---

name: github-actions-templates compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent github actions templates with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: github-actions-templates, github actions templates, how do i github-actions-templates, orchestrate github-actions-templates, automate github-actions-templates, agent github-actions-templates archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

---

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

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

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 | |

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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
• GitHub Actions Workflow Syntax
• GitHub Actions Marketplace
• Reusable Workflows (GitHub Docs)
• GitHub Actions Self-Hosted Runners