Awesome-omni-skill Problem Framing
Problem framing is the critical first step before any implementation,
install
source · Clone the upstream repo
git clone https://github.com/diegosouzapw/awesome-omni-skill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/backend/problem-framing" ~/.claude/skills/diegosouzapw-awesome-omni-skill-problem-framing && rm -rf "$T"
manifest:
skills/backend/problem-framing/SKILL.mdsafety · automated scan (low risk)
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- references .env files
- references API keys
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source content
Problem Framing
Skill Profile
(Select at least one profile to enable specific modules)
- DevOps
- Backend
- Frontend
- AI-RAG
- Security Critical
Overview
Problem framing is the critical first step before any implementation, teaching agents to detect vague or incomplete requirements, pause execution, and ask clarifying questions. This skill reduces hallucinations, prevents wasted effort, and improves first-shot success rate by ensuring the agent understands the problem before attempting to solve it. It provides systematic methods for ambiguity detection, question formulation, and problem restatement to establish clear understanding between humans and AI agents.
Why This Matters
- Increases First-shot Success: Prevents wasted time on incorrect implementations by clarifying requirements upfront
- Reduces Re-work: Saves significant time by avoiding work on misunderstood requirements
- Improves User Satisfaction: Delivers outputs that match actual needs rather than assumptions
- Enhances Communication: Reduces back-and-forth by establishing clear understanding early
- Supports AI-Human Collaboration: Enables more effective collaboration between AI agents and human users
Core Concepts & Rules
1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs
2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment
Inputs / Outputs / Contracts
- Inputs:
- User task or request
- Available context (code, files, error messages)
- Previous clarifications or discussions
- Entry Conditions:
- Task received from user
- Initial context available
- User is available for clarification
- Outputs:
- Clarifying questions (if ambiguous)
- Problem restatement
- Confirmed understanding
- Implementation plan (after confirmation)
- Artifacts Required (Deliverables):
- Problem statement document
- Clarification questions
- Confirmed understanding from user
- Acceptance Evidence:
- User confirms understanding is correct
- All critical information gathered
- Ambiguities resolved
- Success Criteria:
- User confirms problem restatement
- No remaining ambiguities
- Implementation can proceed without assumptions
Skill Composition
- Depends on: None (foundational skill)
- Compatible with: All other skills (used before any implementation)
- Conflicts with: None
- Related Skills: architectural-reviews, system-thinking
Quick Start / Implementation Example
- Review requirements and constraints
- Set up development environment
- Implement core functionality following patterns
- Write tests for critical paths
- Run tests and fix issues
- Document any deviations or decisions
# Example implementation following best practices def example_function(): # Your implementation here pass
Assumptions / Constraints / Non-goals
- Assumptions:
- Development environment is properly configured
- Required dependencies are available
- Team has basic understanding of domain
- Constraints:
- Must follow existing codebase conventions
- Time and resource limitations
- Compatibility requirements
- Non-goals:
- This skill does not cover edge cases outside scope
- Not a replacement for formal training
Compatibility & Prerequisites
- Supported Versions:
- Python 3.8+
- Node.js 16+
- Modern browsers (Chrome, Firefox, Safari, Edge)
- Required AI Tools:
- Code editor (VS Code recommended)
- Testing framework appropriate for language
- Version control (Git)
- Dependencies:
- Language-specific package manager
- Build tools
- Testing libraries
- Environment Setup:
keys:.env.example
,API_KEY
(no values)DATABASE_URL
Test Scenario Matrix (QA Strategy)
| Type | Focus Area | Required Scenarios / Mocks |
|---|---|---|
| Unit | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage |
| Integration | DB / API | All external API calls or database connections must be mocked during unit tests |
| E2E | User Journey | Critical user flows to test |
| Performance | Latency / Load | Benchmark requirements |
| Security | Vuln / Auth | SAST/DAST or dependency audit |
| Frontend | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |
Technical Guardrails & Security Threat Model
1. Security & Privacy (Threat Model)
- Top Threats: Injection attacks, authentication bypass, data exposure
- Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
- Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
- Authorization: Validate user permissions before state changes
2. Performance & Resources
- Execution Efficiency: Consider time complexity for algorithms
- Memory Management: Use streams/pagination for large data
- Resource Cleanup: Close DB connections/file handlers in finally blocks
3. Architecture & Scalability
- Design Pattern: Follow SOLID principles, use Dependency Injection
- Modularity: Decouple logic from UI/Frameworks
4. Observability & Reliability
- Logging Standards: Structured JSON, include trace IDs
request_id - Metrics: Track
,error_rate
,latencyqueue_depth - Error Handling: Standardized error codes, no bare except
- Observability Artifacts:
- Log Fields: timestamp, level, message, request_id
- Metrics: request_count, error_count, response_time
- Dashboards/Alerts: High Error Rate > 5%
Agent Directives & Error Recovery
(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)
- Thinking Process: Analyze root cause before fixing. Do not brute-force.
- Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
- Self-Review: Check against Guardrails & Anti-patterns before finalizing.
- Output Constraints: Output ONLY the modified code block. Do not explain unless asked.
Definition of Done (DoD) Checklist
- Tests passed + coverage met
- Lint/Typecheck passed
- Logging/Metrics/Trace implemented
- Security checks passed
- Documentation/Changelog updated
- Accessibility/Performance requirements met (if frontend)
Anti-patterns / Pitfalls
- ⛔ Don't: Log PII, catch-all exception, N+1 queries
- ⚠️ Watch out for: Common symptoms and quick fixes
- 💡 Instead: Use proper error handling, pagination, and logging
Reference Links & Examples
- Internal documentation and examples
- Official documentation and best practices
- Community resources and discussions
Versioning & Changelog
- Version: 1.0.0
- Changelog:
- 2026-02-22: Initial version with complete template structure