Error Shape Taxonomy
Organization-wide standard error response format covering error codes, categories, and structure that enables clients and monitoring tools to understand errors immediately.
Best use case
Error Shape Taxonomy is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Organization-wide standard error response format covering error codes, categories, and structure that enables clients and monitoring tools to understand errors immediately.
Teams using Error Shape Taxonomy should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/error-shape-taxonomy/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Error Shape Taxonomy Compares
| Feature / Agent | Error Shape Taxonomy | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Organization-wide standard error response format covering error codes, categories, and structure that enables clients and monitoring tools to understand errors immediately.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Error Shape Taxonomy
## Skill Profile
*(Select at least one profile to enable specific modules)*
- [ ] **DevOps**
- [x] **Backend**
- [ ] **Frontend**
- [ ] **AI-RAG**
- [ ] **Security Critical**
## Overview
Organization-wide standard error response format covering error codes, categories, and structure that enables clients and monitoring tools to understand errors immediately.
## Why This Matters
- **Debuggability**: รู้ทันทีว่า error มาจากไหน ทำไม
- **Client handling**: Frontend/mobile handle errors ได้ถูกต้อง
- **Monitoring**: Alert และ dashboard แยก error types ได้
- **Documentation**: Error catalog ที่ reference ได้
---
## Core Concepts
#
## 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
```typescript
export type ErrorCategory = "AUTH" | "AUTHZ" | "VAL" | "BIZ" | "RATE" | "SYS";
export interface ErrorResponse {
error: {
code: string;
category: ErrorCategory;
message: string;
status: number;
requestId: string;
timestamp: string;
path?: string;
method?: string;
retryable?: boolean;
retryAfterSeconds?: number;
details?: Record<string, unknown>;
validationErrors?: Array<{ field: string; reason: string }>;
};
}
```
## 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**:
- `.env.example` keys: `API_KEY`, `DATABASE_URL` (no values)
## 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`, `latency`, `queue_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
1. **Generic errors**: "Something went wrong"
2. **Leaking internals**: Stack traces to client
3. **Inconsistent shape**: Different format per service
4. **Missing correlation**: No request ID
5. **Changing meaning**: เปลี่ยน semantics ของ code เดิม ทำให้ client/alert พัง
## 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 structureRelated Skills
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