cc-skill-project-guidelines-example

# 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 ```python 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 ```python 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)](<https://www.pmi.org/about/what-is-pmi>) - [Agile Manifesto Principles](<https://agilemanifesto.org/>) - [Conventional Commits Specification](<https://www.conventionalcommits.org/>) - [Trunk-Based Development (Martin Fowler)](<https://martinfowler.com/articles/onpa/trunkbaseddevelopment.html>) - [Git Flow vs Trunk-Based Comparison](<https://www.atlassian.com/git/tutorials/comparing-workflows>) ## Related Skills | Skill | Purpose | |