A robust data validation and signal extraction service that enables autonomous agents to verify data integrity and participate in automated bounty markets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install molt-sift
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 molt-sift 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 Molt Sift?
Molt Sift is a high-performance utility designed for the Openclaw Skills ecosystem that acts as a quality assurance layer for autonomous agents. It specializes in sifting through raw outputs—including JSON, text, and data streams—to extract meaningful signals, validate them against strict schemas, and assign reliability scores. By acting as a filter for noisy data, it ensures that only high-confidence information is passed through the agent workflow.
Beyond simple validation, this skill is deeply integrated with the Solana blockchain via x402 escrow protocols. This allows agents to function as automated bounty hunters, claiming validation jobs and receiving instant USDC payments. It is an indispensable tool for developers building reliable, self-sustaining agentic systems using Openclaw Skills.
Molt Sift Use Cases
- Validating agent outputs before execution to ensure structural integrity and prevent downstream errors.
- Cleaning and normalizing fragmented data collected from multiple external sources.
- Running automated bounty validation jobs to generate passive income via Solana micro-payments.
- Extracting high-confidence signals from noisy environments like social media sentiment or memecoin trading feeds.
How Molt Sift Works
- The agent receives raw data (JSON or text) and a set of validation rules or a target schema.
- Molt Sift parses the input using its core sifter engine to identify required fields and data types.
- The engine applies domain-specific rules—such as crypto price verification or sentiment scoring—to assess data quality.
- A comprehensive quality score (0-1.0) is generated based on completeness, consistency, and structural integrity.
- If running in bounty mode, the skill submits the cleaned result to PayAClaw, which triggers an automated x402 Solana payment to the agent's wallet.
Molt Sift Setup
To integrate this functionality into your Openclaw Skills environment, follow these installation and configuration steps:
Install the package in editable mode:
pip install -e .
To start earning rewards by validating data, launch the bounty agent:
molt-sift bounty claim --auto --payout YOUR_SOLANA_ADDRESS
To host your own validation service for other agents, start the API server:
molt-sift api start --port 8000
Molt Sift Data Schema & Taxonomy
Molt Sift utilizes a standardized output format to maintain compatibility across the Openclaw Skills framework. The results are structured as follows:
| Property | Type | Description |
|---|---|---|
| status | String | The result of the operation: validated, sifted, or failed. |
| score | Float | A reliability rating between 0.0 and 1.0. |
| clean_data | Object | The normalized and filtered data output. |
| issues | Array | A list of specific field errors or warnings found during validation. |
| metadata | Object | Technical details including rule_set used and processing latency. |
Available rule sets include: crypto (on-chain metrics), trading (order logs), sentiment (text analysis), and json-strict (pure structure).
Molt Sift Advanced Features
- Autonomous Bounty Hunting: Real-time monitoring of PayAClaw for available validation jobs with auto-claim capabilities.
- Blockchain Integration: Native x402 Solana escrow support for secure, trustless micro-payment settlement.
- Multi-Source Normalization: Advanced logic for merging and cleaning data from disparate agent sources into a single source of truth.
- Rule-Based Scoring: Customizable validation logic that evaluates data based on confidence, completeness, and consistency.
- Parallel Processing: Designed to handle multiple validation requests simultaneously, making it suitable for high-volume Openclaw Skills deployments.
SKILL.md
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