Week 2 (Nov 21-25):                                          │
│ • Optimize identified slow queries [DB Team]                │
│ • Write best practices doc [Tech Writer + Dev Lead]         │
│ • Create connection leak test [QA Team]                     │
│                                                              │
│ Week 3-4 (Nov 28 - Dec 9):                                   │
│ • Evaluate connection pool libraries [Dev Team]             │
│ • Add linter rules [Dev Lead]                               │
│ • Update PR template [Dev Lead]                             │
│                                                              │
│ Dependencies: None blocking Week 1 fixes                     │
│ Resources: 2 developers, 1 DevOps, 1 SRE                    │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 7. FOLLOW-UP                                                 │
├─────────────────────────────────────────────────────────────┤
│ Success Metrics:                                             │
│ • Zero downtime incidents (monitor 4 weeks)                 │
│ • Pool usage stays <80% during peak                         │
│ • No connection leaks detected                              │
│ • Response time <200ms p95                                  │
│                                                              │
│ Monitoring:                                                  │
│ • Daily: Check pool usage dashboard                         │
│ • Weekly: Review connection leak alerts                     │
│ • Bi-weekly: Team retrospective on progress                 │
│                                                              │
│ Review Dates:                                                │
│ • Week 1 (Nov 18): Verify immediate fixes effective         │
│ • Week 2 (Nov 25): Assess optimization impact               │
│ • Week 4 (Dec 9): Final review, close A3                    │
│                                                              │
│ Prevention:                                                  │
│ • Add connection handling to onboarding                     │
│ • Monthly audit of resource management code                 │
│ • Include pool metrics in SRE runbook                       │
└─────────────────────────────────────────────────────────────┘

═══════════════════════════════════════════════════════════════

Example 2: Security Vulnerability in Production

═══════════════════════════════════════════════════════════════
                    A3 PROBLEM ANALYSIS
═══════════════════════════════════════════════════════════════

TITLE: Critical SQL Injection Vulnerability
OWNER: Security Team Lead
DATE: 2024-11-14

