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API Design

@yaleh/meta-cc
12
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Systematic API design methodology with 6 validated patterns covering parameter categorization, safe refactoring, audit-first approach, automated validation, quality gates, and example-driven documentation. Use when designing new APIs, improving API consistency, implementing breaking change policies, or building API quality enforcement. Provides deterministic decision trees (5-tier parameter system), validation tool architecture, pre-commit hook patterns. Validated with 82.5% cross-domain transferability, 37.5% efficiency gains through audit-first refactoring.

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SKILL.md

name API Design
description Systematic API design methodology with 6 validated patterns covering parameter categorization, safe refactoring, audit-first approach, automated validation, quality gates, and example-driven documentation. Use when designing new APIs, improving API consistency, implementing breaking change policies, or building API quality enforcement. Provides deterministic decision trees (5-tier parameter system), validation tool architecture, pre-commit hook patterns. Validated with 82.5% cross-domain transferability, 37.5% efficiency gains through audit-first refactoring.
allowed-tools Read, Write, Edit, Bash, Grep, Glob

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:

  1. List all targets to audit
  2. Define compliance criteria
  3. Assess each target (compliant vs. non-compliant)
  4. Categorize and prioritize
  5. Execute changes on non-compliant targets only
  6. 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

  1. Single domain validation: Patterns extracted from API design only, need validation in non-API contexts
  2. JSON-specific: Pattern 2 (Safe Refactoring) assumes JSON-based APIs
  3. Moderate scale: Validated on 16-tool API, not tested on 100+ tool systems
  4. 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:

  1. Required (must have)
  2. Filtering (WHAT is returned)
  3. Range (bounds/thresholds)
  4. Output control (HOW MUCH)
  5. 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