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 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/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.
<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="Reviewed on agentmods" width="80" 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 5.1 | $0.00148 | $0.01875 |
| Opus 5.5 | $0.00059 | $0.00750 |
| Sonnet 5.5 | $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 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 1mo 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 (12 stars, last pushed 5mo ago), with no licence file. It adds 148 tokens to every session and 1,875 once invoked, about $0.0006 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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generate-rag-dataset
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edd
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rag-knowledge
Work with the RAG knowledge base — ingest documents, run semantic search, manage collections, or add a sync source/connector (Google Drive, S3). Use when populating or debugging the knowledge base, tuning retrieval, or adding a new document source. This project uses {{ cookiecutter.vectorstore }} + {{…
rag-eval
Use when the user has a RAG (Retrieval-Augmented Generation) system and wants to evaluate its quality — separating retrieval issues from generation issues. Also use when the user mentions RAG evaluation, faithfulness checking, hallucination detection in RAG, retrieval quality, chunking optimization, or "is my RAG…