---

name: git-pr-workflows-pr-enhance compatibility: opencode completeness: 95 content-types:

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

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 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:

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.

• GitHub PR Review Best Practices
• Diff Checker and Code Comparison Tools
• Code Review Checklist (Google Engineering)
• Conventional Commits Specification
• Pull Request Template Guidelines (GitHub Docs)