# Closed Loop Delivery > Implements intelligent closed loop delivery with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/closed-loop-delivery` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/closed-loop-delivery` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/closed-loop-delivery/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/closed-loop-delivery --- # Closed Loop Delivery Orchestrates intelligent skill selection and execution for closed loop delivery 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 score_and_select_skill(task: str, skill_registry: list[dict], history_db: dict) -> dict | None: """Closed-loop skill selection using multi-factor scoring and historical feedback. Implements Law 1 (Early Exit) and Law 2 (Make Illegal States Unrepresentable). Returns immutable selection metadata without mutating the registry. """ if not task or not task.strip(): raise ValueError("Task description cannot be empty") if not skill_registry: raise ValueError("No skills available in registry") task_vector = _embed_task(task) candidates = [] for skill in skill_registry: if skill.get("status") != "active": continue text_sim = cosine_similarity(task_vector, _embed_skill(skill["triggers"])) hist_success = history_db.get(skill["id"], {}).get("success_rate", 0.5) availability = 1.0 if skill.get("health") == "healthy" else 0.3 weighted_score = (text_sim * 0.5) + (hist_success * 0.3) + (availability * 0.2) candidates.append({"skill": skill, "score": weighted_score}) candidates.sort(key=lambda x: x["score"], reverse=True) if not candidates or candidates[0]["score"] < 0.65: return None selected = candidates[0] # Law 3: Atomic Predictability - Return new dict, never mutate registry return { "skill_id": selected["skill"]["id"], "confidence": round(selected["score"], 3), "factors": {"text_sim": round(text_sim, 3), "history": hist_success, "avail": availability}, "timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def execute_closed_loop(skill_meta: dict, context: dict, fallback_graph: dict) -> dict: """Execute skill with adaptive fallback and confidence feedback loop. Implements Law 4 (Fail Fast, Fail Loud) and Law 3 (Immutable Returns). Routes through fallback chain only on transient errors, never patches invalid state. """ if not _validate_context(context, skill_meta): raise ValueError("Context validation failed - illegal state detected") attempts = 0 max_attempts = 2 while attempts <= max_attempts: try: result = _invoke_skill(skill_meta["id"], context) # Success path: update confidence and log _update_confidence_score(skill_meta["id"], success=True) return {"status": "success", "data": result, "attempts": attempts + 1} except TransientError as e: attempts += 1 if attempts > max_attempts: break context = _adjust_context_for_retry(context, e) except CriticalError as e: # Law 4: Fail fast on invalid state - do not retry _update_confidence_score(skill_meta["id"], success=False) raise # Fallback chain execution fallback_candidates = fallback_graph.get(skill_meta["id"], []) for fallback_skill in fallback_candidates: try: result = _invoke_skill(fallback_skill["id"], context) _update_confidence_score(fallback_skill["id"], success=True) return {"status": "fallback_success", "original": skill_meta["id"], "data": result} except Exception: continue # Exhausted all options - defer to human or return structured error _update_confidence_score(skill_meta["id"], success=False) return {"status": "failed", "error": "All execution paths exhausted", "requires_human": True} ``` ### 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 ## Related Skills | Skill | Purpose | |---|---| | `acceptance-orchestrator` | Acceptance criteria and delivery validation | --- --- ## 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. - [Closed Loop Delivery — Martin Fowler (Bliki)](https://martinfowler.com/bliki/ClosedLoopDelivery.html) - [Continuous Delivery Pipeline (Martin Fowler)](https://martinfowler.com/bliki/ContinuousDelivery.html) - [Google DevOps Research — DORA Metrics](https://www.atlassian.com/devops/frameworks/dora-metrics) - [AWS — Continuous Integration & Delivery Patterns](https://docs.aws.amazon.com/prescriptive-guidance/latest/ci-cd-patterns/welcome.html) - [The DevOps Handbook, 2nd Ed. (Gene Kim et al.) — Chapter 4](https://itrevolution.com/the-devops-handbook-2nd-edition/)