┌─────────────────────────────────────────────────────────────┐
│ 1. BACKGROUND                                                │
├─────────────────────────────────────────────────────────────┤
│ • Critical security vulnerability reported by researcher    │
│ • SQL injection in user search endpoint                     │
│ • Potential data breach affecting 100K+ user records        │
│ • CVSS score: 9.8 (Critical)                                │
│ • Vulnerability exists in production for 6 months           │
│ • Similar issue found in 2 other endpoints (scanning)       │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 2. CURRENT CONDITION                                         │
├─────────────────────────────────────────────────────────────┤
│ Vulnerable Code:                                             │
│ • /api/users/search endpoint uses string concatenation      │
│ • Input: search query (user-provided, not sanitized)        │
│ • Pattern: `SELECT * FROM users WHERE name = '${input}'`    │
│                                                              │
│ Scope:                                                       │
│ • 3 endpoints vulnerable (search, filter, export)           │
│ • All use same unsafe pattern                               │
│ • No parameterized queries                                  │
│ • No input validation layer                                 │
│                                                              │
│ Risk Assessment:                                             │
│ • Exploitable from public internet                          │
│ • No evidence of exploitation (logs checked)                │
│ • Similar code in admin panel (higher privilege)            │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 3. GOAL/TARGET                                               │
├─────────────────────────────────────────────────────────────┤
│ • Patch all SQL injection vulnerabilities within 24 hours   │
│ • Zero SQL injection vulnerabilities in codebase            │
│ • Prevent similar issues in future code                     │
│ • Verify no unauthorized access occurred                    │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 4. ROOT CAUSE ANALYSIS                                       │
├─────────────────────────────────────────────────────────────┤
│ 5 Whys:                                                      │
│ Problem: SQL injection vulnerability in production          │
│ Why 1: User input concatenated directly into SQL            │
│ Why 2: Developer wasn't aware of SQL injection risks        │
│ Why 3: No security training for new developers              │
│ Why 4: Security not part of onboarding checklist            │
│ Why 5: Security team not involved in development process    │
│                                                              │
│ Contributing Factors (Fishbone):                             │
│ • Process: No security code review                          │
│ • Technology: ORM not used consistently                     │
│ • People: Knowledge gap in secure coding                    │
│ • Methods: No SAST tools in CI/CD                           │
│                                                              │
│ ROOT CAUSE: Security not integrated into development        │
│             process, training gap                            │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 5. COUNTERMEASURES                                           │
├─────────────────────────────────────────────────────────────┤
│ Immediate (24 Hours):                                        │
│ 1. Patch all 3 vulnerable endpoints                         │
│ 2. Deploy hotfix to production                              │
│ 3. Scan codebase for similar patterns                       │
│ 4. Review access logs for exploitation attempts             │
│                                                              │
│ Short-term (1 Week):                                         │
│ 5. Replace all raw SQL with parameterized queries           │
│ 6. Add input validation middleware                          │
│ 7. Set up SAST tool in CI (Snyk/SonarQube)                  │
│ 8. Security team review of all data access code             │
│                                                              │
│ Long-term (1 Month):                                         │
│ 9. Mandatory security training for all developers           │
│ 10. Add security review to PR process                       │
│ 11. Migrate to ORM for all database access                  │
│ 12. Implement security champion program                     │
│ 13. Quarterly security audits                               │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 6. IMPLEMENTATION PLAN                                       │
├─────────────────────────────────────────────────────────────┤
│ Hour 0-4 (Emergency Response):                               │
│ • Write & test patches [Security + Senior Dev]              │
│ • Emergency PR review [CTO + Tech Lead]                     │
│ • Deploy to staging [DevOps]                                │
│                                                              │
│ Hour 4-24 (Production Deploy):                               │
│ • Deploy hotfix [DevOps + On-call]                          │
│ • Monitor for issues [SRE Team]                             │
│ • Scan logs for exploitation [Security Team]                │
│ • Notify stakeholders [Security Lead + CEO]                 │
│                                                              │
│ Day 2-7:                                                     │
│ • Full codebase remediation [Dev Team]                      │
│ • SAST tool setup [DevOps + Security]                       │
│ • Security review [External Auditor]                        │
│                                                              │
│ Week 2-4:                                                    │
│ • Security training program [Security + HR]                 │
│ • Process improvements [Engineering Leadership]             │
│                                                              │
│ Dependencies: External auditor availability (Week 2)         │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ 7. FOLLOW-UP                                                 │
├─────────────────────────────────────────────────────────────┤
│ Success Metrics:                                             │
│ • Zero SQL injection vulnerabilities (verified by scan)     │
│ • 100% of PRs pass SAST checks                              │
│ • 100% developer security training completion               │
│ • No unauthorized access detected in log analysis           │
│                                                              │
│ Verification:                                                │
│ • Day 1: Verify patch deployed, vulnerability closed        │
│ • Week 1: External security audit confirms fixes            │
│ • Week 2: SAST tool catching similar issues                 │
│ • Month 1: Training completion, process adoption            │
│                                                              │
│ Prevention:                                                  │
│ • SAST tools block vulnerable code in CI                    │
│ • Security review required for data access code             │
│ • Quarterly penetration testing                             │
│ • Annual security training refresh                          │
│                                                              │
│ Incident Report:                                             │
│ • Post-mortem meeting: Nov 16                               │
│ • Document lessons learned                                  │
│ • Share with engineering org                                │
└─────────────────────────────────────────────────────────────┘

═══════════════════════════════════════════════════════════════

Notes

  • A3 forces concise, complete thinking (fits on one page)
  • Use data and facts, not opinions or blame
  • Root cause analysis is critical—use /why or /cause-and-effect
  • Countermeasures must address root causes, not symptoms
  • Implementation plan needs clear ownership and timelines
  • Follow-up ensures sustainable improvement
  • A3 becomes historical record for organizational learning
  • Update A3 as situation evolves (living document until closed)
  • Consider A3 for: incidents, recurring issues, major improvements
  • Overkill for: small bugs, one-line fixes, trivial issues

Frequently asked questions

What does the Analyse Problem AI skill do?

Comprehensive A3 one-page problem analysis with root cause and action plan

Why use Analyse Problem on TypingMind?

Because you install it once and use it with any model. Analyse Problem is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analyse Problem in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NeoLabHQ/context-engineering-kit/tree/master/antigravity/skills/analyse-problem. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Analyse Problem?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analyse Problem?

As many as you like. As long as a model supports skills, you can use Analyse Problem with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analyse Problem AI skill free?

Yes. It is published on GitHub by NeoLabHQ under the GPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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