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Rewardful
by
Vlad Ursul
· GitHub ↗
· v1.0.2
· MIT-0
252
Downloads
0
Stars
0
Active Installs
3
Versions
Install in OpenClaw
/install rewardful
Description
Rewardful integration. Manage Affiliates, Products. Use when the user wants to interact with Rewardful data.
Usage Guidance
This skill is an instruction-only integration that tells you how to use the Membrane CLI to talk to Rewardful. Before installing or running it: (1) Verify the @membranehq/cli package on the npm registry and the upstream repo (check maintainers, recent releases, and reviews). (2) Prefer npx where possible to avoid a global install, or install in an isolated environment. (3) Keep in mind Membrane handles OAuth/browser login — you will sign in via your browser; do not paste API keys into chat. (4) Review the CLI's permissions/behaviors (network access, file system usage) if you have strict security requirements. (5) If you want to limit risk, run initial testing in a sandboxed container or VM and audit the CLI commands the skill runs.
Capability Analysis
Type: OpenClaw Skill
Name: rewardful
Version: 1.0.2
The skill provides instructions for an AI agent to interact with Rewardful via the Membrane CLI (@membranehq/cli). It outlines standard procedures for authentication, connection management, and executing API actions through the Membrane platform. No malicious patterns, data exfiltration, or suspicious execution logic were identified in SKILL.md or _meta.json.
Capability Assessment
Purpose & Capability
The name/description (Rewardful integration) match the runtime instructions: it tells the agent how to install and use the Membrane CLI to connect to Rewardful, list/run actions, and proxy requests. Required capabilities (network + Membrane account) are appropriate for this purpose.
Instruction Scope
SKILL.md stays on-topic: it instructs installing Membrane CLI, performing browser-based login flows, creating a connection, listing/running actions, and proxying API requests. It does not instruct the agent to read unrelated files, ask for local secrets, exfiltrate data, or modify other system configuration.
Install Mechanism
The skill recommends installing @membranehq/cli globally via npm (npm install -g). Using an npm package is expected for a CLI but carries the usual supply-chain considerations; the skill itself does not bundle or download arbitrary archives. The README also suggests using npx for some commands, which avoids global install.
Credentials
No environment variables, config paths, or credentials are requested by the skill. Authentication is delegated to Membrane's browser-based flow, which is proportionate to the described integration.
Persistence & Privilege
always is false and the skill is user-invocable. The skill does not request persistent system presence or attempt to change other skills' configurations. Autonomous invocation is allowed (platform default) but not accompanied by broad privileges or secret access.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install rewardful - After installation, invoke the skill by name or use
/rewardful - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.2
Revert refresh marker
v1.0.1
Refresh update marker
v1.0.0
Auto sync from membranedev/application-skills
Metadata
Frequently Asked Questions
What is Rewardful?
Rewardful integration. Manage Affiliates, Products. Use when the user wants to interact with Rewardful data. It is an AI Agent Skill for Claude Code / OpenClaw, with 252 downloads so far.
How do I install Rewardful?
Run "/install rewardful" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Rewardful free?
Yes, Rewardful is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Rewardful support?
Rewardful is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Rewardful?
It is built and maintained by Vlad Ursul (@gora050); the current version is v1.0.2.
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