# Create Branch > Implements intelligent create branch with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/create-branch` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/create-branch` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/create-branch/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/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 ```python 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 ```python 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 | Skill | Purpose | |