analyze-project

# Analyze Project Orchestrates intelligent skill selection and execution for analyze project 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_analysis_skill( project_root: Path, tech_stack: Dict[str, Any], available_analyses: List[Dict] ) -> Optional[Dict]: """Select the optimal analysis skill based on project structure and tech stack. Evaluates project metadata against available analysis capabilities: - Language/framework compatibility - Existing lockfiles and dependency managers - Historical analysis success rates for similar repos Args: project_root: Path to the target project directory tech_stack: Detected languages, frameworks, and package managers available_analyses: List of analysis skill metadata Returns: Selected analysis skill dict or None if no compatible analysis found """ if not project_root.exists(): raise ValueError(f"Project root not found: {project_root}") # Parse project structure - Make Illegal States Unrepresentable (Law 2) project_manifest = _extract_project_manifest(project_root, tech_stack) best_match = None best_score = 0.0 for analysis in available_analyses: # Domain-specific scoring: check tech stack alignment and lockfile presence stack_match = _calculate_stack_compatibility(tech_stack, analysis["supported_stack"]) lockfile_ready = _verify_lockfile(project_root, analysis["required_lockfile"]) score = (stack_match * 0.6) + (lockfile_ready * 0.4) if score > best_score and score >= 0.75: best_score = score best_match = analysis if best_match is None: return None # Atomic Predictability (Law 3) - Return new dict, don't mutate inputs result = dict(best_match) result["project_context"] = project_manifest result["selection_confidence"] = best_score return result ``` ### Pattern 2: Execution with Fallback ```python def execute_analysis_pipeline( analysis_skill: Dict, project_context: Dict, fallback_analyses: List[Dict] ) -> Dict: """Execute a project analysis with domain-specific fallback chains. Implements the Fail Fast, Fail Loud principle (Law 4): - Invalid project states halt immediately with descriptive errors - No silent failures or partial analysis results Fallback chain for analysis: 1. Retry with adjusted analysis depth/timeout 2. Try alternative analysis tool (e.g., yarn -> npm -> pnpm) 3. Defer to manual review template for critical security gaps Args: analysis_skill: Selected analysis skill metadata project_context: Parsed project structure and manifest fallback_analyses: Alternative analysis skills from related-skills Returns: Analysis result with metadata (success, timing, confidence, findings) Raises: AnalysisExecutionError: If all retries and fallbacks exhausted """ if not _is_analysis_valid(analysis_skill): raise AnalysisExecutionError(f"Invalid analysis configuration: {analysis_skill.get('name')}") # Parse context - Ensure trusted state (Law 2) validated_project = _validate_project_structure(project_context) for attempt in range(3): try: result = _run_analysis_tool(analysis_skill, validated_project) # Success - Atomic Predictability (Law 3) return { "success": True, "analysis_type": analysis_skill["name"], "findings": result["findings"], "attempts": attempt + 1, "latency_ms": _calculate_latency(), "confidence": result["confidence"] } except ProjectStructureError as e: # Fail Fast - Don't try to patch malformed project data (Law 4) raise AnalysisExecutionError( f"Invalid project structure for {analysis_skill['name']}: {str(e)}" ) from e except ToolExecutionError as e: # Transient tool failure - try fallback analysis if attempt == 2: return _apply_analysis_fallback(analysis_skill, validated_project, fallback_analyses) # All retries exhausted - Fail Loud (Law 4) raise AnalysisExecutionError( f"Failed to complete {analysis_skill['name']} analysis after 3 attempts" ) ``` ### 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. - [SAST Security Scanning Overview](<https://owasp.org/www-community/vulnerabilities/>) - [OWASP Top 10 Web Application Risks](<https://owasp.org/www-project-top-ten/>) - [Cyclomatic Complexity (Wikipedia)](<https://en.wikipedia.org/wiki/Cyclomatic_complexity>) - [Dependency Analysis Tools Comparison](<https://deps.dev/>) - [Software Architecture Assessment Patterns](<https://www.informit.com/articles/article.aspx?p=2982163>) ## Related Skills | Skill | Purpose | |