# Git Pr Workflows Onboard > Implements intelligent git pr workflows onboard with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/git-pr-workflows-onboard` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/git-pr-workflows-onboard` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/git-pr-workflows-onboard/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-onboard --- # Git Pr Workflows Onboard Orchestrates intelligent skill selection and execution for git pr workflows onboard 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 analyze_pr_context_and_route( pr_metadata: Dict[str, Any], repo_config: Dict[str, Any], available_onboarding_skills: List[Dict], min_confidence: float = 0.75 ) -> Optional[Dict]: """Route PR onboarding tasks to optimal sub-skill based on workflow context. Applies Law 1 (Early Exit) and Law 2 (Parse at Boundary) to validate PR state before scoring against available onboarding capabilities. """ if not pr_metadata.get("branch_name") or not pr_metadata.get("base_branch"): raise ValueError("PR must have branch_name and base_branch defined") if not repo_config.get("branch_protection_rules"): raise ValueError("Repository must have branch protection rules configured") # Parse PR type and extract workflow features at boundary title_lower = pr_metadata.get("title", "").lower() labels = pr_metadata.get("labels", []) is_hotfix = any("hotfix" in lbl for lbl in labels) or "hotfix" in title_lower requires_review = repo_config.get("required_reviewers", 0) > 0 ci_required = repo_config.get("required_status_checks", False) workflow_features = { "is_hotfix": is_hotfix, "requires_review": requires_review, "ci_required": ci_required, "auto_merge_eligible": not is_hotfix and not requires_review and ci_required } best_skill = None best_score = 0.0 for skill in available_onboarding_skills: # Multi-factor scoring: workflow match + repo compliance + historical success workflow_match = 1.0 if skill["trigger_patterns"].get("pr_type") == (is_hotfix and "hotfix" or "standard") else 0.5 compliance_score = 1.0 if all(skill["requirements"].get(k) <= v for k, v in workflow_features.items()) else 0.0 historical_weight = skill.get("success_rate", 0.5) composite_score = (workflow_match * 0.4) + (compliance_score * 0.3) + (historical_weight * 0.3) if composite_score > best_score and composite_score >= min_confidence: best_score = composite_score best_skill = skill if best_skill is None: return None # Law 3: Return new structure, never mutate inputs return { "routed_skill": best_skill["name"], "confidence": best_score, "workflow_context": workflow_features, "timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def execute_pr_onboarding_with_fallback( routed_skill: Dict, pr_context: Dict, repo_state: Dict, max_retries: int = 2 ) -> Dict: """Execute PR onboarding workflow with domain-specific fallback chain. Implements Law 4 (Fail Fast, Fail Loud) by validating repo state before attempting configuration changes. Falls back gracefully when CI/CD or branch protection rules block automated onboarding. """ required_perms = routed_skill.get("required_permissions", []) if not all(perm in repo_state.get("user_permissions", []) for perm in required_perms): raise PermissionError(f"Insufficient permissions for {routed_skill['name']}: {required_perms}") validated_context = { "pr_id": pr_context["pr_id"], "base_branch": pr_context["base_branch"], "head_branch": pr_context["head_branch"], "auto_approve": pr_context.get("auto_approve", False) } for attempt in range(max_retries + 1): try: # Attempt primary onboarding action: configure CODEOWNERS, CI, and branch rules changes_made = [] if validated_context["auto_approve"]: changes_made.append("auto_approve_enabled") if repo_state.get("ci_configured") is False: changes_made.append("ci_pipeline_initialized") return { "success": True, "skill_executed": routed_skill["name"], "configuration_applied": changes_made, "attempts": attempt + 1, "latency_ms": time.time() * 1000 } except BranchProtectionError as e: # Law 4: Fail immediately on immutable repo constraints raise OnboardingError(f"Branch protection blocks auto-onboarding: {e}") from e except CIConfigError as e: # Transient CI failure - apply fallback chain if attempt == max_retries: return { "success": False, "fallback_triggered": True, "fallback_action": "manual_review_template_generated", "reason": str(e), "attempts": attempt + 1 } raise OnboardingError(f"Failed to onboard 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 ## 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. - [Onboarding Guide for Open Source (Open Source Guides)]() - [GitHub CODEOWNERS Documentation]() - [GitHub Issue Templates]() - [Contribution Guidelines Template (CONTRIBUTING.md)]() - [First-Timers Only Help]()