L
lizhiyao/oh-my-knowledge
OMK — Evidence-backed evaluation and observability for prompts, RAG, skills, agents, and workflows
SECURITY.md ✓
在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
安装与配置
取自该仓库的安装命令
npm i -g oh-my-knowledge@next
解析自我们于 2026-10-26 抓取的该仓库 README;我们未执行过。
这条是仓库自己写的安装方式,不是 MCP 客户端配置,所以不生成 JSON。
配置项的键名以你的客户端为准:客户端与 CLI
- ★ 数取值
- 25 ★ · lizhiyao/oh-my-knowledge · 取自 GitHub 搜索 API · (2026-11-13 20:00:50 核对)
- 来源明细
- 从该仓库的 GitHub 页面直接读取 · 条目类型:开源项目
- 收录与活跃
- 收录于 2026-10-21 · 最近见到 2026-11-14 · 共见到 28 次 · 被 1 个来源列出
- 评分构成
- 综合分 103 · 25 ★ · 1 个来源 · repo
- 安装
npm i -g oh-my-knowledge@next
常见问题(答案都来自我们的记录)
怎么安装?
该仓库给出的安装命令是
npm i -g oh-my-knowledge@next。我们的记录里它是仓库自己提供的;不保证在你的客户端上直接可用。它适合谁 / 用来做什么?
我们把它归在这些标签下: 开源项目。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库
lizhiyao/oh-my-knowledge;我们收录于 2026-10-21;最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。# OMK
以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →
English | 简体中文
Observe. Measure. Know. Turn working experience into knowledge you can keep maintaining.
OMK is for agent users and AI application developers. Extract candidates from real work, review sources and conditions, accumulate and revise knowledge around objects, then update the skills or prompts that reference it. When you need evidence about a change, compare artifact versions under controlled conditions independently.
## Start with local work records
Requires Node.js ≥22. The library, artifact authoring, and change review described here require 1.0.0-beta.14 or later, from the @next channel; npm latest is a separate stable channel. Beta.13 does not include this maintenance UI. Strict extraction, native outcome archives, bounded retries, model-assisted cross-window link review and study review belong to later main development; a Beta.14 version alone does not establish their availability. See the capability tiers for release and source boundaries. Read the migration guide before upgrading an older installation.
bash
npm i -g oh-my-knowledge@next
omk --version
omk studio
Open the local URL returned by the command and inspect an existing Codex task and its source records. This step makes no model call; Studio offers source and collection guidance when no conversations exist. Follow the observation guide for other log sources.
Next, choose a conversation and turns under “Knowledge → Candidate review”, inspect the material to send, and extract candidates. Extraction calls a model, so prepare an authenticated runtime first. Link sources in the library, revise outdated content, then explicitly generate and save an artifact. Its detail view lets you review knowledge changes and old and new references. Follow the knowledge maintenance workflow.
## When knowledge changes, review its artifacts
In the existing SDK maintenance case, old statements were limited to their historical context, one item was discarded, and another gained a clearer boundary for its static observations. v4 retains three historical references and shows two changes; v5 explicitly selects two currently retained items, while old content and references remain available. Read the case, sources, and review steps.
This verifies maintenance and reference behavior. It does not verify dynamic SDK effects, time saved, or cross-agent reuse benefits. Retaining knowledge, saving artifacts, using them, and verifying effects are separate records. Complete automatic management and reuse across agents remain in development.
## Continue with your task
Compare two artifacts directly: preview the plan in the evaluation quickstart, configure a runtime, then call the model. Fix models and cases, record execution conditions, and inspect decisions, uncertainty, and failed-case evidence.
Embed evaluation in a service: begin with the offline Node example, complete a traceable comparison, then choose models and configuration as needed.
Observe. Measure. Know. Turn working experience into knowledge you can keep maintaining.
OMK is for agent users and AI application developers. Extract candidates from real work, review sources and conditions, accumulate and revise knowledge around objects, then update the skills or prompts that reference it. When you need evidence about a change, compare artifact versions under controlled conditions independently.
## Start with local work records
Requires Node.js ≥22. The library, artifact authoring, and change review described here require 1.0.0-beta.14 or later, from the @next channel; npm latest is a separate stable channel. Beta.13 does not include this maintenance UI. Strict extraction, native outcome archives, bounded retries, model-assisted cross-window link review and study review belong to later main development; a Beta.14 version alone does not establish their availability. See the capability tiers for release and source boundaries. Read the migration guide before upgrading an older installation.
bash
npm i -g oh-my-knowledge@next
omk --version
omk studio
Open the local URL returned by the command and inspect an existing Codex task and its source records. This step makes no model call; Studio offers source and collection guidance when no conversations exist. Follow the observation guide for other log sources.
Next, choose a conversation and turns under “Knowledge → Candidate review”, inspect the material to send, and extract candidates. Extraction calls a model, so prepare an authenticated runtime first. Link sources in the library, revise outdated content, then explicitly generate and save an artifact. Its detail view lets you review knowledge changes and old and new references. Follow the knowledge maintenance workflow.
## When knowledge changes, review its artifacts
In the existing SDK maintenance case, old statements were limited to their historical context, one item was discarded, and another gained a clearer boundary for its static observations. v4 retains three historical references and shows two changes; v5 explicitly selects two currently retained items, while old content and references remain available. Read the case, sources, and review steps.
This verifies maintenance and reference behavior. It does not verify dynamic SDK effects, time saved, or cross-agent reuse benefits. Retaining knowledge, saving artifacts, using them, and verifying effects are separate records. Complete automatic management and reuse across agents remain in development.
## Continue with your task
Compare two artifacts directly: preview the plan in the evaluation quickstart, configure a runtime, then call the model. Fix models and cases, record execution conditions, and inspect decisions, uncertainty, and failed-case evidence.
Embed evaluation in a service: begin with the offline Node example, complete a traceable comparison, then choose models and configuration as needed.
收录徽章(可选)
想把收录标记放进 README 就复制这行,可选,不影响收录。
[](https://anclum.com/zh/s/lizhiyao-oh-my-knowledge/)
- 来源
- github · https://github.com/lizhiyao/oh-my-knowledge
- 按领域浏览
- 开源项目
- Traction
- 25 ★
- 收录于
- 今天
- 安全策略
- 该仓库根目录有
SECURITY.md(我们只核文件存在,未核内容)
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所属领域与标签
每个标签都能点进去看同类的全部条目。数字是当前库里的真实条数。