# Github Workflow Automation > Implements intelligent github workflow automation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/github-workflow-automation` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/github-workflow-automation` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/github-workflow-automation/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/github-workflow-automation --- # Github Workflow Automation Orchestrates intelligent skill selection and execution for github workflow automation 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 select_and_prepare_workflow_run( repo_owner: str, repo_name: str, workflow_id: str, branch: str, inputs: Dict[str, Any], github_client: GitHubClient ) -> Dict[str, Any]: """Select optimal workflow dispatch parameters and validate against repository constraints. Implements Law 2 (Parse at boundary) by validating branch existence and workflow configuration. Implements Law 1 (Early Exit) by checking repo permissions and workflow status immediately. """ # Early exit: validate repository access and workflow existence try: workflow = github_client.get_workflow(repo_owner, repo_name, workflow_id) if workflow.get("state") != "active": raise ValueError(f"Workflow {workflow_id} is not active in {repo_owner}/{repo_name}") except RepositoryNotFoundError: raise ValueError(f"Repository {repo_owner}/{repo_name} not found or inaccessible") # Parse and validate branch inputs (Law 2) if not branch or not branch.strip(): raise ValueError("Target branch must be specified for workflow dispatch") # Check branch protection rules and required status checks branch_protection = github_client.get_branch_protection(repo_owner, repo_name, branch) if branch_protection.get("required_status_checks"): required_checks = branch_protection["required_status_checks"]["strict"] if not required_checks: raise ValueError(f"Branch {branch} requires strict status checks before workflow dispatch") # Construct dispatch payload with validated inputs dispatch_payload = { "ref": branch, "inputs": {k: str(v) for k, v in inputs.items()} } # Return new structure (Law 3) return { "workflow_id": workflow_id, "dispatch_payload": dispatch_payload, "validation_timestamp": time.time(), "branch_protected": branch_protection.get("protected", False) } ``` ### Pattern 2: Execution with Fallback ```python def execute_workflow_with_fallback( repo_owner: str, repo_name: str, workflow_id: str, dispatch_payload: Dict[str, Any], fallback_workflow_id: Optional[str] = None, max_retries: int = 2 ) -> Dict[str, Any]: """Execute GitHub Actions workflow dispatch with resilient fallback chain. Implements Law 4 (Fail Fast/Loud) by immediately raising on invalid payloads. Implements fallback chain: retry dispatch -> fallback workflow -> manual alert. """ # Guard clause: validate payload structure if "ref" not in dispatch_payload or "inputs" not in dispatch_payload: raise ValueError("Dispatch payload must contain 'ref' and 'inputs' keys") last_exception = None for attempt in range(max_retries + 1): try: # Execute primary workflow dispatch response = github_client.create_workflow_dispatch( repo_owner, repo_name, workflow_id, dispatch_payload ) # Verify run was created successfully if response.status_code == 201: run_id = response.json().get("id") return { "success": True, "run_id": run_id, "workflow_id": workflow_id, "attempts": attempt + 1, "status_url": f"https://github.com/{repo_owner}/{repo_name}/actions/runs/{run_id}" } else: raise RuntimeError(f"Unexpected status code: {response.status_code}") except RateLimitExceededError: last_exception = sys.exc_info() if attempt < max_retries: time.sleep(2 ** attempt) continue except TransientAPIError as e: last_exception = sys.exc_info() if attempt < max_retries: continue break # Fallback chain: try alternative workflow if primary exhausted if fallback_workflow_id and attempt == max_retries: try: fallback_payload = {**dispatch_payload, "inputs": {**dispatch_payload["inputs"], "fallback": "true"}} fallback_response = github_client.create_workflow_dispatch( repo_owner, repo_name, fallback_workflow_id, fallback_payload ) if fallback_response.status_code == 201: return { "success": True, "run_id": fallback_response.json().get("id"), "workflow_id": fallback_workflow_id, "fallback_triggered": True } except Exception as fallback_err: last_exception = fallback_err # Fail Loud: raise comprehensive error after all attempts raise WorkflowExecutionError( f"Failed to dispatch workflow {workflow_id} after {max_retries + 1} attempts. " f"Last error: {last_exception}" ) ``` ### 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 --- ## Constraints ### MUST DO - Implement idempotent automation triggers: running the same automation twice should not create duplicate resources or actions - Validate all trigger conditions with explicit allowlists before executing automated actions - Include rollback procedures in every automation workflow — every CREATE should have a corresponding DELETE capability - Log all automation executions with input state, output state, duration, and any errors for monitoring and debugging ### MUST NOT DO - Do not create circular automation loops where trigger A causes action B which triggers A again - Avoid using automations that modify production data without explicit human approval gates - Never embed API keys or credentials directly in automation workflows — use vaulted secrets with rotation - Do not assume external service availability; implement retry logic with exponential backoff and dead-letter queues ## Related Skills | Skill | Purpose | |