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 xyva-yuangui/XyvaClaw --skill rag-knowledge-basegit clone --depth 1 https://github.com/xyva-yuangui/XyvaClawWrote 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/xyva-yuangui/xyvaclaw/rag-knowledge-base)<a href="https://agentmods.dev/skills/xyva-yuangui/xyvaclaw/rag-knowledge-base"><img src="https://agentmods.dev/badge/skills/xyva-yuangui/xyvaclaw/rag-knowledge-base/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/xyva-yuangui/xyvaclaw/rag-knowledge-base"><img src="https://agentmods.dev/badge/skills/xyva-yuangui/xyvaclaw/rag-knowledge-base.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 | $0.00000 | $0.00360 |
| Opus 5 | $0.00000 | $0.00180 |
| Sonnet 5 | $0.00000 | $0.00072 |
| Haiku 4.5 | $0.00000 | $0.00036 |
Grade A, and why
rag-knowledge-base 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 11d 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.
What it actually says
RAG Knowledge Base
本地 RAG 知识库系统，支持文档解析、向量存储、混合检索和增强生成。
功能
- 文档解析：PDF / Word / Excel / Markdown / 纯文本
- 智能分块：递归字符分割，保留语义完整性
- 向量存储：ChromaDB 本地持久化
- 混合检索：向量语义检索 + 关键词 BM25
- Embedding：DashScope text-embedding-v3（通义千问）
- CLI 管理：add / remove / list / query / stats
使用方式
# 添加文档到知识库
python3 scripts/kb_manager.py add /path/to/document.pdf --collection default
# 添加整个目录
python3 scripts/kb_manager.py add /path/to/docs/ --collection project-docs
# 查询知识库
python3 scripts/kb_manager.py query "什么是 OpenClaw 的架构？" --top-k 5
# 列出所有 collection
python3 scripts/kb_manager.py list
# 查看统计
python3 scripts/kb_manager.py stats
# 删除文档
python3 scripts/kb_manager.py remove --source /path/to/document.pdf
# 导出检索结果为上下文（供 LLM 使用）
python3 scripts/kb_manager.py context "用户问题" --max-tokens 4000
配置
环境变量 DASHSCOPE_API_KEY 用于 embedding 模型调用。
数据存储在 ~/.openclaw/data/chromadb/。
What ships with it
2 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.
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.
- 11d ago First seen · 43 lines · 0 tokens per session scan A 1ec5ca4d1970
rag-knowledge-base is a skill published in the GitHub repository xyva-yuangui/XyvaClaw (21 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 360 tokens. 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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