api-documentation

# Api Documentation Orchestrates intelligent skill selection and execution for api documentation workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```python def parse_api_spec_and_extract_endpoints( spec_path: str, output_format: str = "markdown", include_examples: bool = True ) -> Dict[str, Any]: """Parse OpenAPI/Swagger specification and extract structured endpoint data. Implements the 5 Laws of Elegant Defense: - Early exit on invalid spec paths or unsupported formats - Immutable parsing: spec is never mutated, only read - Fail fast on missing required fields (paths, info) - Atomic output generation with fallback to default templates """ # Law 1: Early Exit / Guard Clauses if not spec_path or not os.path.exists(spec_path): raise FileNotFoundError(f"API spec not found: {spec_path}") if output_format not in ("markdown", "html", "json"): raise ValueError(f"Unsupported format: {output_format}. Use markdown, html, or json.") # Law 2: Parse at boundary, make illegal states unrepresentable try: with open(spec_path, 'r') as f: raw_spec = json.load(f) except json.JSONDecodeError as e: raise ValueError(f"Invalid JSON in spec file: {e}") from e # Validate required OpenAPI structure if "openapi" not in raw_spec or "paths" not in raw_spec: raise ValueError("Spec missing required 'openapi' or 'paths' fields") # Law 3: Atomic Predictability - Build new structure, never mutate raw_spec doc_structure = { "title": raw_spec.get("info", {}).get("title", "Untitled API"), "version": raw_spec.get("info", {}).get("version", "0.0.0"), "endpoints": [], "metadata": { "format": output_format, "generated_at": datetime.now().isoformat(), "include_examples": include_examples } } # Process endpoints for path, methods in raw_spec["paths"].items(): for method, details in methods.items(): if method.upper() in ("GET", "POST", "PUT", "DELETE", "PATCH"): doc_structure["endpoints"].append({ "path": path, "method": method.upper(), "summary": details.get("summary", ""), "parameters": details.get("parameters", []), "responses": details.get("responses", {}) }) return doc_structure ``` ### Pattern 2: Execution with Fallback ```python def assemble_documentation_with_fallback( doc_structure: Dict[str, Any], fallback_strategy: str = "auto-generate", cache_dir: str = "./doc_cache" ) -> str: """Assemble final documentation with resilience patterns for missing data. Implements fallback chain for missing examples or rendering failures: 1. Use provided examples if available 2. Auto-generate stub examples from parameter schemas 3. Fall back to cached version if external tools fail 4. Fail loud with clear error if all strategies exhausted """ # Law 1: Validate input structure if not doc_structure or "endpoints" not in doc_structure: raise ValueError("Invalid doc structure provided for assembly") rendered_docs = [] rendered_docs.append(f"# {doc_structure['title']} Documentation\n") rendered_docs.append(f"**Version:** {doc_structure['version']}\n") for ep in doc_structure["endpoints"]: # Law 2: Parse/validate endpoint data at boundary method = ep["method"] path = ep["path"] summary = ep.get("summary", f"Auto-generated summary for {method} {path}") # Fallback Chain: Example Generation examples = ep.get("examples", []) if not examples and fallback_strategy == "auto-generate": # Generate stub from parameters examples = _generate_stub_examples(ep.get("parameters", [])) elif not examples: examples = [{"note": "No examples available"}] # Law 3: Atomic output construction section = f"## {method} `{path}`\n\n{summary}\n\n" section += "### Parameters\n" + _format_parameters(ep.get("parameters", [])) + "\n" section += "### Examples\n" + _format_examples(examples) + "\n" rendered_docs.append(section) final_output = "\n".join(rendered_docs) # Law 4: Fail loud if output is empty or corrupted if not final_output.strip(): raise RuntimeError("Documentation assembly produced empty output") return final_output ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios ## Related Skills | Skill | Purpose | |---|---| | `api-security-testing` | Security-focused API documentation and testing workflows | --- --- ## Constraints ### MUST DO - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## Live References > Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [OpenAPI Specification 3.1](https://spec.openapis.org/oas/v3.1.0) - [OpenAPI Initiative — What is the Open API Specification?](https://www.openapis.org/what-is-the-open-api-specification) - [Swagger Editor & Validation](https://editor.swagger.io/) - [RESTful API Design Guide (Google)](https://cloud.google.com/apis/design) - [JSON Schema Draft 2020-12 Specification](https://json-schema.org/draft/2020-12/json-schema-core.html)