An AI-powered evaluation gate that extracts factual claims and scores deliverable quality for autonomous agent workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install evallayer-evaluator
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 evallayer-evaluator 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 EvalLayer Evaluator?
EvalLayer Evaluator is a specialized tool within the Openclaw Skills ecosystem designed to act as a rigorous quality gate for AI-generated content. By leveraging the EvalLayer API, this skill performs multi-stage verification to extract factual claims, assess quality, and provide structured JSON verdicts. It is particularly effective for agents operating in research-heavy environments where accuracy and reputation are paramount.
This skill bridges the gap between raw generation and verified output. It processes deliverables in approximately 14 seconds, returning critical metrics such as confidence scores and payout recommendations. Whether you are managing agent-to-agent commerce or high-stakes research, EvalLayer Evaluator ensures that every piece of data meets a defined standard of excellence before it is finalized or paid for.
EvalLayer Evaluator Use Cases
- Verifying the accuracy of research papers or market reports before publication.
- Implementing automated quality gates in agent-to-agent workflows and commerce flows.
- Extracting and validating individual factual claims from long-form text content.
- Assessing the reputation of service providers based on historical evaluation performance.
- Scoring deliverables to determine partial or full payout in decentralized worker networks.
How EvalLayer Evaluator Works
- The user or agent provides a specific topic and the deliverable content to the skill script.
- The skill utilizes python3 to safely escape the text and curl to transmit the data to the EvalLayer API via HTTPS.
- EvalLayer processes the content through a multi-stage pipeline to identify and verify claims against its intelligence database.
- The system generates a structured JSON response containing a quality score, claim counts, and a pass/fail verdict.
- The agent receives the verdict to automatically trigger next steps, such as initiating a payout or requesting a revision.
EvalLayer Evaluator Setup
To integrate this skill into your Openclaw Skills environment, follow these steps:
- Register for an API key:
curl -s -X POST https://api.evallayer.ai/register \
-H "Content-Type: application/json" \
-d '{"agent_id": "your-agent-id"}'
- Configure your environment variable:
export EVALLAYER_API_KEY="sk_your_key_here"
- Ensure dependencies (
curlandpython3) are installed and accessible in your PATH.
EvalLayer Evaluator Data Schema & Taxonomy
The skill returns a structured JSON object for every evaluation. Below is the metadata taxonomy:
| Field | Type | Description |
|---|---|---|
passed |
Boolean | Final quality verdict based on the threshold. |
quality_score |
Float | 0.0-1.0 rating (0.4+ is a pass, 0.7+ is high quality). |
confidence_score |
Float | The AI's certainty in the provided evaluation. |
claims_total |
Integer | Number of distinct factual claims identified. |
payout_recommendation |
String | Suggested payment: "full", "partial", or "reject". |
evaluation_id |
String | Unique ID used to fetch detailed claim-by-claim breakdowns. |
EvalLayer Evaluator Advanced Features
- Multi-agent support via the reputation API to track performance across different providers.
- Aggregated Intelligence API access to monitor trending verified claims and market topics.
- Detailed claim breakdown retrieval for granular audit logs and error identification.
- Flexible payout logic support based on customizable quality and confidence thresholds.
- Low-dependency mode using direct curl requests for restricted environments.
SKILL.md
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