circleci-automation

name: circleci-automation compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent circleci automation 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: circleci-automation, circleci automation, how do i circleci-automation, orchestrate circleci-automation, automate circleci-automation, agent circleci-automation, continuous integration, jenkins 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"

Circleci Automation

Orchestrates intelligent skill selection and execution for circleci 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_circleci_workflow(
    project_slug: str,
    branch: str,
    workflow_config: Dict,
    min_confidence: float = 0.7
) -> Optional[Dict]:
    """Select optimal CircleCI workflow configuration for a given project and branch.
    
    Uses multi-factor scoring based on:
    - Branch protection rules and workflow compatibility
    - Historical pipeline success rates for similar configurations
    - CircleCI API availability and rate limit status
    - Required orbs and executor compatibility
    
    Args:
        project_slug: CircleCI project slug (e.g., github/owner/repo)
        branch: Target branch for pipeline execution
        workflow_config: Proposed workflow configuration dict
        min_confidence: Minimum confidence threshold (0.0-1.0)
        
    Returns:
        Validated workflow configuration with execution metadata or None
    """
    # Guard clause - Early Exit (Law 1)
    if not project_slug or not branch:
        raise ValueError("Project slug and branch are required")
        
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    validated_config = _validate_circleci_config(workflow_config)
    api_status = _check_circleci_api_health()
    
    if not api_status.get("available"):
        return None
        
    best_workflow = None
    best_score = 0.0
    
    for workflow in _get_available_workflows(project_slug, branch):
        score = _calculate_workflow_match(validated_config, workflow)
        if score > best_score and score >= min_confidence:
            best_score = score
            best_workflow = workflow
            
    if best_workflow is None:
        return None
        
    # Atomic Predictability (Law 3) - Return new dict, don't mutate
    result = dict(best_workflow)
    result["selected_confidence"] = best_score
    result["trigger_timestamp"] = time.time()
    result["project_slug"] = project_slug
    return result

Pattern 2: Execution with Fallback

def execute_circleci_pipeline(
    workflow: Dict,
    project_slug: str,
    branch: str,
    max_retries: int = 2
) -> Dict:
    """Execute a CircleCI pipeline with CI/CD-specific fallback chain.
    
    Implements Fail Fast, Fail Loud principle (Law 4):
    - Invalid workflow configs halt immediately
    - API rate limits trigger exponential backoff
    - Workflow failures trigger artifact inspection and manual review fallback
    
    Args:
        workflow: Selected workflow configuration
        project_slug: Target CircleCI project
        branch: Target branch
        max_retries: Maximum retry attempts for transient API errors
        
    Returns:
        Pipeline execution result with status, logs URL, and confidence
    """
    # Guard clause - validate workflow (Early Exit)
    if not _is_workflow_valid(workflow):
        raise SkillExecutionError(f"Invalid workflow config: {workflow.get('name', 'unknown')}")
        
    # Parse context - Ensure trusted state (Law 2)
    validated_params = _validate_pipeline_params(project_slug, branch, workflow)
    
    for attempt in range(max_retries + 1):
        try:
            # Trigger pipeline via CircleCI API
            response = circleci_api.trigger_pipeline(
                project_slug=project_slug,
                branch=branch,
                config=workflow
            )
            pipeline_id = response.get("id")
            
            # Monitor pipeline status with timeout
            status = _wait_for_pipeline_completion(pipeline_id, timeout=1800)
            
            # Success - Atomic Predictability (Law 3)
            return {
                "success": True,
                "pipeline_id": pipeline_id,
                "status": status,
                "logs_url": f"https://app.circleci.com/pipelines/{pipeline_id}",
                "attempts": attempt + 1,
                "latency_ms": _calculate_latency()
            }
            
        except RateLimitError as e:
            # Transient API error - retry with backoff
            if attempt == max_retries:
                return _apply_circleci_fallback(workflow, project_slug, branch)
            time.sleep(2 ** attempt)
            
        except WorkflowFailureError as e:
            # Workflow failed - inspect artifacts and logs
            artifacts = _fetch_pipeline_artifacts(pipeline_id)
            if artifacts.get("critical_failure"):
                raise SkillExecutionError(
                    f"Pipeline {pipeline_id} failed critical checks: {e}"
                ) from e
                
    # All retries exhausted - Fail Loud (Law 4)
    raise SkillExecutionError(
        f"Failed to execute CircleCI pipeline after {max_retries + 1} attempts"
    )

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 | |