# Git Pr Workflows Git Workflow > Implements intelligent git pr workflows git workflow with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/git-pr-workflows-git-workflow` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/git-pr-workflows-git-workflow` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/git-pr-workflows-git-workflow/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/git-pr-workflows-git-workflow --- # Git Pr Workflows Git Workflow Orchestrates intelligent skill selection and execution for git pr workflows git workflow 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 evaluate_pr_workflow_strategy( branch_name: str, target_branch: str, ci_status: str, review_requirements: List[str] ) -> Dict: """Evaluate the optimal Git PR workflow strategy based on current state. Applies Law 2 (Make Illegal States Unrepresentable) by validating branch states and CI results before selecting a workflow path. Args: branch_name: Source branch for the PR target_branch: Destination branch ci_status: Current CI pipeline status (passing, failing, pending) review_requirements: List of required reviewers or approval rules Returns: Strategy dict with workflow type, required actions, and confidence """ # Guard clause - Early Exit (Law 1) if not branch_name or not target_branch: raise ValueError("Both source and target branches must be specified") # Parse input - Make Illegal States Unrepresentable (Law 2) workflow_state = _analyze_git_state(branch_name, target_branch) if ci_status == "failing": strategy = { "workflow_type": "fix_and_retest", "priority": "high", "confidence": 0.95, "next_action": "run_local_checks_and_push_fix" } elif "required_reviewer" in review_requirements and not workflow_state.get("approved"): strategy = { "workflow_type": "await_review", "priority": "medium", "confidence": 0.85, "next_action": "request_reviews_and_wait" } else: strategy = { "workflow_type": "auto_merge", "priority": "low", "confidence": 0.90, "next_action": "execute_merge_with_checks" } # Atomic Predictability (Law 3) - Return new dict, don't mutate inputs strategy["evaluated_at"] = time.time() strategy["branch_context"] = dict(workflow_state) return strategy ``` ### Pattern 2: Execution with Fallback ```python def execute_pr_workflow_step( strategy: Dict, repo_context: Dict, max_retries: int = 2 ) -> Dict: """Execute a Git PR workflow step with resilience and fallback chains. Implements Fail Fast, Fail Loud (Law 4) for git operations: - Invalid branch states halt immediately - CI failures trigger local validation fallback - Merge blocks trigger manual review escalation Args: strategy: Evaluated workflow strategy from evaluate_pr_workflow_strategy repo_context: Repository configuration and authentication context max_retries: Maximum retry attempts for transient git/CI errors Returns: Execution result with PR URL, status, and audit metadata """ # Guard clause - validate repo context (Early Exit) if not _is_repo_accessible(repo_context): raise GitWorkflowError("Repository is inaccessible or credentials invalid") workflow_type = strategy.get("workflow_type") result = {"success": False, "workflow_type": workflow_type} for attempt in range(max_retries + 1): try: if workflow_type == "fix_and_retest": pr_data = _push_fix_and_update_pr(repo_context, strategy) elif workflow_type == "await_review": pr_data = _request_reviews_and_monitor(repo_context, strategy) else: pr_data = _execute_safe_merge(repo_context, strategy) # Success - Atomic Predictability (Law 3) result.update({ "success": True, "pr_url": pr_data.get("url"), "status": pr_data.get("state"), "attempts": attempt + 1, "latency_ms": _calculate_latency() }) break except CIExecutionError as e: # Fail Fast - Don't proceed with failing CI (Law 4) if attempt == max_retries: return _apply_ci_fallback(repo_context, strategy, e) continue except MergeConflictError as e: # Transient conflict - try rebase fallback if attempt == max_retries: return _apply_merge_fallback(repo_context, strategy, e) continue if not result["success"]: raise GitWorkflowError(f"Workflow '{workflow_type}' exhausted retries") return result ``` ### 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 ## Related Skills | Skill | Purpose | | --- --- ## 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. - [Pull Request Workflows (Pro Git Book Ch. 3)]() - [GitHub Pull Requests Documentation]() - [GitLab Merge Request Guidelines]() - [Branch Protection Rules (GitHub Docs)]() - [Code Review Best Practices (Google Engineering Practices)]()