API Design
Systematic API design with validated patterns and automated quality enforcement.
Good APIs are designed, not discovered. 82.5% of patterns transfer across domains.
When to Use This Skill
Use this skill when:
- 🎯 Designing new API: Need systematic parameter organization and naming conventions
- 🔄 Refactoring existing API: Improving consistency without breaking changes
- 📊 API quality enforcement: Building validation tools and quality gates
- 📝 API documentation: Writing clear, example-driven documentation
- 🚀 API evolution: Implementing versioning, deprecation, and migration policies
- 🔍 API consistency: Standardizing conventions across multiple endpoints
Don't use when:
- ❌ API has <5 endpoints (overhead not justified)
- ❌ No team collaboration (conventions only valuable for teams)
- ❌ Prototype/throwaway code (skip formalization)
- ❌ Non-REST/non-JSON APIs without adaptation (patterns assume JSON-based APIs)
Prerequisites
Tools
- API framework (language-specific): Go, Python, TypeScript, etc.
- Validation tools (optional): Linters, schema validators
- Version control: Git (for pre-commit hooks)
Concepts
- REST principles: Resource-based design, HTTP methods
- JSON specification: Object property ordering (unordered), schema design
- Semantic Versioning: Major.Minor.Patch versioning (if using Pattern 1)
- Pre-commit hooks: Git hooks for quality gates
Background Knowledge
- API design basics (endpoints, parameters, responses)
- Backward compatibility principles
- Testing strategies (integration tests, contract tests)
Quick Start (30 minutes)
This skill was extracted using systematic knowledge extraction methodology from Bootstrap-006 experiment.
Status: PARTIAL EXTRACTION (demonstration of methodology, not complete skill)
Note: This is a minimal viable skill created to validate the knowledge extraction methodology. A complete skill would include:
- Detailed pattern descriptions with code examples
- Step-by-step walkthroughs for each pattern
- Templates for API specifications
- Scripts for validation and quality gates
- Comprehensive reference documentation
Extraction Evidence:
- Source experiment: Bootstrap-006 (V_instance=0.87, V_meta=0.786)
- Patterns extracted: 6/6 identified (not yet fully documented here)
- Principles extracted: 8/8 identified (not yet fully documented here)
- Extraction time: 30 minutes (partial, demonstration only)
Patterns Overview
Pattern 1: Deterministic Parameter Categorization
Context: When designing or refactoring API parameters, categorization decisions must be consistent and unambiguous.
Solution: Use 5-tier decision tree system:
- Tier 1: Required parameters (can't execute without)
- Tier 2: Filtering parameters (affect WHAT is returned)
- Tier 3: Range parameters (define bounds/thresholds)
- Tier 4: Output control parameters (affect HOW MUCH is returned)
- Tier 5: Standard parameters (cross-cutting concerns, framework-applied)
Evidence: 100% determinism across 8 tools, 37.5% efficiency gain through pre-audit
Transferability: ✅ Universal to all query-based APIs (REST, GraphQL, CLI)
Pattern 2: Safe API Refactoring via JSON Property
Context: Need to improve API schema readability without breaking existing clients.
Solution: Leverage JSON specification guarantee that object properties are unordered. Parameter order in schema definition is documentation only.
Evidence: 60 lines changed, 100% test pass rate, zero compatibility issues
Transferability: ✅ Universal to all JSON-based APIs
Pattern 3: Audit-First Refactoring
Context: Need to refactor multiple targets (tools, parameters, schemas) for consistency.
Solution: Systematic audit process before making changes:
- List all targets to audit
- Define compliance criteria
- Assess each target (compliant vs. non-compliant)
- Categorize and prioritize
- Execute changes on non-compliant targets only
- Verify compliant targets (no changes)
Evidence: 37.5% unnecessary work avoided (3 of 8 tools already compliant)
Transferability: ✅ Universal to any refactoring effort (not API-specific)
Patterns 4-6
Note: Patterns 4-6 (Automated Consistency Validation, Automated Quality Gates, Example-Driven Documentation) are documented in the source experiment (Bootstrap-006) but not yet extracted here due to time constraints in this validation iteration.
Source: See experiments/bootstrap-006-api-design/results.md lines 616-733 for full descriptions.
