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name: create-issue-gate compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent create issue gate with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: create-issue-gate, create issue gate, how do i create-issue-gate, orchestrate create-issue-gate, automate create-issue-gate, agent create-issue-gate archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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Create Issue Gate

Orchestrates intelligent skill selection and execution for create issue gate 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 evaluate_issue_gate(
    request: Dict[str, Any],
    gate_rules: List[Dict],
    available_trackers: List[Dict]
) -> Optional[Dict]:
    """Gate evaluation for issue creation requests.
    
    Validates request against gate rules, scores available issue trackers,
    and selects the optimal routing path based on project mapping and reliability.
    """
    # Law 1: Early exit on malformed request
    if not request.get("title") or not request.get("project_key"):
        raise ValueError("Issue gate requires 'title' and 'project_key'")
        
    # Law 2: Parse & validate against gate rules
    validated_request = _parse_issue_request(request)
    for rule in gate_rules:
        if not _check_gate_rule(validated_request, rule):
            return {"status": "blocked", "reason": f"Failed gate rule: {rule['id']}"}
            
    # Score trackers based on project mapping & historical success
    best_tracker = None
    best_score = 0.0
    for tracker in available_trackers:
        score = _calculate_tracker_match(validated_request, tracker)
        if score > best_score and score >= 0.75:
            best_score = score
            best_tracker = tracker
            
    if not best_tracker:
        return {"status": "unroutable", "reason": "No tracker meets minimum gate threshold"}
        
    # Law 3: Return new structure, don't mutate inputs
    return {
        "status": "routed",
        "selected_tracker": dict(best_tracker),
        "confidence": best_score,
        "validated_request": validated_request
    }

Pattern 2: Execution with Fallback

def execute_issue_creation_with_fallback(
    tracker_skill: Dict,
    validated_request: Dict,
    fallback_trackers: List[Dict]
) -> Dict:
    """Execute issue creation with multi-level fallback chain.
    
    Implements resilient issue submission: primary tracker -> alternative -> manual queue.
    """
    max_retries = 2
    attempt = 0
    
    while attempt <= max_retries:
        try:
            # Law 4: Fail fast on auth/config errors
            if not _validate_tracker_config(tracker_skill):
                raise ConfigurationError(f"Tracker {tracker_skill['id']} misconfigured")
                
            result = _call_tracker_api(tracker_skill, validated_request)
            return {
                "success": True,
                "issue_id": result.get("id"),
                "url": result.get("url"),
                "tracker": tracker_skill["id"],
                "attempts": attempt + 1
            }
            
        except RateLimitError:
            attempt += 1
            if attempt > max_retries:
                break
            time.sleep(2 ** attempt)
            
    # Fallback chain exhausted -> try alternative trackers
    for alt in fallback_trackers:
        try:
            result = _call_tracker_api(alt, validated_request)
            return {
                "success": True,
                "issue_id": result.get("id"),
                "url": result.get("url"),
                "tracker": alt["id"],
                "fallback_used": True
            }
        except Exception:
            continue
            
    # Final fallback: queue for manual review
    return {
        "success": False,
        "status": "queued_for_manual_review",
        "reason": "All automated trackers failed",
        "request": validated_request
    }

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

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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 API Reference
• Issue Templates (GitHub Docs)
• Jira Issue Management Guide
• Issue Triage Best Practices (GitHub)
• Project Board Automation Rules

Related Skills

| Skill | Purpose | |