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hermes-rubric MCP Server

hermes-labs-ai/hermes-rubric

Score: 46.8 Rank #20805 MCP Server
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Evidence-first LLM-as-judge scoring for AI artifacts — papers, PRs, prompts, cold emails: synthesizes a rubric, collects quoted-evidence citations, scores only against that evidence, and hedges on thin evidence. Every dimension ties to a file:line or quote, with reproducibility receipts. 7 backends.

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Overview

hermes-labs-ai/hermes-rubric is a Python tool licensed under Apache-2.0. Evidence-first LLM-as-judge scoring for AI artifacts — papers, PRs, prompts, cold emails: synthesizes a rubric, collects quoted-evidence citations, scores only against that evidence, and hedges on thin evidence. Every dimension ties to a file:line or quote, with reproducibility receipts. 7 backends.

Ranked #20805 out of 24840 indexed tools.

Actively maintained with commits in the last week.

Ecosystem

Python Apache-2.0

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 2

2 stars → early stage

Freshness 25% today

Last commit today → actively maintained

Issue Health 25% 0%

0/1 issues closed → many open issues

Contributors 10% 0

0 contributors → solo project

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads N/A
Forks 2
Description Detailed
License Apache-2.0

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Issue Health high impact

You have 1 open vs 0 closed issues — triaging stale issues improves health

Contributors medium impact

Single-contributor projects carry bus-factor risk — welcoming contributors boosts confidence

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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AgentRank score for hermes-labs-ai/hermes-rubric
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topic:mcp

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