# Address Github Comments > Implements intelligent address github comments with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/address-github-comments` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/address-github-comments` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/address-github-comments/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/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 ```python 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 ```python 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]() ## Related Skills | Skill | Purpose | |