paulpas Git Pr Workflows Git Workflow
Orchestrates intelligent skill selection and execution for git pr workflows git workflow 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 evaluate_pr_workflow_strategy(
branch_name: str,
target_branch: str,
ci_status: str,
review_requirements: List[str]
) -> Dict:
"""Evaluate the optimal Git PR workflow strategy based on current state.
Applies Law 2 (Make Illegal States Unrepresentable) by validating
branch states and CI results before selecting a workflow path.
Args:
branch_name: Source branch for the PR
target_branch: Destination branch
ci_status: Current CI pipeline status (passing, failing, pending)
review_requirements: List of required reviewers or approval rules
Returns:
Strategy dict with workflow type, required actions, and confidence
"""
# Guard clause - Early Exit (Law 1)
if not branch_name or not target_branch:
raise ValueError("Both source and target branches must be specified")
# Parse input - Make Illegal States Unrepresentable (Law 2)
workflow_state = _analyze_git_state(branch_name, target_branch)
if ci_status == "failing":
strategy = {
"workflow_type": "fix_and_retest",
"priority": "high",
"confidence": 0.95,
"next_action": "run_local_checks_and_push_fix"
}
elif "required_reviewer" in review_requirements and not workflow_state.get("approved"):
strategy = {
"workflow_type": "await_review",
"priority": "medium",
"confidence": 0.85,
"next_action": "request_reviews_and_wait"
}
else:
strategy = {
"workflow_type": "auto_merge",
"priority": "low",
"confidence": 0.90,
"next_action": "execute_merge_with_checks"
}
# Atomic Predictability (Law 3) - Return new dict, don't mutate inputs
strategy["evaluated_at"] = time.time()
strategy["branch_context"] = dict(workflow_state)
return strategy
Pattern 2: Execution with Fallback
def execute_pr_workflow_step(
strategy: Dict,
repo_context: Dict,
max_retries: int = 2
) -> Dict:
"""Execute a Git PR workflow step with resilience and fallback chains.
Implements Fail Fast, Fail Loud (Law 4) for git operations:
- Invalid branch states halt immediately
- CI failures trigger local validation fallback
- Merge blocks trigger manual review escalation
Args:
strategy: Evaluated workflow strategy from evaluate_pr_workflow_strategy
repo_context: Repository configuration and authentication context
max_retries: Maximum retry attempts for transient git/CI errors
Returns:
Execution result with PR URL, status, and audit metadata
"""
# Guard clause - validate repo context (Early Exit)
if not _is_repo_accessible(repo_context):
raise GitWorkflowError("Repository is inaccessible or credentials invalid")
workflow_type = strategy.get("workflow_type")
result = {"success": False, "workflow_type": workflow_type}
for attempt in range(max_retries + 1):
try:
if workflow_type == "fix_and_retest":
pr_data = _push_fix_and_update_pr(repo_context, strategy)
elif workflow_type == "await_review":
pr_data = _request_reviews_and_monitor(repo_context, strategy)
else:
pr_data = _execute_safe_merge(repo_context, strategy)
# Success - Atomic Predictability (Law 3)
result.update({
"success": True,
"pr_url": pr_data.get("url"),
"status": pr_data.get("state"),
"attempts": attempt + 1,
"latency_ms": _calculate_latency()
})
break
except CIExecutionError as e:
# Fail Fast - Don't proceed with failing CI (Law 4)
if attempt == max_retries:
return _apply_ci_fallback(repo_context, strategy, e)
continue
except MergeConflictError as e:
# Transient conflict - try rebase fallback
if attempt == max_retries:
return _apply_merge_fallback(repo_context, strategy, e)
continue
if not result["success"]:
raise GitWorkflowError(f"Workflow '{workflow_type}' exhausted retries")
return result
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
- 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.