AI Dev Jobs MCP

Ai Dev Jobs Mcp

Orchestrates intelligent skill selection and execution for ai dev jobs mcp 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 resolve_ai_dev_job_skill(
    mcp_job_request: Dict[str, Any],
    available_mcp_tools: List[Dict[str, Any]],
    min_confidence: float = 0.75
) -> Optional[Dict[str, Any]]:
    """Resolve the optimal MCP tool for an AI development job request.
    
    Analyzes job requirements (code_gen, test_runner, deployer, etc.) against
    available MCP tool capabilities, historical success rates, and current
    system load to select the best fit.
    
    Args:
        mcp_job_request: Parsed MCP job payload with 'task_type', 'repo_context', 'constraints'
        available_mcp_tools: List of registered MCP tool definitions
        min_confidence: Minimum capability match threshold
        
    Returns:
        Selected MCP tool dict with resolved parameters, or None
    """
    if not mcp_job_request.get("task_type"):
        raise ValueError("MCP job request missing required 'task_type' field")
        
    job_spec = _parse_mcp_job_spec(mcp_job_request)
    best_match = None
    best_score = 0.0
    
    for tool in available_mcp_tools:
        capability_match = _calculate_capability_overlap(job_spec.required_capabilities, tool.capabilities)
        historical_success = tool.get("success_rate_30d", 0.0)
        load_penalty = 1.0 - (tool.get("current_queue_depth", 0) / tool.get("max_concurrent", 10))
        
        score = (capability_match * 0.5) + (historical_success * 0.3) + (load_penalty * 0.2)
        
        if score > best_score and score >= min_confidence:
            best_score = score
            best_match = {
                "tool_id": tool["id"],
                "tool_name": tool["name"],
                "resolved_params": _bind_job_to_tool_params(job_spec, tool),
                "confidence": score,
                "estimated_latency_ms": tool.get("avg_execution_ms", 5000)
            }
            
    if best_match is None:
        return None
        
    return best_match

Pattern 2: Execution with Fallback

def run_ai_dev_job_with_mcp_fallback(
    selected_tool: Dict[str, Any],
    job_context: Dict[str, Any],
    max_retries: int = 2
) -> Dict[str, Any]:
    """Execute an AI dev job via MCP with domain-specific fallback handling.
    
    Handles MCP-specific failure modes: context window limits, rate limits,
    tool unavailability, and model degradation. Implements a 3-tier fallback:
    1. Retry with reduced context window
    2. Switch to fallback tool (e.g., from related-skills)
    3. Queue for async processing if sync timeout exceeded
    
    Args:
        selected_tool: Output from resolve_ai_dev_job_skill
        job_context: Full job execution context including repo state
        max_retries: Maximum synchronous retry attempts
        
    Returns:
        Job execution result with MCP trace ID, timing, and confidence
    """
    if not selected_tool.get("tool_id"):
        raise MCPJobError("Cannot execute job: no valid tool resolved")
        
    execution_params = selected_tool["resolved_params"]
    trace_id = f"mcp-job-{uuid4().hex[:8]}"
    
    for attempt in range(max_retries + 1):
        try:
            result = await _invoke_mcp_tool(
                tool_id=selected_tool["tool_id"],
                params=execution_params,
                context_window=job_context.get("context_window", 8192)
            )
            
            return {
                "status": "completed",
                "trace_id": trace_id,
                "tool_executed": selected_tool["tool_name"],
                "output": result,
                "attempts": attempt + 1,
                "confidence": selected_tool["confidence"],
                "latency_ms": time.time_ns() // 1_000_000 - job_context.get("start_time_ms", 0)
            }
            
        except ContextWindowExceededError:
            execution_params["context_window"] = int(execution_params.get("context_window", 8192) * 0.75)
            continue
            
        except RateLimitExceededError:
            if attempt == max_retries:
                return await _queue_job_for_async_processing(selected_tool, job_context)
            await asyncio.sleep(2 ** attempt)
            continue
            
        except ToolNotFoundError:
            return await _switch_to_related_skill(selected_tool, job_context)
            
    raise MCPJobError(f"Job {trace_id} failed after {max_retries + 1} 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

Related Skills

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.

• Model Context Protocol — Official Documentation
• MCP SDK — GitHub Repository
• MCP Transport Protocols — Overview
• Building MCP Servers — Tutorial
• Anthropic MCP Blog Post