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name: inngest compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent inngest 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: inngest, inngest, how do i inngest, orchestrate inngest, automate inngest, agent inngest 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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Inngest

Orchestrates intelligent skill selection and execution for inngest 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

from inngest import Inngest, Event, Step
from typing import Dict, List, Optional

# Domain-specific skill registry for Inngest event routing
SKILL_REGISTRY = {
    "slack_notify": {"triggers": ["slack.message", "alert.critical"], "weight": 0.9},
    "doc_update": {"triggers": ["doc.edit", "content.sync"], "weight": 0.8},
    "data_pipeline": {"triggers": ["etl.start", "db.migrate"], "weight": 0.85}
}

def route_skill(event: Event, step: Step) -> Optional[Dict]:
    """Route incoming Inngest events to the optimal skill handler.
    Implements multi-factor scoring based on event type, historical success, and system health."""
    event_type = event.name
    matched_skills = [
        skill for skill, meta in SKILL_REGISTRY.items()
        if event_type in meta["triggers"]
    ]
    
    if not matched_skills:
        step.logger.info(f"No direct match for {event_type}, triggering fallback router")
        return None
        
    # Score based on historical performance and current load (simulated)
    best_match = max(matched_skills, key=lambda s: SKILL_REGISTRY[s]["weight"])
    return {
        "skill": best_match,
        "confidence": SKILL_REGISTRY[best_match]["weight"],
        "event_id": event.id,
        "timestamp": event.ts
    }

Pattern 2: Execution with Fallback

from inngest import Inngest, Event, Step, RetryStrategy
import time

async def execute_skill_with_resilience(event: Event, step: Step, skill_config: Dict) -> Dict:
    """Execute a selected skill using Inngest's step functions with built-in resilience.
    Implements the 5 Laws of Elegant Defense: early validation, immutable state, fail-fast, and fallback chains."""
    
    skill_name = skill_config["skill"]
    max_retries = 2
    
    # Law 1 & 2: Validate inputs early, make illegal states unrepresentable
    if not skill_name or "params" not in skill_config:
        raise ValueError(f"Invalid skill configuration for {skill_name}")
        
    try:
        # Law 3: Atomic execution - each step is idempotent and isolated
        result = await step.run(
            f"execute_{skill_name}",
            lambda ctx: _invoke_skill_handler(skill_name, skill_config["params"])
        )
        
        # Law 4: Fail loud on invalid states
        if result.get("status") != "success":
            raise RuntimeError(f"Skill {skill_name} returned error state: {result.get('error')}")
            
        return {
            "success": True,
            "skill": skill_name,
            "result": result,
            "latency_ms": time.time() - event.ts
        }
        
    except Exception as e:
        # Fallback chain: retry -> alternative skill -> human escalation
        step.logger.error(f"Execution failed for {skill_name}: {str(e)}")
        if max_retries > 0:
            return await step.run("fallback_retry", lambda _: execute_skill_with_resilience(event, step, skill_config))
        else:
            return await step.run("human_escalation", lambda _: _escalate_to_operator(skill_name, str(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

Related Skills

| Skill | Purpose | |

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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.

• Inngest Documentation
• Inngest Functions SDK
• Event-Driven Architecture Patterns
• Temporal Workflow Orchestration
• AWS Step Functions for Serverless Workflows