Address Github Comments

Address Github Comments

Orchestrates intelligent skill selection and execution for address github comments 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_and_select_action(
    github_event: Dict,
    repo_config: Dict,
    min_confidence: float = 0.7
) -> Optional[Dict]:
    """Analyze a GitHub comment and select the appropriate response action.
    
    Evaluates comment intent against repository-specific guidelines to determine
    whether to auto-reply, request clarification, apply a code fix, or escalate.
    
    Args:
        github_event: Raw webhook payload or parsed comment context
        repo_config: Repository-specific rules, labels, and maintainer preferences
        min_confidence: Minimum confidence threshold for automated responses
        
    Returns:
        Action plan dictionary with strategy, parameters, and confidence
        
    Raises:
        ValueError: If github_event lacks required fields or repo_config is invalid
    """
    # Guard clause - Early Exit (Law 1)
    if not github_event or not github_event.get("comment", {}).get("body"):
        raise ValueError("Invalid GitHub comment event: missing body")
        
    if not repo_config.get("allowed_actions"):
        raise ValueError("Repository configuration missing allowed_actions")
    
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    comment_data = _normalize_comment(github_event["comment"])
    repo_rules = _load_active_rules(repo_config)
    
    best_action = None
    best_score = 0.0
    
    for action in repo_config["allowed_actions"]:
        score = _score_action_match(comment_data, action, repo_rules)
        
        if score > best_score and score >= min_confidence:
            best_score = score
            best_action = action
    
    if best_action is None:
        return None
    
    # Atomic Predictability (Law 3) - Return new dict, don't mutate
    result = dict(best_action)
    result["selected_confidence"] = best_score
    result["comment_id"] = github_event["comment"]["id"]
    result["timestamp"] = time.time()
    return result

Pattern 2: Execution with Fallback

def execute_comment_response(
    action_plan: Dict,
    github_client: Any,
    max_retries: int = 2
) -> Dict:
    """Execute the selected response action for a GitHub comment with fallback chain.
    
    Implements the Fail Fast, Fail Loud principle (Law 4):
    - Invalid states halt immediately with descriptive errors
    - No silent failures or partial results
    
    Fallback chain:
    1. Retry with original parameters
    2. Retry with adjusted parameters (e.g., shorter response, different template)
    3. Queue for manual review if rate-limited or critical
    4. Log & return error with context for audit trail
    
    Args:
        action_plan: Selected action strategy with parameters
        github_client: Authenticated GitHub API client instance
        max_retries: Maximum retry attempts before fallback
        
    Returns:
        Execution result with metadata (success, timing, confidence, response_url)
        
    Raises:
        GitHubResponseError: If all retries and fallbacks exhausted
    """
    # Guard clause - validate action plan (Early Exit)
    if not _is_action_valid(action_plan):
        raise GitHubResponseError(f"Invalid action plan: {action_plan.get('type', 'unknown')}")
    
    # Parse context - Ensure trusted state (Law 2)
    validated_params = _validate_response_params(action_plan, github_client)
    
    for attempt in range(max_retries + 1):
        try:
            # Execute domain-specific GitHub API call
            response_url = github_client.post_comment(
                repo=validated_params["repo"],
                issue_number=validated_params["issue_number"],
                body=validated_params["response_body"],
                in_reply_to=validated_params["comment_id"]
            )
            
            # Success - Atomic Predictability (Law 3)
            return {
                "success": True,
                "action_executed": action_plan["type"],
                "response_url": response_url,
                "attempts": attempt + 1,
                "latency_ms": _calculate_latency(),
                "confidence": action_plan["selected_confidence"]
            }
            
        except RateLimitError as e:
            # Transient error - try fallback
            if attempt == max_retries:
                return _queue_for_manual_review(action_plan, validated_params)
            time.sleep(2 ** attempt)  # Exponential backoff
            
        except InvalidStateError as e:
            # Fail Fast - Don't try to patch bad data (Law 4)
            raise GitHubResponseError(
                f"Invalid state during response: {str(e)}"
            ) from e
    
    # All retries exhausted - Fail Loud (Law 4)
    raise GitHubResponseError(
        f"Failed to post response 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

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.

• GitHub API Documentation
• GitHub Webhooks Reference
• OpenAPI Specification
• RESTful API Design Guide (Microsoft)
• GitHub Actions Expressions

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