paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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 | |