github-actions-templates

# Github Actions Templates Orchestrates intelligent skill selection and execution for github actions templates 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 generate_github_action_template( task_description: str, available_templates: List[Dict], min_confidence: float = 0.7 ) -> Optional[Dict]: """Generate a GitHub Actions workflow template based on task description. Selects the most appropriate base template (CI, CD, Lint, etc.) using multi-factor scoring: trigger type, language/framework, and historical usage. Applies Law 1 (Early Exit) and Law 2 (Immutable State). Args: task_description: Natural language description of the automation task available_templates: List of template metadata with triggers and languages min_confidence: Minimum match threshold for template selection Returns: Rendered workflow dict with metadata, or None if no suitable template """ if not task_description or not task_description.strip(): raise ValueError("Task description cannot be empty") if not available_templates: raise ValueError("No GitHub Actions templates available") # Extract intent features (Law 2: Parse at boundary) intent = _extract_workflow_intent(task_description) best_template = None best_score = 0.0 for tmpl in available_templates: score = _calculate_template_match(intent, tmpl) if score > best_score and score >= min_confidence: best_score = score best_template = tmpl if best_template is None: return None # Law 3: Return new structure, never mutate input template workflow = { "name": best_template["name"], "on": best_template["triggers"], "jobs": {}, "metadata": { "source_template": best_template["id"], "confidence": best_score, "generated_at": time.time() } } return workflow ``` ### Pattern 2: Execution with Fallback ```python def render_and_validate_template( workflow_template: Dict, context_vars: Dict, fallback_templates: List[Dict] = None ) -> Dict: """Render a GitHub Actions workflow with variable substitution and validation. Implements fallback chain (Law 4: Fail Fast, Fail Loud): 1. Attempt full rendering with provided context 2. Fallback to simplified template if complex variables fail 3. Defer to manual review if YAML validation fails Args: workflow_template: Base workflow structure from Pattern 1 context_vars: User-provided variables (e.g., python-version, os) fallback_templates: Alternative templates to try on failure Returns: Validated workflow YAML string with execution metadata """ if not workflow_template or "jobs" not in workflow_template: raise ValueError("Invalid workflow template structure") # Law 2: Validate context before rendering validated_vars = _sanitize_context_vars(context_vars) try: # Attempt primary render rendered_yaml = _apply_jinja2_template(workflow_template, validated_vars) _validate_github_actions_schema(rendered_yaml) return { "success": True, "yaml": rendered_yaml, "strategy": "primary", "validation": "passed" } except SchemaValidationError as e: # Law 4: Fail fast on invalid state if fallback_templates: # Fallback 1: Try simplified template variant for alt in fallback_templates: try: alt_yaml = _apply_jinja2_template(alt, validated_vars) _validate_github_actions_schema(alt_yaml) return { "success": True, "yaml": alt_yaml, "strategy": "fallback_simplified", "validation": "passed" } except SchemaValidationError: continue # Fallback 2: Return structured error for human review return { "success": False, "error": f"Template validation failed: {str(e)}", "strategy": "defer_human", "raw_context": validated_vars } ``` ### 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 | | --- --- ## 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. - [GitHub Actions Documentation](<https://docs.github.com/en/actions>) - [GitHub Actions Workflow Syntax](<https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions>) - [GitHub Actions Marketplace](<https://github.com/marketplace?type=actions>) - [Reusable Workflows (GitHub Docs)](<https://docs.github.com/en/actions/sharing-automations/reusable-workflows>) - [GitHub Actions Self-Hosted Runners](<https://docs.github.com/en/actions/hosting-your-own-runners/managing-self-hosted-runners>)