paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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.