Awesome-omni-skill Env Diagnosis
Environment diagnosis is systematic process of identifying and resolving
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 "$T/skills/backend/env-diagnosis" ~/.claude/skills/diegosouzapw-awesome-omni-skill-env-diagnosis && rm -rf "$T"
manifest:
skills/backend/env-diagnosis/SKILL.mdsafety · automated scan (low risk)
This is a pattern-based risk scan, not a security review. Our crawler flagged:
- uses sudo
Always read a skill's source content before installing. Patterns alone don't mean the skill is malicious — but they warrant attention.
source content
Env Diagnosis
Skill Profile
(Select at least one profile to enable specific modules)
- DevOps
- Backend
- Frontend
- AI-RAG
- Security Critical
Overview
Environment diagnosis is systematic process of identifying and resolving issues in development environments before they block productivity. This skill provides checklists, automated scripts, and repair protocols for common environment problems including version mismatches, permission issues, port conflicts, and dependency conflicts.
When to use this skill: When experiencing environment issues, when setting up a new development environment, or when encountering "it works on my machine" problems.
Why This Matters
- <Benefit>: <short explanation>
- <Benefit>: <short explanation>
- <Benefit>: <short explanation>
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:
- <e.g., env vars, request payload, file paths, schema>
- Entry Conditions:
- <Pre-requisites: e.g., Repo initialized, DB running, specific branch checked out>
- Outputs:
- <e.g., artifacts (PR diff, docs, tests, dashboard JSON)>
- Artifacts Required (Deliverables):
- <e.g., Code Diff, Unit Tests, Migration Script, API Docs>
- Acceptance Evidence:
- <e.g., Test Report (screenshot/log), Benchmark Result, Security Scan Report>
- Success Criteria:
- <e.g., p95 < 300ms, coverage ≥ 80%>
Skill Composition
- Depends on: None
- Compatible with: None
- Conflicts with: None
- Related Skills: None
Quick Start
Assumptions
- Developer has access to terminal
- Version managers (nvm, pyenv) are available
- Sufficient permissions to make changes
Compatibility
- Works on Linux, macOS, Windows (with WSL)
- Language-agnostic diagnosis principles
- Can be adapted to different environments
Test Scenario Matrix
| Scenario | Diagnosis | Fix | Notes |
|---|---|---|---|
| Node version mismatch | Check vs | Use | Requires nvm |
| Port in use | Check | Kill process | Verify correct process |
| Permission denied | Check | Use | May need sudo |
| Dependency conflict | Check | Clean install | May need to clear cache |
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
- Diagnosis protocol documented
- Version checking procedures
- Permission diagnosis and fixes
- Port conflict resolution
- Dependency issue resolution
- Automated repair scripts
- Quick reference guide
- Common pitfalls documented
- Additional resources linked
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
- Node Version Manager (nvm)
- Python Version Manager (pyenv)
- Docker Troubleshooting
- Linux Permissions Guide
Versioning & Changelog
- Version: 1.0.0
- Changelog:
- 2026-02-22: Initial version with complete template structure