cc-skill-continuous-learning

# Cc Skill Continuous Learning Orchestrates intelligent skill selection and execution for cc skill continuous learning 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 update_skill_learning_metrics( skill_id: str, execution_outcome: Dict, learning_buffer: List[Dict], decay_factor: float = 0.95 ) -> Dict: """Track execution outcomes and update continuous learning state. Implements Law 3 (Atomic Predictability) by returning new state dicts. Implements Law 4 (Fail Fast) by validating outcome structure immediately. """ # Guard clause - validate outcome structure (Law 4) required_keys = {"success", "latency_ms", "confidence", "error_type"} if not required_keys.issubset(execution_outcome.keys()): raise ValueError(f"Invalid execution outcome: missing {required_keys - set(execution_outcome.keys())}") # Parse input at boundary (Law 2) new_record = { "skill_id": skill_id, "timestamp": time.time(), "success": execution_outcome["success"], "latency_ms": execution_outcome["latency_ms"], "confidence": execution_outcome["confidence"], "error_type": execution_outcome.get("error_type", "none") } # Append to learning buffer immutably updated_buffer = learning_buffer + [new_record] # Calculate rolling confidence using exponential decay recent_records = [r for r in updated_buffer if time.time() - r["timestamp"] < 3600] if not recent_records: return {"buffer": updated_buffer, "rolling_confidence": 0.0, "trigger_retrain": False} success_rate = sum(1 for r in recent_records if r["success"]) / len(recent_records) avg_latency = sum(r["latency_ms"] for r in recent_records) / len(recent_records) rolling_confidence = success_rate * (1.0 - min(avg_latency / 5000.0, 0.5)) # Determine if continuous learning trigger is met trigger_retrain = rolling_confidence < 0.6 or len(recent_records) >= 50 return { "buffer": updated_buffer, "rolling_confidence": round(rolling_confidence, 3), "trigger_retrain": trigger_retrain, "recent_sample_size": len(recent_records) } ``` ### Pattern 2: Execution with Fallback ```python def orchestrate_learning_fallback( skill_state: Dict, learning_buffer: List[Dict], fallback_skills: List[str], human_review_threshold: float = 0.4 ) -> Dict: """Orchestrate fallback chain based on continuous learning metrics. Applies Law 1 (Early Exit) and Law 4 (Fail Loud) for degraded skills. """ # Early exit if skill is already in stable state if skill_state.get("status") == "stable" and skill_state.get("rolling_confidence", 1.0) > 0.85: return {"action": "continue", "next_skill": skill_state["id"], "reason": "stable_performance"} # Parse buffer immutably (Law 2) recent_failures = [r for r in learning_buffer if not r["success"] and time.time() - r["timestamp"] < 1800] # Fail fast if critical degradation detected if len(recent_failures) >= 3: return { "action": "escalate_to_human", "reason": "critical_degradation", "failure_count": len(recent_failures), "fallback_chain": fallback_skills } # Apply adaptive fallback chain if fallback_skills: next_candidate = fallback_skills[0] return { "action": "switch_fallback", "target_skill": next_candidate, "reason": "confidence_drop", "current_confidence": skill_state.get("rolling_confidence", 0.0) } # Default: log and defer with adjusted parameters return { "action": "retry_with_adjusted_params", "reason": "transient_error", "adjustments": {"timeout_multiplier": 1.5, "retry_backoff": "exponential"} } ``` ### 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. - [Continuous Learning in ML (Google Research)](<https://research.google/pubs/continuous-learning-a-survey/>) - [Active Learning Survey Paper (arXiv)](<https://arxiv.org/abs/2302.13898>) - [Reinforcement Learning Basics (Sutton & Barto)](<http://www.incompleteideas.net/book/the-book-2nd.html>) - [Online Machine Learning Tutorial](<https://online-learning-course.github.io/>) - [Model Drift Detection Methods](<https://arxiv.org/abs/1904.02652>) ## Related Skills | Skill | Purpose | |