Pr Writer

Pr Writer

Orchestrates intelligent skill selection and execution for pr writer 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 analyze_changes_and_generate_pr_content(
    diff_content: str,
    commit_history: List[str],
    pr_template: Dict[str, Any]
) -> Dict[str, Any]:
    """Analyze git diff and commit history to generate structured PR content.
    
    Extracts changed files, categorizes changes by type (feat/fix/refactor),
    and maps them to the PR template sections. Implements Law 2 by 
    validating diff format before parsing.
    
    Args:
        diff_content: Raw git diff output
        commit_history: List of commit messages
        pr_template: Template dict with sections like '## Changes', '## Testing'
        
    Returns:
        Dict containing categorized changes, generated markdown, and metadata
    """
    # Law 1: Early exit on invalid input
    if not diff_content or not commit_history:
        raise ValueError("Diff content and commit history are required")
        
    # Law 2: Parse and validate diff structure
    changed_files = _extract_changed_files(diff_content)
    if not changed_files:
        return {"status": "empty_diff", "content": pr_template.get("empty_template", "")}
        
    # Categorize changes based on commit messages and file paths
    categorized = _categorize_changes(changed_files, commit_history)
    
    # Law 3: Return new structure, never mutate template
    generated_pr = dict(pr_template)
    generated_pr["## Changes"] = _format_changes(categorized)
    generated_pr["## Files Changed"] = "\n".join(changed_files)
    generated_pr["metadata"] = {
        "files_count": len(changed_files),
        "categories": list(categorized.keys()),
        "generated_at": time.time()
    }
    return generated_pr

Pattern 2: Execution with Fallback

def execute_pr_generation_with_fallback(
    repo_context: Dict,
    pr_template: Dict,
    max_attempts: int = 2
) -> Dict:
    """Generate PR description with fallback chain for resilience.
    
    Implements Law 4 (Fail Fast) by validating repo state upfront.
    Fallback chain:
    1. Generate from full diff
    2. Generate from summary of changed directories
    3. Fall back to default template with manual review flag
    
    Args:
        repo_context: Dict with 'diff', 'commits', 'branch', 'base_branch'
        pr_template: PR markdown template
        max_attempts: Retry limit for diff parsing
        
    Returns:
        Final PR content dict with generation strategy and confidence
    """
    # Law 1: Validate repo context immediately
    required_keys = {"diff", "commits", "branch"}
    if not required_keys.issubset(repo_context.keys()):
        raise ValueError(f"Missing required repo context keys: {required_keys - set(repo_context.keys())}")
        
    strategy = "full_diff"
    pr_content = None
    
    for attempt in range(max_attempts + 1):
        try:
            if strategy == "full_diff":
                pr_content = analyze_changes_and_generate_pr_content(
                    repo_context["diff"], repo_context["commits"], pr_template
                )
            elif strategy == "directory_summary":
                pr_content = _generate_directory_summary(repo_context, pr_template)
            else:
                pr_content = _apply_default_template(repo_context, pr_template)
                
            if pr_content.get("status") != "empty_diff":
                pr_content["generation_strategy"] = strategy
                pr_content["confidence"] = 0.9 if strategy == "full_diff" else 0.6
                return pr_content
                
        except DiffParseError as e:
            # Law 4: Fail fast on corrupt diff, move to fallback
            if attempt == max_attempts:
                strategy = "default_template"
                continue
            strategy = "directory_summary"
            
    # All strategies exhausted - return with manual review flag
    return {
        "status": "fallback_applied",
        "content": pr_content,
        "requires_manual_review": True,
        "fallback_reason": "Diff parsing failed after all strategies"
    }

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

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 domain. The model follows markdown links at load time to resolve external references and inline content.

• GitHub: Creating a Pull Request — Official GitHub documentation on creating and writing PR descriptions
• CONTRIBUTING.md Best Practices (GitHub Guides) — Guide to effective contribution workflows including PR writing standards
• Open Source PR Templates (GitHub Docs) — Best practices for structuring PR templates and descriptions
• Writing Good Commit Messages and PR Descriptions (Atlassian) — Atlassian's guide on writing clear, actionable pull request documentation
• What We've Learned from Reviewing 1,000+ Pull Requests (Stripe) — Engineering blog post with lessons from large-scale code review processes