gitlab-automation

# Gitlab Automation Orchestrates intelligent skill selection and execution for gitlab 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 ```python def parse_gitlab_automation_context(user_input: str, project_config: Dict) -> Dict: """Parse user request into GitLab-specific automation context. Extracts project identifiers, branch targets, and CI/CD parameters while validating against GitLab API constraints and access controls. """ import re from urllib.parse import urlparse # Guard clause - Early Exit (Law 1) if not user_input or not project_config.get("api_token"): raise ValueError("Missing GitLab project configuration or authentication token") # Parse input - Make Illegal States Unrepresentable (Law 2) url_match = re.search(r'gitlab\.com/([^/]+/[^/]+)', user_input) if not url_match: raise ValueError("Invalid GitLab project URL format") project_path = url_match.group(1) branch_match = re.search(r'--branch\s+(\S+)', user_input) target_branch = branch_match.group(1) if branch_match else project_config.get("default_branch", "main") # Validate against GitLab API constraints if not re.match(r'^[a-zA-Z0-9_.-]+$', target_branch): raise ValueError("Invalid GitLab branch name format") # Atomic Predictability (Law 3) - Return new structured context return { "project_path": project_path, "source_branch": target_branch, "api_base_url": f"https://gitlab.com/api/v4/projects/{project_path}", "auth_headers": {"PRIVATE-TOKEN": project_config["api_token"]}, "automation_type": "ci_cd_orchestration", "validation_timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def execute_gitlab_pipeline_with_fallback(context: Dict, pipeline_vars: Dict, max_retries: int = 2) -> Dict: """Execute GitLab CI/CD pipeline trigger with domain-specific fallback handling. Implements resilient GitLab API interactions: - Handles 403/401 auth failures immediately (Fail Fast) - Retries on 429 rate limits with exponential backoff - Falls back to manual MR approval if pipeline trigger fails """ import time import requests # Guard clause - validate context (Early Exit) if not context.get("api_base_url") or not context.get("auth_headers"): raise ValueError("Incomplete GitLab automation context") payload = { "ref": context["source_branch"], "variables": pipeline_vars, "commit": True } for attempt in range(max_retries + 1): try: response = requests.post( f"{context['api_base_url']}/pipeline", headers=context["auth_headers"], json=payload, timeout=30 ) # Success - Atomic Predictability (Law 3) if response.status_code == 201: return { "success": True, "pipeline_id": response.json()["id"], "status_url": response.json()["web_url"], "attempts": attempt + 1 } # Fail Fast - Invalid state or auth (Law 4) if response.status_code in (401, 403): raise PermissionError(f"GitLab API rejected access: {response.json().get('message')}") # Transient error - Retry with backoff if response.status_code == 429: wait_time = 2 ** attempt time.sleep(wait_time) continue except requests.exceptions.RequestException as e: if attempt == max_retries: return _fallback_to_manual_approval(context, pipeline_vars) time.sleep(1) # All retries exhausted - Fail Loud (Law 4) raise RuntimeError(f"GitLab pipeline trigger failed 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 | |