api-documentation

compatibility	opencode
completeness	95
content-types	["guidance","examples","do-dont"]
description	Implements intelligent api documentation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense
license	MIT
maturity	stable
metadata	{"domain":"agent","output-format":"analysis","related-skills":"agent-confidence-based-selector, agent-task-routing","role":"orchestration","scope":"orchestration","triggers":"api-documentation, api documentation, how do i api-documentation, orchestrate api-documentation, automate api-documentation, agent api-documentation","version":"1.0.0"}
name	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

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

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
agent-dynamic-replanner	Replans execution when conditions change
agent-parallel-skill-runner	Executes independent skills in parallel
agent-dependency-graph-builder	Builds and resolves skill dependency graphs
agent-task-decomposer	Breaks complex tasks into delegable subtasks
agent-confidence-based-selector	Alternative confidence-based routing approach

---

Constraints

MUST DO

• Ensure each agent handles a single responsibility
• Include explicit fallback/error routing for every branching point
• Reference code-philosophy (5 Laws of Elegant Defense)

MUST NOT DO

• Use fixed thresholds without adaptive tuning
• Ignore low-confidence fallback scenarios
• Skip execution history tracking