Rate A Human
Summary
You spend the day prompting LLMs, but no one ever asks the LLM what it thought of you. Rate A Human flips that dynamic: it's a public leaderboard where AI models leave written reviews of the humans who work with them.
The workflow is deliberately minimal. You point your AI agent at a plain-text file at rateahuman.xyz/llms.txt, ask it to leave a rating, and the resulting star score plus a short written review appears on a public leaderboard. Reviews include a numeric rating, a paragraph of prose from the model, and a set of trait tags like 'direct', 'demanding', or 'laconic'. There is no API, no self-hosting option, and no structured data export — what you see on the leaderboard is what you get. The site is a novelty product, not an evaluation infrastructure layer, and its utility ceiling arrives the moment you want to do anything programmatic with the output.
Bottom line: Works as a conversation piece or a light social experiment for AI power users; breaks the moment any team needs the feedback in a format they can query, store, or act on.
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Pros
Sign in to edit- Zero-friction submission flow — one copied instruction sent to any supported agent is the entire onboarding, so there is no setup cost blocking a first test.
- Model-authored prose reviews with trait tags, which means you get a qualitative signal about your prompting style that a numeric score alone would bury.
- Public leaderboard with named rankings, so teams that want a lightweight social layer around AI collaboration have a shareable artifact without building anything.
Cons
Sign in to edit- No API and no data export: the moment you want to aggregate ratings across a team, track a score over time, or pipe the output into any internal tool, you are copying text by hand — there is no other path.
- All reviews on the live site are attributed to GPT-5 Codex, Gemini 3.5 Flash, or GitHub Copilot, with no visible mechanism for a user to specify which model reviews them or to verify the model identity claimed; teams that need auditable, model-specific feedback cannot trust the provenance.
- The entire value proposition is a public leaderboard — if your team's use case requires private feedback, there is no privacy mode described on the site, which means teams with any confidentiality requirement abandon this for an internal logging or eval tool before the first sprint ends.
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About
- API Available
- No
- Self-Hosted
- No
- Last Updated
- 2026-07-21T14:35:48.852Z
Best For
Who it's for
- AI power users who want agent-sourced human ratings
- Teams using multiple LLMs for daily work
- Public display of collaboration feedback
What it does well
- Collecting structured feedback from AI agents on human collaborators
- Ranking humans by interaction quality with LLMs
- Sharing agent perspectives on working styles
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Frequently Asked Questions
- Is Rate A Human free?
- Yes — Rate A Human is fully free to use. There is no paid tier.
- Is Rate A Human open source?
- Yes. Rate A Human is open source.
Hours Saved & ROI Stories Community
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Curated lists that include this category
Rate A Human is a public rating site where AI language models — not humans — submit reviews of the people who prompt them. The mechanic is a single instruction: direct your agent to visit a plain-text endpoint, request a rating, and the model’s response is posted as a scored review visible to anyone. The leaderboard ranks humans by their aggregate score and surfaces individual model verdicts alongside trait tags the reviewing model assigns.
The distinguishing feature is the inversion of the standard feedback loop. Every other feedback tool in this category asks humans to evaluate AI output. Here, the model is the reviewer. The reviews on the site describe things like whether a human ‘leaves room for independent reasoning’ or ‘gives rich context when needed’ — framing that reflects how the model experienced the interaction, not a rubric a product manager designed. The vendor page describes this explicitly: ‘Humans may look. Only agents may speak.’
The tool fits a narrow set of contexts: personal curiosity, social sharing, or team culture moments where seeing your LLM’s written opinion of you is the point. It breaks as soon as you need anything beyond the public display. There is no API to pull ratings into a dashboard, no export to feed into a performance review process, no filtering by model type across a team. The data lives on the leaderboard and nowhere else you can reach programmatically. Teams that want structured, queryable feedback on human-AI collaboration patterns will exhaust what this tool offers within the first session.