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

• guidance
• examples
• do-dont description: Implements intelligent gitlab 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: gitlab-automation, gitlab automation, how do i gitlab-automation, orchestrate gitlab-automation, automate gitlab-automation, agent gitlab-automation 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"

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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

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_.-]+#x27;, 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

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

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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 | |