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name: git-advanced-workflows compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent git advanced workflows 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-advanced-workflows, git advanced workflows, how do i git-advanced-workflows, orchestrate git-advanced-workflows, automate git-advanced-workflows, agent git-advanced-workflows 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 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

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

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 | |

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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)