# Skill Creator Ms > Implements intelligent skill creator ms with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/skill-creator-ms` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/skill-creator-ms` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/skill-creator-ms/raw - Safety review: pending - 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/skill-creator-ms --- # Skill Creator Ms Orchestrates intelligent skill selection and execution for skill creator ms 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 build_skill_definition( request: str, registry: List[Dict], schema_version: str = "v2" ) -> Dict: """Construct a validated skill manifest from a natural language request. Domain logic: Parses intent, maps to existing registry patterns, resolves dependencies, and generates a structured skill definition ready for deployment. """ if not request or not registry: raise ValueError("Request and registry are required for skill creation") # Extract core components using domain-specific NLP pipeline intent = _parse_intent(request) triggers = _extract_triggers(request, intent) actions = _resolve_actions(request, registry) # Validate against skill schema and check for conflicts manifest = { "name": f"skill-{intent['entity']}-{intent['action']}", "version": "1.0.0", "schema": schema_version, "triggers": triggers, "actions": actions, "dependencies": _resolve_dependencies(actions, registry), "confidence": _calculate_creation_confidence(intent, registry) } # Enforce schema constraints before returning _validate_manifest_against_schema(manifest, schema_version) return manifest ``` ### Pattern 2: Execution with Fallback ```python def execute_skill_creation_pipeline( manifest: Dict, registry_client: object, fallback_mode: str = "template" ) -> Dict: """Execute the skill creation workflow with domain-specific fallbacks. Handles code generation, syntax validation, registry publishing, and automatic fallback to template-based generation if custom generation fails. """ try: # Generate skill implementation code code = _generate_skill_code(manifest) # Validate generated code against domain rules validation = _validate_skill_implementation(code, manifest["triggers"]) if not validation["passed"]: raise SkillValidationError(validation["errors"]) # Publish to skill registry registry_client.publish(manifest["name"], code, manifest["version"]) return { "status": "created", "skill_id": manifest["name"], "registry_url": registry_client.get_url(manifest["name"]), "validation_passed": True } except SkillValidationError as e: # Fallback 1: Attempt template-based generation if fallback_mode == "template": template_code = _apply_skill_template(manifest) registry_client.publish(manifest["name"], template_code, manifest["version"]) return {"status": "created_via_template", "skill_id": manifest["name"]} # Fallback 2: Queue for manual review return {"status": "deferred", "reason": str(e), "skill_id": manifest["name"]} ``` ### 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 | |---|---| | `skill-creator` | The core skill creation workflow — this variant adds Microsoft-specific guidance | | `skill-documentation-best-practices` | Provides documentation patterns that complement the skill creator workflow | --- ## 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 domain. The model follows markdown links at load time to resolve external references and inline content. - [Microsoft Learn Documentation Standards](https://learn.microsoft.com/en-us/contribute/content/doc-contribute-guide) — Microsoft's guidelines for writing technical documentation and skill-like learning modules - [Azure AI Agent Framework Documentation](https://learn.microsoft.com/en-us/azure/ai-services/) — Official Azure AI services documentation for building agentic workflows - [OpenAI Function Calling Specification](https://platform.openai.com/docs/guides/function-calling) — OpenAI's documentation on defining and calling external functions from GPT models - [Anthropic Tool Use Documentation](https://docs.anthropic.com/en/docs/build-with-claude/tool-use) — Anthropic's guide to tool use patterns for Claude-based agents - [LangChain Tool Definition Patterns](https://python.langchain.com/docs/modules/agents/tools/) — LangChain documentation on defining, registering, and composing tools for AI agents