# Git Hooks Automation > Implements intelligent git hooks automation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/git-hooks-automation` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/git-hooks-automation` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/git-hooks-automation/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/git-hooks-automation --- # 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 ```python 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 ```python 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 | | --- --- ## 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]()