- name
- create-branch
- compatibility
- opencode
- completeness
- 95
- content-types
- ["guidance","examples","do-dont"]
- description
- Implements intelligent create branch with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense
- license
- MIT
- maturity
- stable
- metadata
- {"domain":"agent","output-format":"analysis","related-skills":"agent-confidence-based-selector, agent-task-routing","role":"orchestration","scope":"orchestration","triggers":"create-branch, create branch, how do i create-branch, orchestrate create-branch, automate create-branch, agent create-branch","archetypes":["orchestration","strategic"],"anti_triggers":["brainstorming","vague ideation","single-agent monolith"],"response_profile":{"verbosity":"medium","directive_strength":"high","abstraction_level":"tactical"}}
- version
- 1.0.0
# 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)](<https://www.atlassian.com/git/tutorials/comparing-workflows>)
- [Feature Branch Workflow Guide](<https://docs.github.com/en/get-started/using-github/github-flow>)
- [Git Flow vs GitHub Flow Comparison](<https://nvie.com/posts/a-successful-git-branching-model/>)
- [Trunk-Based Development (Martin Fowler)](<https://martinfowler.com/articles/onpa/trunkbaseddevelopment.html>)
## Related Skills
| Skill | Purpose |
|
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