acceptance-orchestrator

# Acceptance Orchestrator Orchestrates intelligent skill selection and execution for acceptance orchestrator 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 evaluate_and_select_skill(task_context: dict, skill_registry: list) -> dict: """Multi-factor scoring for acceptance orchestrator selection. Implements Law 2 (Make Illegal States Unrepresentable) by validating context boundaries before scoring. Returns immutable selection metadata. """ if not task_context.get("intent"): raise ValueError("Missing intent in task context") scored_candidates = [] for skill in skill_registry: semantic_score = _compute_semantic_similarity(task_context["intent"], skill["triggers"]) history_score = skill.get("success_rate", 0.0) availability_score = 1.0 if _check_dependencies(skill["deps"]) else 0.0 composite_score = (0.5 * semantic_score) + (0.3 * history_score) + (0.2 * availability_score) if composite_score >= 0.7: scored_candidates.append({ "skill_id": skill["id"], "composite_score": round(composite_score, 3), "factors": {"semantic": semantic_score, "history": history_score, "availability": availability_score} }) if not scored_candidates: return {"status": "no_match", "fallback_triggered": True} scored_candidates.sort(key=lambda x: x["composite_score"], reverse=True) selected = scored_candidates[0] _log_selection_audit(task_context["request_id"], selected) return {"status": "selected", "skill": selected} ``` ### Pattern 2: Execution with Fallback ```python def execute_with_resilience_chain(skill_config: dict, execution_context: dict) -> dict: """Orchestrates execution with a strict fallback chain and confidence tracking. Enforces Law 4 (Fail Fast, Fail Loud) by halting on acceptance violations. Implements Law 3 (Atomic Predictability) by returning new result structures. """ max_retries = execution_context.get("max_retries", 2) current_attempt = 0 while current_attempt <= max_retries: try: result = _invoke_skill_endpoint(skill_config["endpoint"], execution_context) if not _validate_acceptance_criteria(result, skill_config["acceptance_rules"]): raise AcceptanceValidationError("Result failed acceptance criteria") confidence = _calculate_execution_confidence(result["latency_ms"], current_attempt) _update_skill_confidence(skill_config["id"], confidence) return { "status": "success", "result": result, "confidence": confidence, "attempts": current_attempt + 1 } except AcceptanceValidationError as e: _log_failure(skill_config["id"], "acceptance_violation", str(e)) raise except TransientNetworkError as e: current_attempt += 1 if current_attempt > max_retries: alt_skill = _resolve_fallback_skill(skill_config["related_skills"]) if alt_skill: skill_config = alt_skill current_attempt = 0 continue else: return _escalate_to_human(execution_context, skill_config["id"]) except Exception as e: _log_failure(skill_config["id"], "unexpected", str(e)) raise OrchestratorExecutionError(f"Failed after {current_attempt} attempts") from e ``` ### 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 | |---|---| | `closed-loop-delivery` | End-to-end delivery pipeline with feedback loops | --- --- ## 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. - [Practical Agile — Martin Fowler](https://martinfowler.com/articles/practicalAgile.html) - [Acceptance Criteria Patterns (BDD/SpecByExample)](https://www.cucumber.io/bdd/) - [Definition of Done — Scrum Guide 2020](https://scrumguides.org/scrum-guide.html) - [Behavior-Driven Development with Gherkin Syntax](https://cucumber.io/docs/gherkin/reference/) - [Acceptance Test-Driven Development (ATDD) Practices](https://www.agilealliance.org/glossary/atdd/)