---

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

• guidance
• examples
• do-dont description: Implements intelligent plan writing 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: plan-writing, plan writing, how do i plan-writing, orchestrate plan-writing, automate plan-writing, agent plan-writing 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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Plan Writing

Orchestrates intelligent skill selection and execution for plan writing 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_plan_writing_skills(
    plan_request: Dict[str, Any],
    available_plan_skills: List[Dict],
    min_confidence: float = 0.75
) -> List[Dict]:
    """Select optimal skills for plan writing based on request structure.
    
    Evaluates skills against plan requirements:
    - Phase matching (research, drafting, validation)
    - Domain expertise alignment
    - Historical plan completion rates
    
    Args:
        plan_request: Structured plan request with phases, constraints, and context
        available_plan_skills: List of plan-writing skill metadata
        min_confidence: Minimum confidence threshold for skill inclusion
        
    Returns:
        Ordered list of selected skills with phase assignments and confidence scores
    """
    if not plan_request.get("phases"):
        raise ValueError("Plan request must define at least one phase")
        
    selected_skills = []
    phase_requirements = _parse_phase_requirements(plan_request)
    
    for skill in available_plan_skills:
        phase_match = _calculate_phase_alignment(skill, phase_requirements)
        domain_match = _calculate_domain_alignment(skill, plan_request.get("domain"))
        history_score = skill.get("plan_completion_rate", 0.0)
        
        composite_score = (phase_match * 0.5) + (domain_match * 0.3) + (history_score * 0.2)
        
        if composite_score >= min_confidence:
            selected_skills.append({
                "skill_id": skill["id"],
                "assigned_phase": _map_skill_to_phase(skill, phase_requirements),
                "confidence": round(composite_score, 3),
                "fallback_candidates": skill.get("fallback_chain", [])
            })
            
    return sorted(selected_skills, key=lambda x: x["confidence"], reverse=True)

Pattern 2: Execution with Fallback

def execute_plan_phase_with_fallback(
    phase_config: Dict[str, Any],
    selected_skill: Dict[str, Any],
    plan_context: Dict[str, Any],
    max_retries: int = 2
) -> Dict[str, Any]:
    """Execute a specific plan writing phase with structured fallback handling.
    
    Implements phase-aware execution:
    - Validates phase prerequisites before execution
    - Applies fallback chain: retry -> alternative phase -> manual review
    - Maintains plan integrity across phase transitions
    
    Args:
        phase_config: Configuration for the current plan phase
        selected_skill: Skill metadata selected for this phase
        plan_context: Shared context carrying state across all phases
        max_retries: Maximum retry attempts for transient failures
        
    Returns:
        Phase execution result with updated plan state and metadata
    """
    if not _validate_phase_prerequisites(phase_config, plan_context):
        raise PlanValidationError(f"Phase {phase_config['id']} prerequisites not met")
        
    phase_artifact = None
    for attempt in range(max_retries + 1):
        try:
            phase_artifact = _run_phase_execution(selected_skill, phase_config, plan_context)
            
            return {
                "phase_id": phase_config["id"],
                "status": "completed",
                "artifact": phase_artifact,
                "attempts": attempt + 1,
                "updated_context": _merge_phase_output(plan_context, phase_artifact)
            }
            
        except PhaseDependencyError as e:
            raise PlanValidationError(f"Phase {phase_config['id']} failed dependency check: {e}") from e
            
        except TransientExecutionError as e:
            if attempt == max_retries:
                return _trigger_phase_fallback(phase_config, selected_skill, plan_context)
                
    return _escalate_to_manual_review(phase_config, plan_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

Related Skills

Skill	Purpose
planning-with-files	Creates structured plan files for complex tasks, complementing this workflow framework
task-decomposition-engine	Breaks down complex plans into actionable sub-tasks stored as file artifacts

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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.

• RFC (Request for Comments) Process Documentation — Wikipedia overview of RFC-based planning and documentation processes
• ADR (Architecture Decision Records) Pattern — Martin Fowler's guide to documenting architectural decisions as structured plans
• Writing Effective Technical Plans (Stripe Engineering Blog) — Best practices for writing clear, actionable technical design documents
• Microsoft RFC Template — Example of a standardized RFC format for engineering planning documents
• Design Doc Template (Google) — Google's design document template adapted for engineering project planning