Core Principles
1. Specifications Alone are Insufficient
Statement: Methodology extraction requires observing execution, not just reading design documents.
Evidence: Bootstrap-006 Iterations 0-3 produced 0 patterns (specifications only), Iterations 4-6 extracted 6 patterns (execution observed).
Application: Always combine design work with implementation to enable pattern extraction.
2. Operational Quality > Design Quality
Statement: Operational implementation scores higher than design quality when verification is rigorous.
Evidence: Design V_consistency = 0.87, Operational V_consistency = 0.94 (+0.07).
Application: Be conservative with design estimates. Reserve high scores (0.90+) for operational verification.
3-8. Additional Principles
Note: Principles 3-8 are documented in source experiment but not yet extracted here due to time constraints.
Success Metrics
Instance Layer (Task Quality):
- API usability: 0.83
- API consistency: 0.97
- API completeness: 0.76
- API evolvability: 0.88
- Overall: V_instance = 0.87 (exceeds 0.80 threshold by +8.75%)
Meta Layer (Methodology Quality):
- Methodology completeness: 0.85
- Methodology effectiveness: 0.66
- Methodology reusability: 0.825
- Overall: V_meta = 0.786 (approaches 0.80 threshold, gap -1.4%)
Validation: Transfer test across domains achieved 82.5% average pattern transferability (empirically validated).
Transferability
Language Independence: ✅ HIGH (75-85%)
- Patterns focus on decision-making processes, not language features
- Tested primarily in Go, but applicable to Python, TypeScript, Rust, Java
Domain Independence: ✅ HIGH (82.5% empirically validated)
- Patterns transfer from MCP Tools API to Slash Command Capabilities with minor adaptation
- Universal patterns (3, 4, 5, 6): 67% of methodology
- Domain-specific patterns (1, 2): Require adaptation for different parameter models
Codebase Generality: ✅ MODERATE (60-75%)
- Validated on meta-cc (16 MCP tools, moderate scale)
- Application to very large APIs (100+ tools) unvalidated
- Principles scale-independent, but tooling may need adaptation
Limitations and Gaps
Known Limitations
- Single domain validation: Patterns extracted from API design only, need validation in non-API contexts
- JSON-specific: Pattern 2 (Safe Refactoring) assumes JSON-based APIs
- Moderate scale: Validated on 16-tool API, not tested on 100+ tool systems
- Conservative effectiveness: No control group study (ad-hoc vs. methodology comparison)
Skill Completeness
Current Status: PARTIAL EXTRACTION (30% complete)
Completed:
- ✅ Frontmatter (name, description, allowed-tools)
- ✅ When to Use / Prerequisites
- ✅ Patterns 1-3 documented (summaries)
- ✅ Principles 1-2 documented
- ✅ Success Metrics / Transferability / Limitations
Missing (to be completed in future iterations):
- ❌ Patterns 4-6 detailed documentation
- ❌ Principles 3-8 documentation
- ❌ Step-by-step walkthroughs (examples/)
- ❌ Templates directory (API specification templates)
- ❌ Scripts directory (validation tools, quality gates)
- ❌ Reference documentation (comprehensive pattern catalog)
Reason for Incompleteness: This skill created as validation of knowledge extraction methodology, not as production-ready artifact. Demonstrates methodology viability but requires additional 60-90 minutes for completion.
Related Skills
- Testing Strategy: API testing patterns, integration tests, contract tests
- Error Recovery: API error handling, error taxonomy
- CI/CD Optimization: Pre-commit hooks, automated quality gates (overlaps with Pattern 5)
Quick Reference
5-Tier Parameter System:
- Required (must have)
- Filtering (WHAT is returned)
- Range (bounds/thresholds)
- Output control (HOW MUCH)
- Standard (cross-cutting)
Audit-First Efficiency: 37.5% work avoided (3/8 tools already compliant)
Transferability: 82.5% average (empirical validation across domains)
Convergence: V_instance = 0.87, V_meta = 0.786
Skill Status: DEMONSTRATION / PARTIAL EXTRACTION Extraction Source: Bootstrap-006-api-design Extraction Date: 2025-10-19 Extraction Time: 30 minutes (partial) Next Steps: Complete Patterns 4-6, add examples, create templates and scripts