# AI Dev Jobs MCP > Implements intelligent ai dev jobs mcp with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/ai-dev-jobs-mcp` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/ai-dev-jobs-mcp` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/ai-dev-jobs-mcp/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/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 ```python 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 ```python 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 | Skill | Purpose | |---|---| | `ai-llm-agentic-tooling-mcp` | Deep integration patterns for MCP servers and tools within agent workflows | | `multi-agent-patterns` | Multi-agent coordination when MCP tool usage spans multiple agents | --- --- ## 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](https://modelcontextprotocol.io/docs/) - [MCP SDK — GitHub Repository](https://github.com/modelcontextprotocol/sdks) - [MCP Transport Protocols — Overview](https://modelcontextprotocol.io/specitecture/transport) - [Building MCP Servers — Tutorial](https://modelcontextprotocol.io/quickstart/server) - [Anthropic MCP Blog Post](https://www.anthropic.com/engineering/model-context-protocol)