How does the tiered routing system save money on cloud API costs?

The skill uses a local ONNX model to process simple tasks on your CPU; it only sends the request to the cloud (via OpenRouter) if the local model's confidence falls below your set threshold, significantly reducing API costs.

Are the ONNX model checkpoints included in the purchase?

No, you will need to provide your own ONNX checkpoints for the local inference engine, as these are not bundled to keep the skill package lightweight and customizable for your specific use cases.

What are the specific environment requirements for running the local inference tier?

The router is compatible with Python 3.8+, but it requires a separate, isolated Python 3.11 environment which the bundled installer helps set up to manage the specific ONNX runtime dependencies.

Can I manually override the routing and force the skill to stay local?

Yes, you can choose between 'auto', 'local', 'cloud', or 'strict' modes, allowing you to force local-only processing for privacy or cloud-only for complex tasks when needed.

How does this skill handle output compared to standard prompt-based classification?

The skill returns strictly typed primitives like probabilities, choices, or scores, eliminating the need for complex parsing or regex to handle the unpredictable prose often generated by standard LLMs.

Works with the AI tools you already use

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+18 agents

Hermes Decision Brain

A local-first decision router that cascades from CPU inference to cloud only when confidence is low.

$39

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Converts to your local currency at checkout

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Hermes Decision Brain

Example session with this skill installed

Route this user request: "I want to cancel my subscription immediately because of a duplicate charge last month."

Choices: cancel, upgrade, support, billing.

Use auto mode.

Read your context and instructions
Compiled the hermes decision brain
Generated the document

Decision successfully resolved using the local tier. The state fit the local token budget and the model answered with enough confidence, so no cloud escalation was needed.

Decision Audit

Attribute	Value
Result	`cancel`
Source	`laya` (Local, free)
Confidence	`0.7402`
Escalated	`False`
Latency	`211 ms`
Cost (USD)	`$0.00000`
Model	`laya-onnx-v1`

{
  "decision": "cancel",
  "type": "choice",
  "confidence": 0.7402,
  "escalated": false,
  "escalate_reason": null,
  "cost_usd": 0.0,
  "latency_ms": 211,
  "source": "laya",
  "layer_detail": {
    "laya": {
      "status": "success",
      "confidence": 0.7402,
      "distribution": [0.7402, 0.0891, 0.1103, 0.0604],
      "state_tokens": 68
    }
  },
  "metadata": {
    "mode": "auto",
    "token_counter": "laya_tokenizer"
  }
}

hermes-decision-brain.pdf

PDF · document

Generated

Example file from a real run - the skill writes it into your workspace.

Connects securely to your tools. The creator never sees your data.

About this skill

Stop wasting budget on cloud LLMs for simple classification tasks. This skill provides a tiered decision engine that handles routine logic locally on your CPU and only escalates to the cloud when confidence drops below measured thresholds.

What it does

Multi-tier routing handles decisions through a local ONNX model first, then an optimized cloud tier.
Typed outputs returns specific noul probabilities, choice selections, or score values rather than unpredictable prose.
Automated escalation triggers cloud calls based on state length, low confidence, or flat probability distributions.
Audit tracking provides detailed latency, cost, and confidence metrics for every decision layer.
Runtime management builds and maintains an isolated Python 3.11 environment for local inference.

How it works

Initialize the runtime using the bundled installer and health check scripts to set up the local ONNX environment.
Configure routing by choosing between auto, local, cloud, or strict modes to control escalation behavior.
Execute decisions via decision_router.decide() by passing state, questions, and desired output types.
Monitor performance through the on_escalate callback to track costs and refine local confidence thresholds.

Frameworks & tools

Built for Python 3.8+ (router) and Python 3.11 (local venv). Uses ONNX for local CPU inference via Laya and integrates with OpenRouter for cloud escalation.

Why this beats prompting it yourself

Prompting an LLM for classification is slow and expensive. This skill replaces vague prompts with typed primitives and a local-first cascade that cuts latency to milliseconds for routine tasks.

Use cases

Intent classification to route incoming user requests to specific agent tools.
Sentiment analysis for high-volume ticket triaging without cloud costs.
Binary validation to confirm if a state meets specific criteria before proceeding.
Risk scoring for fraud or churn detection using consistent numerical ranges.

Known limitations

Calibrated on five reference cases. Local performance on non-English languages is inconsistent. Requires manual setup of ONNX checkpoints as they are not bundled.

How to install

Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

~30 seconds

1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.

Skills folder by agent

Click the path to copy it. Create the folder if it does not exist yet.

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Openrouter

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Hermes Decision Brain