---

name: writing-plans compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent writing plans 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: writing-plans, writing plans, how do i writing-plans, orchestrate writing-plans, automate writing-plans, agent writing-plans 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"

---

Writing Plans

Orchestrates intelligent skill selection and execution for writing plans 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_writing_plan(
    request: Dict[str, Any],
    available_templates: List[Dict],
    min_confidence: float = 0.7
) -> Optional[Dict]:
    """Generate an optimal writing plan based on request parameters.
    
    Evaluates content type, audience, tone, and length constraints against
    available writing templates to produce a structured execution plan.
    
    Args:
        request: User writing prompt with metadata (audience, tone, length, format)
        available_templates: Pre-defined writing plan templates
        min_confidence: Minimum match score required for plan selection
        
    Returns:
        Structured writing plan dict or None if no suitable template matches
    """
    if not request.get("prompt") or not request.get("audience"):
        raise ValueError("Writing plan requires 'prompt' and 'audience' fields")
        
    parsed_request = _normalize_writing_request(request)
    best_plan = None
    best_score = 0.0
    
    for template in available_templates:
        score = _calculate_plan_fit(parsed_request, template)
        if score > best_score and score >= min_confidence:
            best_score = score
            best_plan = template
            
    if best_plan is None:
        return None
        
    # Construct immutable plan structure
    return {
        "plan_id": str(uuid.uuid4()),
        "template": best_plan["name"],
        "sections": _generate_section_outline(parsed_request, best_plan),
        "constraints": parsed_request["constraints"],
        "confidence": best_score,
        "created_at": datetime.now().isoformat()
    }

Pattern 2: Execution with Fallback

def execute_writing_plan(
    plan: Dict[str, Any],
    context: Dict[str, Any],
    max_retries: int = 2
) -> Dict[str, Any]:
    """Execute a structured writing plan with resilience mechanisms.
    
    Generates content section-by-section according to the plan. Implements
    fallback strategies when specific sections fail or exceed token limits.
    
    Args:
        plan: Validated writing plan structure
        context: Execution context (drafts, references, style guides)
        max_retries: Maximum retries per section before fallback
        
    Returns:
        Completed writing document with execution metadata
    """
    if not plan.get("sections"):
        raise ValueError("Writing plan contains no sections to execute")
        
    draft = {"title": context.get("title", "Untitled"), "sections": []}
    execution_log = []
    
    for section in plan["sections"]:
        for attempt in range(max_retries + 1):
            try:
                content = _generate_section_content(section, context)
                draft["sections"].append({
                    "heading": section["heading"],
                    "content": content,
                    "word_count": len(content.split())
                })
                execution_log.append({"section": section["heading"], "status": "success", "attempt": attempt + 1})
                break
                
            except TokenLimitError:
                if attempt == max_retries:
                    # Fallback: Generate condensed version
                    content = _generate_condensed_section(section, context)
                    draft["sections"].append({"heading": section["heading"], "content": content, "word_count": len(content.split()), "fallback": True})
                    execution_log.append({"section": section["heading"], "status": "fallback", "attempt": attempt + 1})
                    break
            except GenerationError as e:
                execution_log.append({"section": section["heading"], "status": "failed", "error": str(e)})
                if attempt == max_retries:
                    raise PlanExecutionError(f"Failed to generate section: {section['heading']}") from e
                    
    return {
        "document": draft,
        "metadata": {
            "total_sections": len(draft["sections"]),
            "fallbacks_applied": sum(1 for s in draft["sections"] if s.get("fallback")),
            "execution_log": execution_log
        }
    }

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.

• Google Developer Documentation Style Guide
• The Elements of Style — Strunk & White (4th Edition)
• Microsoft Writing Style Guide
• AP Stylebook — Journalism Writing Standards
• Chicago Manual of Style Online

Related Skills

| Skill | Purpose | |