development

# Development Orchestrates intelligent skill selection and execution for development 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 select_development_skill( dev_task: Dict[str, Any], codebase_context: Dict[str, Any], registry: List[Dict[str, Any]], min_confidence: float = 0.75 ) -> Optional[Dict[str, Any]]: """Orchestrate skill selection for development workflows. Evaluates available dev tools (linters, test runners, refactors, API generators) against the task spec and codebase state using multi-factor scoring. """ # Guard clause - Early Exit (Law 1) if not dev_task.get("type") or not codebase_context.get("repo_root"): raise ValueError("Incomplete development context provided") task_features = _extract_dev_features(dev_task, codebase_context) scored_candidates = [] for skill in registry: if skill.get("status") != "active": continue match_score = _calculate_dev_match(task_features, skill) history_score = _get_historical_dev_success_rate(skill["id"]) availability_score = _check_dev_tool_health(skill["id"]) composite = (match_score * 0.5) + (history_score * 0.3) + (availability_score * 0.2) if composite >= min_confidence: scored_candidates.append({ "skill": skill, "score": composite, "factors": {"match": match_score, "history": history_score, "health": availability_score} }) if not scored_candidates: return None scored_candidates.sort(key=lambda x: x["score"], reverse=True) selected = scored_candidates[0] # Atomic Predictability (Law 3) - Return fresh context, never mutate inputs return { "selected_skill": selected["skill"], "confidence": selected["score"], "execution_context": {**codebase_context, "task_id": dev_task.get("id")}, "timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def execute_dev_workflow( selection: Dict[str, Any], task_spec: Dict[str, Any], fallback_registry: List[Dict[str, Any]] ) -> Dict[str, Any]: """Execute development task with domain-specific fallback chains. Implements resilient execution for code generation, testing, and refactoring. Falls back to alternative tools or manual review gates on failure. """ skill_meta = selection["selected_skill"] context = selection["execution_context"] # Parse context - Ensure trusted state (Law 2) if not _validate_dev_prerequisites(skill_meta, context): raise DevOrchestrationError(f"Missing prerequisites for {skill_meta['name']}") attempts = 0 max_attempts = 2 while attempts <= max_attempts: try: result = _run_dev_tool(skill_meta, context, task_spec) # Validate output before returning if not _validate_dev_output(result, skill_meta["output_schema"]): raise DevOrchestrationError("Tool produced invalid output schema") return { "status": "success", "skill": skill_meta["name"], "artifacts": result.get("artifacts", []), "metrics": {"attempts": attempts + 1, "latency_ms": time.time() - selection["timestamp"]} } except DevToolTimeoutError: attempts += 1 if attempts > max_attempts: break continue except DevValidationError as e: # Fail Fast - Don't try to patch corrupted code state (Law 4) raise DevOrchestrationError(f"Validation failed at attempt {attempts}: {e}") from e # Fallback Chain: Try alternative dev tool, then manual review gate fallback_skill = _find_alternative_dev_tool(skill_meta, fallback_registry) if fallback_skill: return execute_dev_workflow({ "selected_skill": fallback_skill, "execution_context": context, "timestamp": time.time() }, task_spec, fallback_registry) # Critical path fallback: Defer to human developer return { "status": "deferred", "reason": "All automated dev tools exhausted", "ticket": _create_dev_ticket(task_spec, context), "assigned_to": "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. - [PyCharm Documentation](<https://www.jetbrains.com/help/pycharm/>) - [VS Code Documentation](<https://code.visualstudio.com/docs>) - [Django Web Framework Docs](<https://docs.djangoproject.com/>) - [Flask Quickstart (Pallets Projects)](<https://flask.palletsprojects.com/en/latest/quickstart/>) - [Node.js Documentation](<https://nodejs.org/en/docs/>) ## Related Skills | Skill | Purpose | |