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 ```python 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 ```python 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](<https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request>) - [GitLab Merge Requests Guide](<https://docs.gitlab.com/ee/user/project/merge_requests/>) - [Conventional Commits Specification](<https://www.conventionalcommits.org/en/v1.0.0/>) - [PR Review Best Practices (Google)](<https://google.github.io/eng-practices/review/>) - [CODEOWNERS File Configuration](<https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners>) ## Related Skills | Skill | Purpose | |