paulpas Git Pr Workflows Pr Enhance
Orchestrates intelligent skill selection and execution for git pr workflows pr enhance 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_enhancement_candidates(
pr_metadata: Dict,
available_enhancers: List[Dict],
min_confidence: float = 0.7
) -> Optional[Dict]:
"""Evaluate PR state and select optimal enhancement strategy.
Applies Law 2 by parsing PR metadata at the boundary before scoring.
Scores based on diff complexity, CI pipeline status, label presence, and historical fix rates.
"""
if not pr_metadata or not pr_metadata.get("diff_stats"):
raise ValueError("PR metadata and diff stats are required for enhancement routing")
diff_lines = pr_metadata["diff_stats"]["lines_changed"]
ci_status = pr_metadata.get("ci_status", "unknown")
labels = set(pr_metadata.get("labels", []))
best_enhancer = None
best_score = 0.0
for enhancer in available_enhancers:
score = 0.0
tags = set(enhancer.get("tags", []))
if "ci-diagnose" in tags and ci_status == "failed":
score += 0.4
if "refactor" in tags and diff_lines > 50:
score += 0.3
if "description" in tags and not pr_metadata.get("body", "").strip():
score += 0.3
if enhancer["name"] in labels:
score += 0.1
if score > best_score and score >= min_confidence:
best_score = score
best_enhancer = enhancer
if best_enhancer is None:
return None
# Law 3: Return new data structure, never mutate inputs
return {
"selected_strategy": best_enhancer["name"],
"confidence": best_score,
"pr_context": {
"lines_changed": diff_lines,
"ci_status": ci_status,
"labels": list(labels)
},
"selection_timestamp": time.time()
}
Pattern 2: Execution with Fallback
def execute_pr_enhancement_with_fallback(
strategy: Dict,
pr_context: Dict,
max_retries: int = 2
) -> Dict:
"""Execute PR enhancement strategy with domain-specific fallback chain.
Implements Law 4 (Fail Fast, Fail Loud) for invalid PR states.
Fallback chain: 1. Retry with adjusted linter rules, 2. Suggest manual review, 3. Log & skip.
"""
if not strategy or not pr_context:
raise ValueError("Strategy and PR context must be provided")
strategy_name = strategy["selected_strategy"]
pr_url = pr_context.get("pr_url")
for attempt in range(max_retries + 1):
try:
if strategy_name == "ci-diagnose":
result = _diagnose_ci_failure(pr_url, pr_context["ci_status"])
elif strategy_name == "refactor":
result = _suggest_refactors(pr_url, pr_context["lines_changed"])
elif strategy_name == "description":
result = _generate_pr_description(pr_url)
else:
raise ValueError(f"Unknown enhancement strategy: {strategy_name}")
# Law 3: Atomic return, no mutation of original context
return {
"success": True,
"strategy_applied": strategy_name,
"enhancements": result["suggestions"],
"attempts": attempt + 1,
"latency_ms": _measure_execution_time()
}
except InvalidPRStateError as e:
# Law 4: Halt immediately on corrupt/invalid PR data
raise SkillExecutionError(f"Invalid PR state for {strategy_name}: {e}") from e
except TransientAPILimitError as e:
if attempt == max_retries:
return _apply_pr_fallback_chain(strategy, pr_context)
raise SkillExecutionError(f"Enhancement {strategy_name} failed 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
- 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.