eval

eval is a skill for Claude Code from nicholasglazer/gnosis-mcp. It costs 71 tokens per session (2,295 once invoked), scanned A, original, MIT.

A command for measuring search quality on a document collection using test queries. It reports metrics such as Hit@5, MRR, precision, and nDCG, which compare retrieved results with expected answers.

In plain words
What is it for?
Running quick or full evaluations, comparing results with a saved baseline, saving a new baseline, and explaining likely tuning needs when scores are poor.
Why use it?
It shows whether retrieval improved or regressed after documents, settings, or models change, instead of relying on guesses.

Skill for Claude Code

Written for Claude Code: $ARGUMENTS substitution.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python tests/bench/bench_real_corpus.py \.

Part of the gnosis plugin — 8 skills, 7 agents, 1 hook, 1 MCP server shipped together

Good fit Running quick or full evaluations, comparing results with a saved baseline, saving a new baseline, and explaining likely tuning needs when scores are poor.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/nicholasglazer/gnosis-mcp
agentmods
npx agentmods add skills/nicholasglazer/gnosis-mcp/eval

Made for: Claude Code.

Or install gnosis, the plugin that ships this one along with the rest of its 8 skills, 7 agents, 1 hook, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for eval

README.md
[![agentmods](https://agentmods.dev/badge/skills/nicholasglazer/gnosis-mcp/eval/github.svg)](https://agentmods.dev/skills/nicholasglazer/gnosis-mcp/eval)
Your own site
<a href="https://agentmods.dev/skills/nicholasglazer/gnosis-mcp/eval"><img src="https://agentmods.dev/badge/skills/nicholasglazer/gnosis-mcp/eval/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for eval

Your own site · 80×15
<a href="https://agentmods.dev/skills/nicholasglazer/gnosis-mcp/eval"><img src="https://agentmods.dev/badge/skills/nicholasglazer/gnosis-mcp/eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 71 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,295 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Rogue Agent · line 96
    Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.
    Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00071 $0.02295
Opus 5.5 $0.00028 $0.00918
Sonnet 5.5 $0.00014 $0.00459
Haiku 4.5 $0.00007 $0.00230

Measured 1mo ago against content hash a81849bbd356, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

eval scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/eval/SKILL.md · 183 lines

How it starts

The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Eval

gnosis-mcp eval in ~10 lines of wrapper logic. Reports retrieval quality, compares to last known numbers, and points at /gnosis:tune if anything looks regressed.

Know what you are measuring. gnosis-mcp eval does not score your corpus. It loads nine hardcoded sample documents (SAMPLE_DOCS + SAMPLE_GIT_HISTORY_DOCS from the repository's tests/eval/ fixtures) into a temporary database and ignores GNOSIS_MCP_DATABASE_URL, so it prints identical numbers no matter what you have indexed. It is a harness smoke test, not a measurement of your docs. To score your own corpus, run the real-corpus benchmark (Step 1b) — that path is corpus-specific and reports nDCG@10, which gnosis-mcp eval never emits.

Different from /gnosis:tune: tune sweeps configurations looking for the best chunk size / embedder / rerank combo. Eval just reports current numbers. Run eval often (after each ingest, whenever the corpus changes). Run tune occasionally (after corpus shape change, new embedder, weekend of experimentation).

Usage

/gnosis:eval                    # full run — print numbers + interpret + compare
/gnosis:eval quick              # numbers only, no interpretation
/gnosis:eval save               # save current result as new baseline
/gnosis:eval diff               # compare current to last saved baseline

Mode: $ARGUMENTS


Step 1 — run the harness

gnosis-mcp eval --json

Parses to:

{
  "cases": 10,
  "hit_rate_at_k": 1.0,
  "mrr": 1.0,
  "mean_precision_at_k": 0.75,
  "k": 5
}

Those five keys are the whole contract — there is no ndcg_at_10 in this command's output, and no queries / hit_at_5 / mean_precision_at_5 (the real names carry _at_k plus the separate k field).

If the command errors, the cause is almost always a missing repository checkout: eval imports its fixtures from tests/eval/, so a plain pip install (no tests/ directory) exits 1 with an install-from-source hint. It does not need the [embeddings] extra, a golden file, or a populated database — it builds its own fixture in a temp DB. Don't proceed with empty numbers — fail loudly.

Read the full file on GitHub · 183 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +52 lines · +7 tokens per session a81849bbd356
  2. 2mo ago First seen · 131 lines · 64 tokens per session scan A f4cf26298589

Subscribe to this mod's changes

eval is a skill published in the GitHub repository nicholasglazer/gnosis-mcp (31 stars, last pushed 22d ago), licensed MIT. It adds 71 tokens to every session and 2,295 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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