blueprint

# Blueprint Orchestrates intelligent skill selection and execution for blueprint 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: Blueprint Orchestration & Skill Routing ```python def orchestrate_blueprint_request( user_request: str, skill_registry: List[Dict], execution_history: List[Dict] ) -> Dict: """Orchestrate a blueprint workflow by routing to the optimal skill chain. Applies the 5 Laws of Elegant Defense: - Law 1: Early exit on malformed requests - Law 2: Parse inputs at boundary, make illegal states unrepresentable - Law 3: Return new routing plan, never mutate registry - Law 4: Fail fast on missing dependencies """ if not user_request or not user_request.strip(): raise ValueError("Blueprint request cannot be empty") # Law 2: Parse & validate at boundary parsed_request = _parse_blueprint_intent(user_request) if not parsed_request.get("intent") or not parsed_request.get("required_params"): raise ValueError("Missing required blueprint intent or parameters") # Law 4: Validate dependencies before scoring available_skills = [ s for s in skill_registry if _check_dependencies_met(s.get("dependencies", []), execution_history) ] # Multi-factor scoring: trigger match + historical success + availability scored_candidates = [] for skill in available_skills: trigger_match = _calculate_trigger_similarity(parsed_request["intent"], skill.get("triggers", [])) historical_success = _get_historical_success_rate(skill["name"], execution_history) availability_score = 1.0 if skill.get("status") == "healthy" else 0.5 composite_score = (trigger_match * 0.5) + (historical_success * 0.3) + (availability_score * 0.2) if composite_score >= 0.6: scored_candidates.append({ "skill": skill, "score": composite_score, "confidence": composite_score * historical_success }) if not scored_candidates: return {"status": "no_match", "fallback": "human_handoff", "reason": "No skills met threshold"} # Law 3: Return new structure, don't mutate best_match = max(scored_candidates, key=lambda x: x["score"]) return { "status": "routed", "selected_skill": best_match["skill"]["name"], "confidence": best_match["confidence"], "routing_plan": { "primary": best_match["skill"]["name"], "fallback_chain": best_match["skill"].get("fallback_skills", []), "retry_policy": best_match["skill"].get("retry_config", {"max": 2}) } } ``` ### Pattern 2: Blueprint Execution & Adaptive Fallback ```python def execute_blueprint_step( step_config: Dict, context: Dict, skill_registry: Dict[str, Callable] ) -> Dict: """Execute a blueprint workflow step with adaptive fallback and confidence tracking. Implements Fail Fast, Fail Loud (Law 4) with structured fallback chains. Updates confidence scores post-execution for adaptive routing. """ skill_name = step_config.get("primary_skill") fallback_chain = step_config.get("fallback_chain", []) max_retries = step_config.get("retry_policy", {}).get("max", 2) # Law 1: Early exit on missing skill if skill_name not in skill_registry: raise KeyError(f"Blueprint step references unknown skill: {skill_name}") execution_log = [] last_error = None for attempt in range(max_retries + 1): try: # Execute with strict input validation result = skill_registry[skill_name](context) # Law 3: Return new result structure execution_log.append({ "attempt": attempt + 1, "status": "success", "latency_ms": result.get("latency_ms", 0) }) # Update confidence for next routing decisions _update_skill_confidence(skill_name, success=True) return { "status": "completed", "skill": skill_name, "result": result.get("data"), "execution_log": execution_log } except TransientError as e: last_error = e execution_log.append({"attempt": attempt + 1, "status": "retry", "error": str(e)}) if attempt == max_retries: break except InvalidStateError as e: # Law 4: Fail immediately on invalid state _update_skill_confidence(skill_name, success=False) raise BlueprintExecutionError(f"Invalid state in {skill_name}: {e}") from e # Fallback chain execution for fallback_skill in fallback_chain: if fallback_skill in skill_registry: try: fallback_result = skill_registry[fallback_skill](context) _update_skill_confidence(fallback_skill, success=True) return { "status": "fallback_success", "original_skill": skill_name, "fallback_skill": fallback_skill, "result": fallback_result.get("data"), "execution_log": execution_log } except Exception as fb_err: execution_log.append({"fallback": fallback_skill, "status": "failed", "error": str(fb_err)}) # Law 4: Fail loud with full context _update_skill_confidence(skill_name, success=False) raise BlueprintExecutionError( f"All attempts and fallbacks exhausted for {skill_name}. " f"Last error: {last_error}. Requires human review." ) ``` ### 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. - [Architecture Decision Records (ADR Pattern)](<https://cognitect.com/blog/2011/11/15/documenting-architecture-decisions>) - [Software Blueprint Patterns (Martin Fowler)](<https://martinfowler.com/bliki/SoftwareBlueprint.html>) - [C4 Model for Software Architecture](<https://c4model.com/>) - [UML Use Case Modeling Guide](<https://www.ibm.com/docs/en/rational-warehouse-modeler/9.6.0?topic=types-use-case-diagram>) - [Design Documentation Best Practices (NIST)](<https://csrc.nist.gov/pubs/sp/800-160/vol-2/final>) ## Related Skills | Skill | Purpose | |