# Commit > Implements intelligent commit with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/commit` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/commit` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/commit/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/commit --- # Commit Orchestrates intelligent skill selection and execution for commit 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 prepare_commit_payload( staged_files: List[str], user_intent: str, commit_rules: Dict ) -> Dict: """Analyze staged changes and generate a validated commit payload. Implements Law 2 (Parse at boundary) by validating file paths and intent. Implements Law 3 (Atomic Predictability) by returning a new payload dict. """ if not staged_files: raise ValueError("No files staged for commit") # Extract change types and scope change_types = _classify_changes(staged_files) scope = _determine_scope(staged_files, commit_rules.get("scope_rules")) # Generate conventional commit message based on intent and changes type_map = {"feat": "feat", "fix": "fix", "refactor": "refactor", "chore": "chore"} commit_type = type_map.get(change_types.get("primary", "chore"), "chore") description = _generate_description(user_intent, change_types) body = _extract_affected_components(staged_files) # Validate against commit rules (Law 4: Fail Fast) if len(description) > 72: raise ValueError("Commit description exceeds 72 characters") if not commit_rules.get("allow_empty", False) and change_types.get("lines_changed", 0) == 0: raise ValueError("Commit would be empty") return { "type": commit_type, "scope": scope, "description": description, "body": body, "files": staged_files, "timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def execute_commit_workflow( commit_payload: Dict, repo_path: str, fallback_strategy: str = "interactive" ) -> Dict: """Execute git commit with domain-specific fallback chain. Implements Law 1 (Early Exit) and Law 4 (Fail Loud) for git operations. Fallback chain: amend -> interactive staging -> manual patch review. """ if not commit_payload.get("description"): raise ValueError("Commit payload missing description") commit_cmd = f"git -C {repo_path} commit -m \"{commit_payload['type']}: {commit_payload['description']}\"" if commit_payload.get("body"): commit_cmd += f"\n\n{commit_payload['body']}" try: # Attempt direct commit result = subprocess.run(commit_cmd, shell=True, capture_output=True, text=True, check=True) return { "success": True, "commit_hash": result.stdout.strip(), "strategy": "direct", "attempts": 1 } except subprocess.CalledProcessError as e: stderr = e.stderr.lower() # Fallback 1: Pre-commit hook failure or linting issue if "pre-commit" in stderr or "lint" in stderr: return _apply_hook_fallback(repo_path, commit_payload) # Fallback 2: Empty commit or no changes detected if "nothing added" in stderr or "no changes" in stderr: return _apply_amend_fallback(repo_path, commit_payload) # Fallback 3: Conflict or merge state if "conflict" in stderr or "merge" in stderr: return _apply_interactive_fallback(repo_path, commit_payload, fallback_strategy) # Fail Loud: Unhandled git error raise GitCommitError(f"Unhandled commit failure: {stderr}") from e ``` ### 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 | |---|---| | `changelog-automation` | Changelog generation from commit history | --- --- ## 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 ## 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. - [Conventional Commits Specification v1.0.0](https://www.conventionalcommits.org/en/v1.0.0/) - [Git — git-commit Documentation](https://git-scm.com/docs/git-commit) - [Git — Commit Message Best Practices (Atlassian)](https://www.atlassian.com/git/tutorials/comitting-changes) - [Semantic Commit Messages (Angular Convention)](https://gist.github.com/joshbuchea/6f47e86d2510bce28f8e7f42ae84c716) - [How to Write a Git Commit Message (GitHub Skills)](https://docs.github.com/en/get-started/using-github/github-flow)