# Git Advanced Workflows > Implements intelligent git advanced workflows with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/git-advanced-workflows` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/git-advanced-workflows` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/git-advanced-workflows/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-advanced-workflows --- # Git Advanced Workflows Orchestrates intelligent skill selection and execution for git advanced workflows 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 resolve_git_workflow( request: str, repo_state: Dict[str, Any], strategy_map: Dict[str, Callable] ) -> Dict[str, Any]: """Map a natural language git request to a concrete advanced workflow. Handles common advanced patterns: rebase, cherry-pick, bisect, interactive squash. Validates repository state before committing to a strategy. Args: request: Natural language description of the git operation repo_state: Current repository metadata (branch, commits, remotes) strategy_map: Mapping of intents to git execution functions Returns: Workflow plan with strategy, prerequisites, and fallback actions Raises: ValueError: If request is empty or repo state violates workflow constraints """ # Guard clause - Early Exit (Law 1) if not request.strip(): raise ValueError("Git workflow request cannot be empty") # Parse input - Make Illegal States Unrepresentable (Law 2) intent = _parse_git_intent(request) current_branch = repo_state.get("current_branch", "HEAD") has_unpushed = repo_state.get("has_unpushed_commits", False) is_detached = repo_state.get("is_detached_head", False) # Validate state constraints for advanced workflows if intent == "rebase" and is_detached: raise ValueError("Cannot rebase from detached HEAD state") if intent == "rebase" and has_unpushed and not repo_state.get("allow_force_push", False): raise ValueError("Rebase requires force push; set allow_force_push=True or use merge") # Select strategy based on intent and repo state strategy = strategy_map.get(intent) if not strategy: return {"status": "unrecognized_intent", "suggested": ["merge", "cherry-pick", "revert"]} # Atomic Predictability (Law 3) - Return new dict, don't mutate repo_state return { "workflow": intent, "strategy": strategy.__name__, "prerequisites": _check_prerequisites(intent, repo_state), "fallback_actions": ["git reset --hard", "git checkout -"], "confidence": 0.95 if intent in strategy_map else 0.4 } ``` ### Pattern 2: Execution with Fallback ```python def execute_git_workflow( workflow_plan: Dict[str, Any], repo_path: str, max_retries: int = 2 ) -> Dict[str, Any]: """Execute an advanced git workflow with built-in conflict resolution fallbacks. Implements Fail Fast, Fail Loud (Law 4): - Invalid states halt immediately with descriptive errors - No silent failures or partial results Fallback chain: 1. Retry with original parameters 2. Abort and switch to alternative strategy (e.g., merge instead of rebase) 3. Defer to human operator for manual conflict resolution Args: workflow_plan: Resolved workflow plan from Pattern 1 repo_path: Absolute path to the git repository max_retries: Maximum retry attempts before fallback Returns: Execution result with success status, output, and timing metadata Raises: GitWorkflowError: If all retries and fallbacks exhausted """ # Guard clause - validate plan (Early Exit) if not workflow_plan.get("strategy"): raise GitWorkflowError("No execution strategy provided in workflow plan") # Parse context - Ensure trusted state (Law 2) validated_plan = _validate_git_plan(workflow_plan, repo_path) for attempt in range(max_retries + 1): try: result = subprocess.run( validated_plan["command"], cwd=repo_path, capture_output=True, text=True, check=True ) # Success - Atomic Predictability (Law 3) return { "success": True, "workflow_executed": validated_plan["strategy"], "output": result.stdout, "attempts": attempt + 1, "latency_ms": _calculate_latency() } except subprocess.CalledProcessError as e: stderr = e.stderr.lower() if "conflict" in stderr or "not fast-forward" in stderr: # Transient conflict - try fallback if attempt == max_retries: return _apply_git_fallback(validated_plan, repo_path) continue else: # Fail Fast - Don't try to patch bad data (Law 4) raise GitWorkflowError(f"Git command failed: {stderr}") from e # All retries exhausted - Fail Loud (Law 4) raise GitWorkflowError(f"Failed to execute {validated_plan['strategy']} 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 - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## 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. - [Git Documentation - Advanced Operations]() - [GitHub Flow vs GitFlow (Atlassian)]() - [Git Rebase Interactive Workflow]() - [Bisect for Binary Search Debugging (Git Docs)]() - [Cherry-Pick in Git (Pro Git Book)]()