What this skill does
Multi-model peer review layer using local LLMs via Ollama to catch errors in cloud model output. Fan-out critiques to 2-3 local models, aggregate flags, synthesize consensus. Use when: validating trade analyses, reviewing agent output quality, testing local model accuracy, checking any high-stakes Claude output before publishing or acting on it. Don't use when: simple fact-checking (just search the web), tasks that don't benefit from multi-model consensus, time-critical decisions where 60s latency is unacceptable, reviewing trivial or low-stakes content. Negative examples: - "Check if this date is correct" → No. Just web search it. - "Review my grocery list" → No. Not worth multi-model inference. - "I need this answer in 5 seconds" → No. Peer review adds 30-60s latency. Edge cases: - Short text (<50 words) → Models may not find meaningful issues. Consider skipping. - Highly technical domain → Local models may lack domain knowledge. Weight flags lower. - Creative writing → Factual review doesn't apply well. Use only for logical consistency.
Peer Review is part of the Developer Tools category — developer tools that help your agent write, review, debug, and ship code. You can install it on its own or alongside other developer tools skills from the OpenClaw catalog.
How to install Peer Review
The easiest path is via the OpenClaw Easy desktop app — one click, no terminal required:
- Download OpenClaw Easy for macOS or Windows (free, one-click installer, ~30 seconds).
- Open the in-app Skills panel.
- Search for
peer-reviewand click Install. - The skill activates automatically when an incoming message matches its description.
Install from the command line
If you already run the OpenClaw CLI, add Peer Review with a single command:
openclaw skills add peer-review
This pulls peer-review from ClawHub and installs it into ~/.openclaw/skills/peer-review/. Restart the OpenClaw gateway afterwards so the new skill is discovered.
How to use Peer Review
Once installed, Peer Review activates on its own: when an incoming message on WhatsApp, Telegram, Slack, Discord, Feishu or Line matches the skill's description, your OpenClaw agent loads it and runs the workflow. You can also trigger it explicitly by describing the task in chat. No extra configuration is required after install.
Turning video into a finished short
Peer Review works with video. Getting from there to something postable is a separate job: ViralMint — an open-source video pipeline — reframes to vertical, cleans the audio, cuts dead air, burns animated captions, and exports a version sized for TikTok, Reels, and Shorts.
It runs as an MCP server, so an OpenClaw agent can drive it from the same chat you already use: ask for a short, and the render comes back finished. See how to connect a video pipeline to your agent.
Manual install (advanced)
If you prefer manual installation:
- Click the Download .zip button above to grab
peer-review-1.0.0.zipdirectly from our S3 mirror. - Unzip into
~/.openclaw/skills/peer-review/(create the directory if it does not exist). - Restart OpenClaw Easy (or the OpenClaw CLI gateway) so the new skill is discovered.
Frequently asked questions
How do I install Peer Review?
Install Peer Review in the OpenClaw Easy desktop app by opening the Skills panel, searching for peer-review, and clicking Install. From a terminal you can run: openclaw skills add peer-review. Either way the skill is placed in ~/.openclaw/skills/peer-review/.
Is Peer Review free?
Yes. Peer Review is free and open-source, distributed under the Apache-2.0 license through ClawHub. No account or payment is required to download or run it.
What does Peer Review do?
Multi-model peer review layer using local LLMs via Ollama to catch errors in cloud model output.
Related: more developer tools skills
If Peer Review looks useful, you may also want to check out other developer tools skills in the OpenClaw catalog:
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Free, open-source · Apache-2.0 · Works with Claude, ChatGPT, Gemini, or local Ollama models