---

name: git-pushing compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent git pushing with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: git-pushing, git pushing, how do i git-pushing, orchestrate git-pushing, automate git-pushing, agent git-pushing archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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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

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

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 | |

---

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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