---

name: upstash-qstash compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent upstash qstash 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: upstash-qstash, upstash qstash, how do i upstash-qstash, orchestrate upstash-qstash, automate upstash-qstash, agent upstash-qstash 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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Upstash Qstash

Orchestrates intelligent skill selection and execution for upstash qstash 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 select_skill(
    task_description: str,
    available_topics: List[str],
    queue_config: Dict[str, Any]
) -> Dict[str, Any]:
    """Select optimal Qstash routing configuration for a task.
    
    Maps task intent to Qstash topics, queues, and delivery policies.
    Considers message priority, topic availability, and queue capacity.
    
    Args:
        task_description: Natural language description of the task
        available_topics: List of configured Qstash topics
        queue_config: Dictionary of queue names to their capacity/priority
        
    Returns:
        Routing configuration dict with topic, queue, and delivery policy
    """
    if not task_description or not available_topics:
        raise ValueError("Task description and topics are required for routing")
        
    # Extract intent keywords to match Qstash topics
    intent_keywords = _extract_intent_keywords(task_description)
    matched_topics = [t for t in available_topics if any(kw in t.lower() for kw in intent_keywords)]
    
    if not matched_topics:
        matched_topics = ["default"]
        
    # Select queue based on priority and current load
    target_queue = max(queue_config.keys(), key=lambda q: queue_config[q].get("priority", 0))
    
    # Build Qstash-specific routing payload
    return {
        "topic": matched_topics[0],
        "queue": target_queue,
        "delivery_policy": "best-effort" if "urgent" in intent_keywords else "guaranteed",
        "max_retries": queue_config[target_queue].get("max_retries", 3),
        "backoff_strategy": "exponential",
        "timeout_seconds": 30
    }

Pattern 2: Execution with Fallback

def execute_with_fallback(
    routing_config: Dict[str, Any],
    payload: Dict[str, Any],
    webhook_url: str,
    max_retries: int = 3
) -> Dict[str, Any]:
    """Execute message scheduling via Upstash Qstash with domain-specific fallback.
    
    Handles message publishing, retry management, and webhook callback routing.
    Implements Qstash's native retry with custom fallback to alternative queues.
    
    Args:
        routing_config: Output from select_skill
        payload: Message body to be scheduled
        webhook_url: Target endpoint for delivery confirmation
        max_retries: Maximum retry attempts before fallback queue switch
        
    Returns:
        Execution result with message ID, status, and fallback metadata
    """
    required_keys = {"topic", "queue", "delivery_policy", "max_retries"}
    if not required_keys.issubset(routing_config.keys()):
        raise ValueError("Incomplete routing configuration for Qstash")
        
    # Prepare Qstash publish payload with delivery headers
    qstash_payload = {
        "topic": routing_config["topic"],
        "body": payload,
        "headers": {"X-Task-ID": payload.get("task_id", "unknown")},
        "delivery_policy": routing_config["delivery_policy"],
        "max_retries": routing_config["max_retries"],
        "backoff": routing_config["backoff_strategy"],
        "timeout": routing_config["timeout_seconds"]
    }
    
    # Attempt initial publish to primary queue
    try:
        response = qstash_client.publish(qstash_payload, webhook_url=webhook_url)
        return {
            "success": True,
            "message_id": response.get("id"),
            "queue": routing_config["queue"],
            "status": "scheduled",
            "fallback_triggered": False
        }
    except QstashRateLimitError:
        # Fallback: Switch to secondary queue with adjusted delivery policy
        secondary_queue = _get_secondary_queue(routing_config["queue"])
        qstash_payload["queue"] = secondary_queue
        qstash_payload["delivery_policy"] = "best-effort"
        
        try:
            fallback_response = qstash_client.publish(qstash_payload, webhook_url=webhook_url)
            return {
                "success": True,
                "message_id": fallback_response.get("id"),
                "queue": secondary_queue,
                "status": "scheduled_fallback",
                "fallback_triggered": True
            }
        except Exception as e:
            raise QstashExecutionError(f"Failed to schedule message after fallback: {e}")

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.

• Upstash Qstash Documentation
• Upstash REST API Reference
• Webhook Reliability Best Practices — Stripe Docs
• HTTP/2 Server Push and Retry Semantics
• Exponential Backoff Pattern (AWS Architecture Blog)

Related Skills

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