# Git Pr Workflows Pr Enhance > Implements intelligent git pr workflows pr enhance with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/git-pr-workflows-pr-enhance` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/git-pr-workflows-pr-enhance` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/git-pr-workflows-pr-enhance/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-pr-enhance --- # Git Pr Workflows Pr Enhance Orchestrates intelligent skill selection and execution for git pr workflows pr enhance 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_enhancement_candidates( pr_metadata: Dict, available_enhancers: List[Dict], min_confidence: float = 0.7 ) -> Optional[Dict]: """Evaluate PR state and select optimal enhancement strategy. Applies Law 2 by parsing PR metadata at the boundary before scoring. Scores based on diff complexity, CI pipeline status, label presence, and historical fix rates. """ if not pr_metadata or not pr_metadata.get("diff_stats"): raise ValueError("PR metadata and diff stats are required for enhancement routing") diff_lines = pr_metadata["diff_stats"]["lines_changed"] ci_status = pr_metadata.get("ci_status", "unknown") labels = set(pr_metadata.get("labels", [])) best_enhancer = None best_score = 0.0 for enhancer in available_enhancers: score = 0.0 tags = set(enhancer.get("tags", [])) if "ci-diagnose" in tags and ci_status == "failed": score += 0.4 if "refactor" in tags and diff_lines > 50: score += 0.3 if "description" in tags and not pr_metadata.get("body", "").strip(): score += 0.3 if enhancer["name"] in labels: score += 0.1 if score > best_score and score >= min_confidence: best_score = score best_enhancer = enhancer if best_enhancer is None: return None # Law 3: Return new data structure, never mutate inputs return { "selected_strategy": best_enhancer["name"], "confidence": best_score, "pr_context": { "lines_changed": diff_lines, "ci_status": ci_status, "labels": list(labels) }, "selection_timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def execute_pr_enhancement_with_fallback( strategy: Dict, pr_context: Dict, max_retries: int = 2 ) -> Dict: """Execute PR enhancement strategy with domain-specific fallback chain. Implements Law 4 (Fail Fast, Fail Loud) for invalid PR states. Fallback chain: 1. Retry with adjusted linter rules, 2. Suggest manual review, 3. Log & skip. """ if not strategy or not pr_context: raise ValueError("Strategy and PR context must be provided") strategy_name = strategy["selected_strategy"] pr_url = pr_context.get("pr_url") for attempt in range(max_retries + 1): try: if strategy_name == "ci-diagnose": result = _diagnose_ci_failure(pr_url, pr_context["ci_status"]) elif strategy_name == "refactor": result = _suggest_refactors(pr_url, pr_context["lines_changed"]) elif strategy_name == "description": result = _generate_pr_description(pr_url) else: raise ValueError(f"Unknown enhancement strategy: {strategy_name}") # Law 3: Atomic return, no mutation of original context return { "success": True, "strategy_applied": strategy_name, "enhancements": result["suggestions"], "attempts": attempt + 1, "latency_ms": _measure_execution_time() } except InvalidPRStateError as e: # Law 4: Halt immediately on corrupt/invalid PR data raise SkillExecutionError(f"Invalid PR state for {strategy_name}: {e}") from e except TransientAPILimitError as e: if attempt == max_retries: return _apply_pr_fallback_chain(strategy, pr_context) raise SkillExecutionError(f"Enhancement {strategy_name} 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 ## 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. - [GitHub PR Review Best Practices]() - [Diff Checker and Code Comparison Tools]() - [Code Review Checklist (Google Engineering)]() - [Conventional Commits Specification]() - [Pull Request Template Guidelines (GitHub Docs)]()