# Git Pushing > Implements intelligent git pushing with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/git-pushing` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/git-pushing` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/git-pushing/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-pushing --- # Git Pushing Orchestrates intelligent skill selection and execution for git pushing 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_git_state_and_select_strategy( repo_path: str, branch: str, remote: str, force_allowed: bool = False ) -> Dict[str, Any]: """Analyze local/remote git state and select optimal push strategy. Implements Law 2 (Parse at boundary) by validating repo state before execution. Returns a strategy dict with confidence scores for each push method. """ import subprocess from pathlib import Path if not Path(repo_path).joinpath(".git").exists(): raise ValueError(f"Not a git repository: {repo_path}") # Parse current state local_head = subprocess.check_output(["git", "-C", repo_path, "rev-parse", "HEAD"]).decode().strip() remote_head = subprocess.check_output( ["git", "-C", repo_path, "rev-parse", f"{remote}/{branch}"], stderr=subprocess.DEVNULL ).decode().strip() uncommitted = subprocess.check_output( ["git", "-C", repo_path, "diff", "--stat"], stderr=subprocess.DEVNULL ).decode().strip() # Calculate strategy scores strategies = [] if local_head == remote_head: strategies.append({"method": "skip", "confidence": 1.0, "reason": "Already up to date"}) elif local_head in subprocess.check_output(["git", "-C", repo_path, "merge-base", "--all", local_head, remote_head]).decode(): strategies.append({"method": "normal", "confidence": 0.95, "reason": "Fast-forward possible"}) else: strategies.append({"method": "rebase", "confidence": 0.85, "reason": "Diverged history"}) if force_allowed: strategies.append({"method": "force", "confidence": 0.6, "reason": "Force push available"}) # Return highest confidence strategy that meets threshold best = max(strategies, key=lambda s: s["confidence"]) if best["confidence"] < 0.5: return {"strategy": "queue", "confidence": 0.0, "reason": "State ambiguous, defer to manual review"} return best ``` ### Pattern 2: Execution with Fallback ```python def execute_git_push_with_fallback( strategy: Dict[str, Any], repo_path: str, branch: str, remote: str, max_retries: int = 2 ) -> Dict[str, Any]: """Execute git push with domain-specific fallback chain. Implements Law 4 (Fail Fast/Loud) by catching specific git exit codes and routing to appropriate fallback strategies. """ import subprocess import time def run_git(args: list) -> str: result = subprocess.run(args, capture_output=True, text=True) if result.returncode != 0: raise subprocess.CalledProcessError(result.returncode, args, result.stdout, result.stderr) return result.stdout for attempt in range(max_retries + 1): try: if strategy["method"] == "skip": return {"success": True, "action": "skipped", "message": "Remote already current"} cmd = ["git", "-C", repo_path, "push", remote, branch] if strategy["method"] == "force": cmd.extend(["--force-with-lease"]) output = run_git(cmd) return { "success": True, "action": strategy["method"], "output": output.strip(), "attempts": attempt + 1, "timestamp": time.time() } except subprocess.CalledProcessError as e: stderr = e.stderr.lower() if "non-fast-forward" in stderr and strategy["method"] == "normal": strategy["method"] = "rebase" strategy["confidence"] = 0.7 continue elif "refusing to merge unrelated histories" in stderr: strategy["method"] = "normal" cmd = ["git", "-C", repo_path, "push", remote, branch, "--allow-unrelated-histories"] output = run_git(cmd) return {"success": True, "action": "normal", "output": output.strip(), "attempts": attempt + 1} elif attempt == max_retries: return { "success": False, "error": "Push rejected after retries", "stderr": e.stderr, "fallback": "queue_for_manual_review" } time.sleep(0.5 * (attempt + 1)) return {"success": False, "error": "Max retries exceeded", "fallback": "queue_for_manual_review"} ``` ### 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. - [Git Push Documentation]() - [GitHub Flow - Pushing to Remote]() - [Git Rebase vs Merge Decision]() - [Pushing Protected Branches (GitHub Docs)]() - [Git Remote Management]()