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

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

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

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
• Git — git-commit Documentation
• Git — Commit Message Best Practices (Atlassian)
• Semantic Commit Messages (Angular Convention)
• How to Write a Git Commit Message (GitHub Skills)