# Documentation

> Implements intelligent documentation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense

- Skill: `paulpas/documentation` (Agent Skill)
- Install (CLI): `npx skillmds@latest add paulpas/documentation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/documentation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: paulpas (https://skillmd.com/u/paulpas)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/paulpas/documentation

---





# Documentation

Orchestrates intelligent skill selection and execution for 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 select_skill(
    task_description: str,
    available_skills: List[Dict],
    min_confidence: float = 0.7
) -> Optional[Dict]:
    """Route a documentation request to the optimal generator.
    
    Evaluates generators based on content type match, historical accuracy,
    and current system load. Returns structured routing metadata.
    """
    # Guard clause - Early Exit (Law 1)
    if not task_description or not task_description.strip():
        raise ValueError("Task description cannot be empty")
        
    if not available_skills:
        raise ValueError("No skills available for selection")
    
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    target_type = "documentation"
    best_match = None
    best_score = 0.0
    
    for skill in available_skills:
        type_match = 1.0 if skill.get("doc_format") == target_type else 0.5
        history_score = skill.get("success_rate", 0.0)
        load_penalty = 1.0 - (skill.get("current_load", 0.0) / 100.0)
        
        score = (type_match * 0.5) + (history_score * 0.3) + (load_penalty * 0.2)
        
        if score > best_score and score >= min_confidence:
            best_score = score
            best_match = skill
    
    if best_match is None:
        return None
    
    # Atomic Predictability (Law 3) - Return new dict, don't mutate
    result = dict(best_match)
    result["selected_confidence"] = best_score
    result["selection_timestamp"] = time.time()
    return result
```


### Pattern 2: Execution with Fallback

```python
def execute_with_fallback(
    skill: Dict,
    task_context: Dict,
    max_retries: int = 2
) -> Dict:
    """Execute documentation generation with structured fallback chain.
    
    Implements the Fail Fast, Fail Loud principle (Law 4):
    - Invalid states halt immediately with descriptive errors
    - No silent failures or partial results
    
    Fallback chain:
    1. Retry with adjusted template parameters
    2. Fallback to static markdown generator
    3. Queue for human documentation specialist
    """
    # Guard clause - validate skill (Early Exit)
    if not _validate_doc_schema(task_context):
        raise DocGenerationError("Invalid documentation schema provided")
    
    # Parse context - Ensure trusted state (Law 2)
    validated_context = _parse_and_validate_context(task_context, skill)
    
    for attempt in range(max_retries + 1):
        try:
            raw_output = _invoke_generator(skill, validated_context)
            validated_doc = _parse_and_validate_output(raw_output)
            
            # Success - Atomic Predictability (Law 3)
            return {
                "success": True,
                "skill_executed": skill["name"],
                "format": skill.get("output_format", "markdown"),
                "content": validated_doc,
                "attempts": attempt + 1,
                "latency_ms": time.time() * 1000
            }
            
        except SchemaValidationError as e:
            # Fail Fast - Don't try to patch bad data (Law 4)
            raise DocGenerationError(f"Schema validation failed: {e}") from e
            
        except GeneratorTimeoutError:
            # Transient error - try fallback
            if attempt == max_retries:
                return _fallback_to_static_generator(validated_context)
    
    # All retries exhausted - Fail Loud (Law 4)
    return _fallback_to_human_review(validated_context, skill["name"])
```

### 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 |
|

---

---

## 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.
- [DITA Open Toolkit](<https://dita.xml.org/get-started/>)
- [MkDocs Documentation Tool](<https://www.mkdocs.org/>)
- [Sphinx Documentation Generator](<https://www.sphinx-doc.org/en/master/>)
- [MDX Documentation Format](<https://mdxjs.com/>)
- [Google Developer Documentation Style Guide](<https://developers.google.com/style>)