Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/lynxlangya/knowject/knowject-rag-evalnpx skills add lynxlangya/knowject --skill knowject-rag-evalgit clone --depth 1 https://github.com/lynxlangya/knowjectWrote 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.
[](https://agentmods.dev/skills/lynxlangya/knowject/knowject-rag-eval)<a href="https://agentmods.dev/skills/lynxlangya/knowject/knowject-rag-eval"><img src="https://agentmods.dev/badge/skills/lynxlangya/knowject/knowject-rag-eval.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00148 | $0.01875 |
| Opus 5 | $0.00074 | $0.00937 |
| Sonnet 5 | $0.00030 | $0.00375 |
| Haiku 4.5 | $0.00015 | $0.00187 |
Grade A, and why
knowject-rag-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 4d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 830 B
- README.md 2.5 KB
- references/eval-file-contract.md 2.5 KB
- references/eval-taxonomy.md 1.5 KB
- references/examples/rag-eval-cases.expected.yaml 1.7 KB
- references/examples/rag-eval-report.expected.md 1.2 KB
- references/source-selection.md 1.1 KB
- scripts/validate-rag-eval-cases.py 6.4 KB runs code
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.
- 4d ago First seen · 183 lines · 148 tokens per session scan A 568f9738d165
knowject-rag-eval is a skill published in the GitHub repository lynxlangya/knowject (56 stars, last pushed 3mo ago), with no licence file. It adds 148 tokens to every session and 1,875 once invoked, about $0.0007 per session on Opus 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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