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

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

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

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