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name: cc-skill-continuous-learning compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent cc skill continuous learning 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-continuous-learning, cc skill continuous learning, how do i cc-skill-continuous-learning, orchestrate cc-skill-continuous-learning, automate cc-skill-continuous-learning, agent cc-skill-continuous-learning 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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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

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

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

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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 skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Continuous Learning in ML (Google Research)
• Active Learning Survey Paper (arXiv)
• Reinforcement Learning Basics (Sutton & Barto)
• Online Machine Learning Tutorial
• Model Drift Detection Methods

Related Skills

| Skill | Purpose | |