# Issues > Implements intelligent issues with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/issues` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/issues` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/issues/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/issues --- # Issues Orchestrates intelligent skill selection and execution for issues 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 triage_issue_request( issue_context: Dict[str, Any], routing_rules: List[Dict[str, Any]], min_confidence: float = 0.75 ) -> Optional[Dict[str, Any]]: """Route an incoming issue to the appropriate handling skill. Analyzes issue metadata, title, and body to determine intent. Matches against routing rules using keyword extraction and priority scoring. Implements Law 2: Parse at boundary, make illegal states unrepresentable. """ if not issue_context.get("title") or not issue_context.get("body"): raise ValueError("Issue must contain title and body for triage") title = issue_context["title"].lower() body = issue_context["body"].lower() combined_text = f"{title} {body}" # Extract intent signals intent_signals = { "bug": bool(re.search(r"error|crash|fail|broken|issue", combined_text)), "feature": bool(re.search(r"request|add|implement|enhancement|suggestion", combined_text)), "docs": bool(re.search(r"doc|guide|how to|explain|clarify", combined_text)), "config": bool(re.search(r"config|setup|env|variable|permission", combined_text)) } best_match = None best_score = 0.0 for rule in routing_rules: score = 0.0 for keyword in rule.get("keywords", []): if keyword.lower() in combined_text: score += rule.get("weight", 1.0) # Boost score if intent signals match rule type rule_type = rule.get("type") if rule_type in intent_signals and intent_signals[rule_type]: score += 2.0 if score > best_score and score >= min_confidence: best_score = score best_match = rule if best_match is None: return None # Return immutable routing decision return { "routed_skill": best_match["skill_id"], "confidence": best_score, "extracted_labels": [k for k, v in intent_signals.items() if v], "priority": best_match.get("priority", "normal") } ``` ### Pattern 2: Execution with Fallback ```python def execute_issue_workflow( issue_id: str, routing_decision: Dict[str, Any], issue_handler: Callable, fallback_queue: List[str] ) -> Dict[str, Any]: """Execute the assigned skill against the issue with domain-specific fallbacks. Handles issue state transitions, API interactions, and untriaged fallbacks. Implements Law 4: Fail fast on invalid issue states, fail loud on API errors. """ if not issue_id or not routing_decision: raise ValueError("Issue ID and routing decision are required") skill_id = routing_decision["routed_skill"] labels = routing_decision.get("extracted_labels", []) try: # Apply skill to issue (e.g., update labels, assign, comment) result = issue_handler( issue_id=issue_id, skill_id=skill_id, labels=labels, priority=routing_decision.get("priority", "normal") ) return { "status": "resolved", "issue_id": issue_id, "skill_applied": skill_id, "action_taken": result.get("action"), "timestamp": time.time() } except RateLimitError: # Fallback 1: Queue for retry with exponential backoff fallback_queue.append(issue_id) return { "status": "queued", "issue_id": issue_id, "reason": "rate_limited", "retry_after": 60 } except MissingInfoError as e: # Fallback 2: Request clarification instead of failing silently return { "status": "awaiting_response", "issue_id": issue_id, "reason": "missing_info", "requested_fields": e.missing_fields } except InvalidStateError: # Fail loud: Issue already closed or locked raise SkillExecutionError(f"Issue {issue_id} is in invalid state for routing") ``` ### 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 | | --- --- ## 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 Issues Documentation]() - [Jira Issue Management REST API]() - [Linear API Documentation]() - [Issue Triage Methodology (Open Source)]() - [GitHub Issues Labels and Automation]()