---

name: development compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent development 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: development, development, how do i development, orchestrate development, automate development, agent development 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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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

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

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

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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.

• PyCharm Documentation
• VS Code Documentation
• Django Web Framework Docs
• Flask Quickstart (Pallets Projects)
• Node.js Documentation

Related Skills

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