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

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

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