Create Pr

Create Pr

Orchestrates intelligent skill selection and execution for create pr 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_pr_payload(
    source_branch: str,
    target_branch: str,
    pr_title: str,
    pr_body: str,
    repo_config: Dict
) -> Dict:
    """Prepare and validate a Pull Request payload before submission.
    
    Validates branch existence, checks for existing open PRs,
    and formats the payload according to repository conventions.
    
    Args:
        source_branch: Feature or fix branch name
        target_branch: Base branch (e.g., main, develop)
        pr_title: Concise PR title
        pr_body: Detailed PR description with checklist
        repo_config: Repository settings including labels and templates
        
    Returns:
        Validated PR payload dictionary ready for API submission
        
    Raises:
        ValidationError: If branches are invalid or PR already exists
    """
    # Guard clause - Early Exit (Law 1)
    if not source_branch or not target_branch:
        raise ValidationError("Source and target branches are required")
        
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    sanitized_title = pr_title.strip()[:100]
    formatted_body = _apply_pr_template(pr_body, repo_config.get("template", "default"))
    
    # Check for existing open PRs to prevent duplicates
    existing_prs = _fetch_open_prs(repo_config["repo_url"], target_branch)
    for pr in existing_prs:
        if pr["head"] == source_branch and pr["state"] == "open":
            raise ValidationError(f"Open PR already exists for {source_branch}")
            
    # Atomic Predictability (Law 3) - Return new dict, don't mutate inputs
    payload = {
        "title": sanitized_title,
        "body": formatted_body,
        "head": source_branch,
        "base": target_branch,
        "labels": repo_config.get("default_labels", []),
        "maintainer_can_modify": repo_config.get("allow_fork_maintainer_edit", True)
    }
    
    # Validate required labels and reviewers
    if repo_config.get("required_reviewers"):
        payload["reviewers"] = repo_config["required_reviewers"]
        
    return payload

Pattern 2: Execution with Fallback

def execute_pr_creation(
    payload: Dict,
    github_client,
    max_retries: int = 2
) -> Dict:
    """Execute Pull Request creation with domain-specific fallback handling.
    
    Handles API rate limits, merge conflicts, and permission issues
    by implementing a targeted fallback chain for PR workflows.
    
    Args:
        payload: Validated PR payload dictionary
        github_client: Authenticated GitHub API client
        max_retries: Maximum retry attempts for transient API failures
        
    Returns:
        PR creation result with URL, status, and metadata
        
    Raises:
        PRCreationError: If all retries and fallbacks are exhausted
    """
    # Guard clause - validate payload structure (Early Exit)
    required_keys = {"title", "body", "head", "base"}
    if not required_keys.issubset(payload.keys()):
        raise PRCreationError("Missing required fields in PR payload")
        
    for attempt in range(max_retries + 1):
        try:
            # Execute PR creation via GitHub API
            response = github_client.create_pull_request(payload)
            
            # Success - Atomic Predictability (Law 3)
            return {
                "success": True,
                "pr_url": response["html_url"],
                "pr_number": response["number"],
                "status": response["state"],
                "attempts": attempt + 1,
                "created_at": response["created_at"]
            }
            
        except RateLimitExceededError as e:
            # Transient API limit - wait and retry with backoff
            if attempt == max_retries:
                return _fallback_to_manual_pr_creation(payload)
            time.sleep(2 ** attempt)
            
        except MergeConflictError as e:
            # Domain-specific conflict - adjust base branch or notify
            if attempt == max_retries:
                return _fallback_to_conflict_resolution(payload, e)
                
        except PermissionError as e:
            # Fail Fast - Don't retry permission issues (Law 4)
            raise PRCreationError(f"Permission denied for PR creation: {str(e)}") from e
            
    # All retries exhausted - Fail Loud (Law 4)
    raise PRCreationError(
        f"Failed to create PR 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.

• GitHub Pull Requests Documentation
• GitLab Merge Requests Guide
• Conventional Commits Specification
• PR Review Best Practices (Google)
• CODEOWNERS File Configuration

Related Skills

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