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

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

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

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