---

name: skill-writer compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent skill writer 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: skill-writer, skill writer, how do i skill-writer, orchestrate skill-writer, automate skill-writer, agent skill-writer archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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Skill Writer

Orchestrates intelligent skill selection and execution for skill writer 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 generate_skill_manifest(
    intent_description: str,
    existing_skills: List[Dict],
    schema_version: str = "1.0.0"
) -> Dict:
    """Generate a structured skill manifest based on user intent.
    
    Validates intent against available skill triggers, constructs
    YAML frontmatter, and assembles the markdown skeleton.
    Applies Law 2 (Parse at boundary) by strictly validating inputs.
    """
    if not intent_description or not intent_description.strip():
        raise ValueError("Intent description is required for skill generation")
        
    # Parse intent features at boundary
    intent_features = _parse_intent_features(intent_description)
    
    # Match against existing skill patterns to avoid duplication
    matches = _find_similar_skills(intent_features, existing_skills)
    if matches:
        return {
            "status": "duplicate_detected",
            "suggested_skill": matches[0]["name"],
            "confidence": matches[0]["match_score"]
        }
        
    # Construct domain-specific manifest structure
    manifest = {
        "frontmatter": {
            "name": _sanitize_skill_name(intent_features["core_action"]),
            "version": schema_version,
            "triggers": intent_features["trigger_keywords"],
            "role": intent_features["agent_role"],
            "scope": intent_features["execution_scope"]
        },
        "structure": {
            "sections": ["TL;DR Checklist", "Core Workflow", "Implementation Patterns", "Constraints"],
            "required_code_blocks": 2,
            "min_domain_specific_lines": 15
        }
    }
    
    # Atomic Predictability (Law 3) - return fresh structure
    return manifest

Pattern 2: Execution with Fallback

def validate_and_refine_skill(
    skill_content: str,
    validation_rules: Dict,
    max_refinement_cycles: int = 3
) -> Dict:
    """Validate generated skill content against domain rules and refine iteratively.
    
    Implements Law 4 (Fail Fast) by halting on structural violations.
    Uses a refinement fallback chain when initial generation misses compliance.
    """
    if not skill_content or len(skill_content) < 100:
        raise ValueError("Skill content too short to validate")
        
    parsed = _parse_skill_markdown(skill_content)
    if not parsed.get("frontmatter"):
        raise ValueError("Missing YAML frontmatter - cannot proceed")
        
    for cycle in range(max_refinement_cycles):
        violations = _check_compliance(parsed, validation_rules)
        if not violations:
            return {
                "status": "validated",
                "skill_name": parsed["frontmatter"]["name"],
                "compliance_score": 1.0,
                "cycles_used": cycle + 1
            }
            
        # Fallback: Apply targeted refinement based on violation type
        parsed = _apply_refinement(parsed, violations)
        _log_refinement_cycle(cycle + 1, violations)
        
    # All cycles exhausted - Fail Loud with actionable error
    return {
        "status": "refinement_exhausted",
        "skill_name": parsed["frontmatter"]["name"],
        "remaining_violations": violations,
        "confidence": 0.0
    }

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
skill-creator	The creation workflow counterpart — writer focuses on documentation, creator covers the full lifecycle
skill-documentation-best-practices	Provides documentation patterns that skill writers use to produce high-fidelity content

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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 domain. The model follows markdown links at load time to resolve external references and inline content.

• CommonMark Specification — Official Markdown specification governing SKILL.md file format
• Writing Documentation for AI Systems (Google) — Google's writing style guide adapted for AI system documentation
• Technical Communication Standards (Diátaxis Framework) — The Diátaxis framework for structuring technical documentation across tutorial, how-to, reference, and explanation categories
• API Documentation Best Practices (Stoplight) — Industry standards for writing clear API documentation applicable to skill metadata documentation
• Obsidian Help: Markdown Syntax — Practical Markdown reference covering all syntax elements used in SKILL.md files