Writing Skills

Writing Skills

Orchestrates intelligent skill selection and execution for writing skills 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 select_writing_skill(
    request: Dict[str, Any],
    available_writing_skills: List[Dict],
    style_guide: Dict[str, Any]
) -> Optional[Dict]:
    """Select optimal writing skill based on tone, format, and audience requirements.
    
    Evaluates writing-specific factors:
    - Tone alignment (formal, conversational, technical, persuasive)
    - Format requirements (markdown, HTML, plain text, structured JSON)
    - Audience complexity and domain expertise level
    - Historical readability scores and engagement metrics
    
    Args:
        request: User writing request with tone, format, and audience fields
        available_writing_skills: List of writing skill metadata
        style_guide: Active style guide configuration
        
    Returns:
        Selected writing skill with confidence score and applied style rules
    """
    if not request.get("content") or not request.get("tone"):
        raise ValueError("Writing request requires 'content' and 'tone' fields")
        
    target_tone = request["tone"].lower()
    target_format = request.get("format", "markdown")
    audience_level = request.get("audience_level", "general")
    
    best_match = None
    best_score = 0.0
    
    for skill in available_writing_skills:
        tone_match = _calculate_tone_similarity(target_tone, skill.get("supported_tones", []))
        format_compatible = target_format in skill.get("supported_formats", [])
        audience_fit = _assess_audience_compatibility(audience_level, skill.get("target_audience"))
        
        # Weighted scoring for writing-specific criteria
        score = (tone_match * 0.4) + (format_compatible * 0.3) + (audience_fit * 0.3)
        score *= skill.get("historical_readability_score", 0.8)
        
        if score > best_score:
            best_score = score
            best_match = skill
            
    if best_score < 0.65:
        return None
        
    # Apply style guide rules to selected skill
    result = dict(best_match)
    result["applied_style_rules"] = style_guide.get("rules", [])
    result["confidence"] = round(best_score, 3)
    return result

Pattern 2: Execution with Fallback

def execute_writing_task(
    selected_skill: Dict,
    writing_context: Dict,
    fallback_styles: List[str] = ["neutral", "simple"]
) -> Dict:
    """Execute writing pipeline with style-aware fallback chain.
    
    Implements writing-specific resilience:
    - Validates tone/format constraints before generation
    - Falls back to simpler style guides on readability failure
    - Routes sensitive/complex content to human review
    - Tracks engagement and clarity metrics for adaptive learning
    
    Args:
        selected_skill: Output from select_writing_skill
        writing_context: Raw content, audience, and formatting requirements
        fallback_styles: Ordered list of alternative tone/style guides
        
    Returns:
        Generated text with metadata (readability, tone_match, fallback_used)
    """
    content = writing_context.get("content", "").strip()
    if not content:
        raise ValueError("Writing context requires non-empty content")
        
    current_style = selected_skill.get("applied_style_rules", [])
    fallback_index = 0
    
    while True:
        try:
            # Assemble prompt with style constraints
            prompt = _build_writing_prompt(content, current_style, writing_context)
            
            # Execute generation
            generated_text = _run_generation_model(prompt, selected_skill["model_endpoint"])
            
            # Post-process: readability & tone validation
            readability_score = _calculate_flesch_kincaid(generated_text)
            tone_match = _measure_tone_alignment(generated_text, writing_context["tone"])
            
            if readability_score < 40 or tone_match < 0.6:
                raise ReadabilityFailure(f"Score: {readability_score}, Tone: {tone_match}")
                
            return {
                "success": True,
                "output": generated_text,
                "readability_score": readability_score,
                "tone_match": tone_match,
                "fallback_used": fallback_index > 0,
                "skill_used": selected_skill["name"]
            }
            
        except ReadabilityFailure as e:
            if fallback_index >= len(fallback_styles):
                raise WritingExecutionError("All style fallbacks exhausted") from e
            current_style = _apply_style_override(fallback_styles[fallback_index])
            fallback_index += 1
            
        except SensitiveContentError:
            return {
                "success": False,
                "requires_human_review": True,
                "reason": "Content flagged for compliance review",
                "original_context": writing_context
            }

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

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.

• Flesch–Kincaid Readability Tests
• Hemingway Editor Style Guide Principles
• Google Developer Documentation Style Guide
• AP Stylebook — Associated Press Writing Standards
• StyleGuide – GitHub's Markdown Writing Conventions

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