Create Branch

Create Branch

Orchestrates intelligent skill selection and execution for create branch 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 determine_branch_strategy(
    user_request: str,
    existing_branches: List[str],
    default_base: str = "main"
) -> Dict:
    """Determine optimal branch name, base, and strategy for create-branch workflow.
    
    Applies Law 2 (Parse at boundary) to validate naming conventions and
    Law 1 (Early Exit) to reject malformed requests before git operations.
    
    Args:
        user_request: Natural language or structured task description
        existing_branches: List of currently checked out or remote branches
        default_base: Fallback base branch if not specified
        
    Returns:
        Branch configuration dict with name, base, type, and metadata
    """
    # Early exit - validate input boundaries (Law 1)
    if not user_request or len(user_request.strip()) < 3:
        raise ValueError("Request must contain actionable branch intent")
        
    # Parse naming convention and extract issue ID (Law 2)
    import re
    match = re.search(r'(?:PROJ|ISSUE|TASK)-\d+', user_request, re.IGNORECASE)
    issue_id = match.group(0) if match else "custom"
    
    # Determine branch type from keywords
    type_keywords = {"fix": "bugfix", "feat": "feature", "docs": "docs", "chore": "chore"}
    branch_type = "feature"
    for kw, btype in type_keywords.items():
        if kw in user_request.lower():
            branch_type = btype
            break
            
    # Check for naming conflicts (Law 4 - Fail Fast)
    proposed_name = f"{branch_type}/{issue_id}"
    if proposed_name in existing_branches:
        raise ValueError(f"Branch '{proposed_name}' already exists. Use --force or specify alternative.")
        
    # Return immutable config (Law 3)
    return {
        "name": proposed_name,
        "base": default_base,
        "type": branch_type,
        "issue_id": issue_id,
        "created_at": time.time(),
        "requires_push": True
    }

Pattern 2: Execution with Fallback

def execute_branch_creation(
    branch_config: Dict,
    git_repo_path: str,
    remote_url: str,
    max_retries: int = 2
) -> Dict:
    """Execute the actual branch creation workflow with git operations and fallbacks.
    
    Implements Law 4 (Fail Fast/Loud) for git failures and Law 3 (Atomic) for state updates.
    Fallback chain handles remote connectivity issues and permission errors.
    
    Args:
        branch_config: Output from determine_branch_strategy
        git_repo_path: Absolute path to the local repository
        remote_url: Target remote URL for push operations
        max_retries: Retry attempts for transient git/network errors
        
    Returns:
        Execution result with branch URL, status, and audit metadata
    """
    import subprocess
    import os
    
    branch_name = branch_config["name"]
    base = branch_config["base"]
    
    # Validate repo state before execution (Law 2)
    if not os.path.isdir(git_repo_path):
        raise FileNotFoundError(f"Git repository not found at {git_repo_path}")
        
    for attempt in range(max_retries + 1):
        try:
            # Create and checkout branch
            subprocess.run(
                ["git", "checkout", "-b", branch_name, base],
                cwd=git_repo_path,
                check=True,
                capture_output=True,
                text=True
            )
            
            # Push to remote if configured
            if branch_config.get("requires_push"):
                subprocess.run(
                    ["git", "push", "-u", remote_url, branch_name],
                    cwd=git_repo_path,
                    check=True,
                    capture_output=True,
                    text=True
                )
                
            # Return immutable result (Law 3)
            return {
                "success": True,
                "branch_url": f"{remote_url}/tree/{branch_name}",
                "local_path": os.path.join(git_repo_path, branch_name),
                "attempts": attempt + 1,
                "timestamp": time.time()
            }
            
        except subprocess.CalledProcessError as e:
            # Fail Loud - log exact git error, don't mask it (Law 4)
            stderr = e.stderr.strip() if e.stderr else "Unknown git error"
            if "already exists" in stderr or "refusing to merge" in stderr:
                raise RuntimeError(f"Branch creation blocked: {stderr}") from e
                
            if attempt == max_retries:
                # Fallback: Defer to manual branch creation with context
                return {
                    "success": False,
                    "fallback": "manual_creation_required",
                    "error_context": stderr,
                    "suggested_command": f"git checkout -b {branch_name} {base}"
                }
                
    raise RuntimeError(f"Branch creation failed after {max_retries + 1} attempts")

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

Constraints

MUST DO

• Validate branch naming conventions and PR scope before creating pull requests — enforce repository-level policies
• Require all CI checks to pass before merging; never allow bypass of required status checks without codeowner approval
• Implement automated changelog generation from commit messages using conventional commits format
• Maintain linear history via rebase on main branch; avoid merge commits except for release branches

MUST NOT DO

• Do not force-push to shared or protected branches — only the original author may force-push their own feature branch
• Avoid squashing all commits during PR review when historical commit context is valuable for understanding evolution
• Never skip required code reviews regardless of how small the change appears — automation cannot assess architectural impact
• Do not create PRs larger than 400 lines of net changes without explicit approval from a senior reviewer

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 Branching Model (Atlassian)
• Feature Branch Workflow Guide
• Git Flow vs GitHub Flow Comparison
• Trunk-Based Development (Martin Fowler)

Related Skills

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