---

name: cc-skill-project-guidelines-example compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent cc skill project guidelines example 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: cc-skill-project-guidelines-example, cc skill project guidelines example, how do i cc-skill-project-guidelines-example, orchestrate cc-skill-project-guidelines-example, automate cc-skill-project-guidelines-example, agent cc-skill-project-guidelines-example 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"

---

Cc Skill Project Guidelines Example

Orchestrates intelligent skill selection and execution for cc skill project guidelines example 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 build_execution_plan(
    user_request: str,
    skill_registry: Dict[str, Dict],
    confidence_threshold: float = 0.75
) -> Dict:
    """Construct an ordered execution plan by scoring skills against request features.
    
    Applies Law 2 (Parse at boundary) and Law 1 (Early Exit) to ensure
    only valid, high-confidence skills enter the pipeline.
    """
    if not user_request or not skill_registry:
        raise ValueError("Request and registry must be non-empty")
        
    # Parse request into structured features at boundary
    features = parse_request_features(user_request)
    
    scored_candidates = []
    for skill_id, metadata in skill_registry.items():
        # Calculate multi-factor score
        text_match = cosine_similarity(features["intent"], metadata["triggers"])
        history_score = metadata.get("success_rate", 0.5)
        availability = 1.0 if metadata.get("status") == "healthy" else 0.0
        
        composite_score = (text_match * 0.5) + (history_score * 0.3) + (availability * 0.2)
        
        if composite_score >= confidence_threshold:
            scored_candidates.append({
                "skill_id": skill_id,
                "score": composite_score,
                "dependencies": metadata.get("requires", []),
                "fallback_targets": metadata.get("fallback_chain", [])
            })
            
    # Sort by score descending and validate dependency graph
    scored_candidates.sort(key=lambda x: x["score"], reverse=True)
    validated_plan = validate_dependency_chain(scored_candidates)
    
    return {
        "plan_id": generate_uuid(),
        "steps": validated_plan,
        "timestamp": time.time(),
        "confidence_threshold_applied": confidence_threshold
    }

Pattern 2: Execution with Fallback

def execute_step_with_resilience(
    step: Dict,
    execution_context: Dict,
    max_retries: int = 2,
    fallback_registry: Dict[str, List[str]] = None
) -> Dict:
    """Execute a single orchestration step with automatic retry and fallback routing.
    
    Implements Law 4 (Fail Fast/Loud) and Law 3 (Atomic Predictability) by
    ensuring state transitions are clean and failures are explicitly handled.
    """
    step_id = step["skill_id"]
    context = validate_context(execution_context, step)
    
    for attempt in range(max_retries + 1):
        try:
            # Invoke the actual skill implementation
            result = invoke_skill(step_id, context)
            
            # Update confidence metrics atomically
            update_skill_metrics(step_id, success=True, latency=result["latency_ms"])
            
            return {
                "step_id": step_id,
                "status": "completed",
                "result": result["output"],
                "attempts": attempt + 1,
                "confidence_updated": True
            }
            
        except DependencyError as e:
            # Fail fast on missing dependencies
            raise OrchestratorError(f"Dependency failure for {step_id}: {e}") from e
            
        except TransientFailure as e:
            if attempt < max_retries:
                continue
            # Apply fallback chain
            fallback_targets = step.get("fallback_targets", [])
            if fallback_targets:
                return execute_step_with_resilience(
                    {"skill_id": fallback_targets[0], "score": 0.0},
                    context,
                    max_retries=0
                )
            
    # All retries exhausted - Fail loud
    update_skill_metrics(step_id, success=False)
    raise OrchestratorError(f"Step {step_id} exhausted all retries and fallbacks")

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.

• Software Project Management (PMI)
• Agile Manifesto Principles
• Conventional Commits Specification
• Trunk-Based Development (Martin Fowler)
• Git Flow vs Trunk-Based Comparison

Related Skills

| Skill | Purpose | |