---

name: analyze-project compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent analyze project with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: analyze-project, analyze project, how do i analyze-project, orchestrate analyze-project, automate analyze-project, agent analyze-project archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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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

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

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

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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
• OWASP Top 10 Web Application Risks
• Cyclomatic Complexity (Wikipedia)
• Dependency Analysis Tools Comparison
• Software Architecture Assessment Patterns

Related Skills

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