---

name: git-hooks-automation compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent git hooks 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: git-hooks-automation, git hooks automation, how do i git-hooks-automation, orchestrate git-hooks-automation, automate git-hooks-automation, agent git-hooks-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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Git Hooks Automation

Orchestrates intelligent skill selection and execution for git hooks 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 validate_and_install_hooks(
    hook_type: str,
    config: Dict[str, Any],
    repo_path: str
) -> Dict[str, Any]:
    """Validate and install a git hook with safety checks.
    
    Implements the 5 Laws of Elegant Defense:
    - Law 1: Early exit on invalid hook types or missing repo
    - Law 2: Parse config into immutable structures
    - Law 3: Return new hook script content without mutating original config
    - Law 4: Fail immediately if hook script contains dangerous commands
    """
    # Guard clause - Early Exit (Law 1)
    valid_hooks = {"pre-commit", "pre-push", "commit-msg", "post-commit"}
    if hook_type not in valid_hooks:
        raise ValueError(f"Unsupported hook type: {hook_type}. Must be one of {valid_hooks}")
    
    if not os.path.isdir(repo_path):
        raise FileNotFoundError(f"Repository path does not exist: {repo_path}")
    
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    hook_config = {
        "type": hook_type,
        "commands": config.get("commands", []),
        "timeout": config.get("timeout", 30),
        "allow_bypass": config.get("allow_bypass", False)
    }
    
    # Atomic Predictability (Law 3) - Generate hook script without mutating config
    hook_script = _generate_hook_script(hook_config)
    hook_path = os.path.join(repo_path, ".git", "hooks", hook_type)
    
    # Fail Fast - Validate script safety (Law 4)
    if _contains_dangerous_patterns(hook_script):
        raise SecurityError("Hook script contains potentially dangerous patterns")
    
    # Write hook and set executable
    os.makedirs(os.path.dirname(hook_path), exist_ok=True)
    with open(hook_path, "w") as f:
        f.write(hook_script)
    os.chmod(hook_path, 0o755)
    
    return {
        "hook_type": hook_type,
        "path": hook_path,
        "status": "installed",
        "commands": hook_config["commands"],
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def execute_hook_with_fallback(
    hook_path: str,
    staged_files: List[str],
    env_context: Dict[str, str],
    max_retries: int = 1
) -> Dict[str, Any]:
    """Execute a git hook with staged file validation and fallback handling.
    
    Implements the 5 Laws of Elegant Defense:
    - Law 1: Early exit if hook script is missing or not executable
    - Law 2: Parse staged files into immutable list for validation
    - Law 3: Return new result dict without mutating env_context
    - Law 4: Fail immediately on syntax errors or permission denied
    """
    # Guard clause - Early Exit (Law 1)
    if not os.path.isfile(hook_path) or not os.access(hook_path, os.X_OK):
        raise HookExecutionError(f"Hook not found or not executable: {hook_path}")
    
    # Parse context - Ensure trusted state (Law 2)
    validated_files = [os.path.abspath(f) for f in staged_files if os.path.isfile(f)]
    if not validated_files:
        return {"status": "skipped", "reason": "No valid staged files", "timestamp": time.time()}
    
    for attempt in range(max_retries + 1):
        try:
            # Execute hook with staged files passed via stdin or args
            result = subprocess.run(
                [hook_path] + validated_files,
                capture_output=True,
                text=True,
                timeout=60,
                env={**os.environ, **env_context}
            )
            
            # Success - Atomic Predictability (Law 3)
            return {
                "status": "passed",
                "exit_code": result.returncode,
                "stdout": result.stdout,
                "stderr": result.stderr,
                "attempts": attempt + 1,
                "timestamp": time.time()
            }
            
        except subprocess.TimeoutExpired:
            # Fail Fast - Don't hang indefinitely (Law 4)
            if attempt == max_retries:
                return _apply_hook_fallback(hook_path, validated_files, "timeout")
            continue
            
        except PermissionError as e:
            raise HookExecutionError(f"Permission denied executing hook: {e}") from e
    
    # All retries exhausted - Fail Loud (Law 4)
    return _apply_hook_fallback(hook_path, validated_files, "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

Related Skills

| Skill | Purpose | |

---

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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

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Git Hooks Documentation
• Husky Git Hooks for JavaScript Projects
• Pre-commit Framework (Python)
• Lint-Staged for Pre-Commit Linting
• Commitlint Conventional Commits