gitlab-ci-patterns

# Gitlab Ci Patterns Orchestrates intelligent skill selection and execution for gitlab ci patterns 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 analyze_gitlab_ci_patterns(config_path: str, pipeline_context: Dict) -> Dict: """Analyze a GitLab CI configuration and recommend optimal patterns. Evaluates job dependencies, runner requirements, caching strategies, and matrix builds to select the most efficient CI pattern. Args: config_path: Path to .gitlab-ci.yml or CI config string pipeline_context: Dict containing project_id, branch, and trigger_type Returns: Dict with recommended patterns, job graph, and optimization hints """ import yaml from pathlib import Path # Parse CI config safely if Path(config_path).exists(): with open(config_path, 'r') as f: ci_config = yaml.safe_load(f) else: ci_config = yaml.safe_load(config_path) if not ci_config or 'stages' not in ci_config: raise ValueError("Invalid GitLab CI config: missing 'stages' definition") stages = ci_config['stages'] jobs = ci_config.get('default', {}).get('services', []) job_graph = {} patterns_applied = [] # Analyze job dependencies and apply patterns for stage_idx, stage in enumerate(stages): stage_jobs = [j for j in ci_config.get('jobs', []) if j.get('stage') == stage] for job in stage_jobs: job_name = job['name'] needs = job.get('needs', []) job_graph[job_name] = { 'stage': stage, 'dependencies': needs, 'runner_type': job.get('tags', ['docker']), 'cache_key': job.get('cache', {}).get('key', None) } # Pattern: Matrix Build Detection if 'variables' in job and 'matrix' in job['variables']: patterns_applied.append('matrix_build') # Pattern: Cache Optimization if job.get('cache'): patterns_applied.append('artifact_cache') # Fallback: If no patterns detected, suggest standard pipeline structure if not patterns_applied: patterns_applied.append('standard_pipeline') return { 'job_graph': job_graph, 'recommended_patterns': list(set(patterns_applied)), 'pipeline_context': pipeline_context, 'validation_status': 'valid' } ``` ### Pattern 2: Execution with Fallback ```python def execute_gitlab_pipeline(config: Dict, fallback_strategy: str = 'retry_with_cache') -> Dict: """Execute a GitLab CI pipeline with intelligent fallback handling. Runs the validated CI configuration against GitLab's API, monitors runner availability, and applies fallback strategies on failure. Args: config: Validated CI config dict from analyze_gitlab_ci_patterns fallback_strategy: Strategy to use on runner/pipeline failure Returns: Dict with pipeline_id, status, logs, and fallback metadata """ import requests import time gitlab_url = config.get('gitlab_url', 'https://gitlab.com') project_id = config['pipeline_context']['project_id'] token = config.get('api_token') if not token: raise ValueError("GitLab API token required for pipeline execution") headers = {'PRIVATE-TOKEN': token} payload = { 'ref': config['pipeline_context'].get('branch', 'main'), 'variables': [{'key': 'CI_PATTERN', 'value': config['recommended_patterns'][0]}] } max_attempts = 3 for attempt in range(max_attempts): try: # Trigger pipeline resp = requests.post(f'{gitlab_url}/api/v4/projects/{project_id}/pipeline', json=payload, headers=headers) resp.raise_for_status() pipeline_id = resp.json()['id'] # Monitor pipeline status status = 'pending' while status in ('pending', 'running'): time.sleep(5) status_resp = requests.get(f'{gitlab_url}/api/v4/projects/{project_id}/pipelines/{pipeline_id}', headers=headers) status = status_resp.json()['status'] return { 'pipeline_id': pipeline_id, 'status': status, 'patterns_applied': config['recommended_patterns'], 'attempts': attempt + 1, 'fallback_used': False } except requests.exceptions.ConnectionError: if attempt == max_attempts - 1: return _apply_gitlab_fallback(config, fallback_strategy) time.sleep(2 ** attempt) return {'status': 'failed', 'error': 'Max retries exceeded'} ``` ### 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 - 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 ## Related Skills | Skill | Purpose | |