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 lat
Skills are meant to be used inside your own AI agent. Install it, run a quick smoke test, then ask your agent to apply it to your real task.
1
Install into your agentCopy the ClawHub install command and run it where your OpenClaw/agent environment is configured.
2
Run a smoke testUse the test prompt below to confirm the skill loads and understands the workflow before relying on it.
3
Use it in your own agentPaste your actual task into Manus, OpenClaw, Claude Code, Cursor, or another agent that supports skills.
I just installed the Peer Review skill. Please run a quick smoke test: explain what this skill can do, ask me for the minimum input it needs, then produce one small sample output for a realistic task.
