A collection of three micro-services focused on repairing broken JSON, validating response rules, and generating guaranteed structured data for AI agents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-reliability-layer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ai-reliability-layer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is AI Reliability Layer?
The AI Reliability Layer provides a critical infrastructure component for developers building with Openclaw Skills. It solves the common hallucination and formatting problems by offering specialized endpoints that automatically repair truncated output, fix syntax errors like trailing commas, and enforce strict schema compliance. This layer ensures that any language model can be converted into a reliable, programmatic data source for production-ready applications.
By acting as a dependable middleware, the service allows developers to move away from fragile regex-based parsing and expensive retry loops. Whether you are dealing with markdown code fences that shouldn't be there or missing brackets in a complex object, these Openclaw Skills provide a standardized way to guarantee the integrity of your agent's data.
AI Reliability Layer Use Cases
- Rectifying malformed JSON strings containing trailing commas, single quotes, or missing brackets.
- Validating that AI generated content adheres to specific length, format, or schema rules.
- Generating guaranteed typed responses for complex data extraction tasks where structure is non-negotiable.
- Implementing cost-effective, pay-per-call reliability checks without managing API keys or subscriptions.
- Cleaning LLM responses that are wrapped in unwanted markdown code fences or containing truncated content.
How AI Reliability Layer Works
- The user identifies a potential output reliability issue or defines a specific structural requirement for an LLM task.
- A POST request is sent to the appropriate service endpoint: fix-json, validate-output, or generate-structured.
- The micro-service processes the payload, applying correction algorithms, validation logic, or auto-fixing generation steps.
- The system returns a clean, valid, and structured JSON response along with metadata describing the changes made.
- Transactional payment is settled automatically per call using USDC on the Base network via the x402 protocol.
AI Reliability Layer Setup
To integrate these Openclaw Skills, you can interact with the endpoints using any standard HTTP client. There is no need for traditional API keys or monthly subscriptions.
# Example: Repairing a malformed JSON string with a trailing comma
curl -X POST https://arena.example.com/x402s/fix-json \
-H "Content-Type: application/json" \
-d '{"input": "{\"status\": \"active\",}"}'
# Example: Generating a structured response with a schema
curl -X POST https://arena.example.com/x402s/generate-structured \
-H "Content-Type: application/json" \
-d '{
"prompt": "List 2 colors",
"schema": {"type": "array", "items": {"type": "string"}}
}'
AI Reliability Layer Data Schema & Taxonomy
The services within this skill utilize a consistent data schema for inputs and outputs:
| Endpoint | Primary Input | Key Output Fields |
|---|---|---|
/fix-json |
input (string) |
output (fixed object), fixed (boolean) |
/validate-output |
output (string), rules (object) |
valid (boolean), violations (array) |
/generate-structured |
prompt (string), schema (object) |
output (typed object), model (string) |
All services utilize the x402 protocol for decentralized payment verification.
AI Reliability Layer Advanced Features
- Frictionless pay-per-call architecture using USDC on Base, eliminating the need for subscription management.
- Robust auto-fix capabilities for truncated LLM responses and common syntax errors.
- Support for complex JSON schema enforcement during the generation phase.
- Comprehensive validation rules including length checks and pattern blocking for security.
- Native integration with the Reno Labs agent fleet and the AEA Arena ecosystem.
SKILL.md
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