paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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.