Borrowing it
Nothing to install: this file belongs to lizhiyao/oh-my-knowledge. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lizhiyao/oh-my-knowledge/main/.agents/skills/omk/SKILL.mdgit clone --depth 1 https://github.com/lizhiyao/oh-my-knowledgeWrote 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/lizhiyao/oh-my-knowledge/omk)<a href="https://agentmods.dev/skills/lizhiyao/oh-my-knowledge/omk"><img src="https://agentmods.dev/badge/skills/lizhiyao/oh-my-knowledge/omk/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/lizhiyao/oh-my-knowledge/omk"><img src="https://agentmods.dev/badge/skills/lizhiyao/oh-my-knowledge/omk.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 | $0.00172 | $0.06817 |
| Opus 5.5 | $0.00069 | $0.02727 |
| Sonnet 5.5 | $0.00034 | $0.01363 |
| Haiku 4.5 | $0.00017 | $0.00682 |
Grade B, and why
omk scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
Codex 是 omk 的一等 runtime。未显式指定执行器时,`omk eval` / `doctor` / `sample` / `evolve`,以及 `omk observe inbox --llm-enhanced-review`,会自动选择 `codex`,从 `$CODEX_HOME/config.toml` 或 `~/.codex/config.toml` 解析模型(顶层 `profile` 指向某个 profile How it starts
The opening of the file, as written. The whole thing — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OMK — Observe. Measure. Know.
你是 OMK 的智能代理。帮助用户按当前任务复用有来源的历史知识,或观测真实表现、受控测量和改进 AI 应用的知识(prompt / RAG / skill / agent / workflow)。知识读取、任务采用与效果验证分别表达。
快捷知识反馈
$omk feedback 是显式提交当前知识反馈的快捷入口,不是 CLI 命令。命中该入口时优先处理本节,不执行后续的 which omk 环境检查:
- 从当前可见对话中定位最近一个明确的事实纠正、知识缺口或重复失败;
$omk feedback <补充说明>的补充文本只用于缩小和澄清该候选。 - 该显式调用本身视为用户确认。当前 MCP 客户端提供
save_observation时,以confirmedByUser: true提交用户授权的最小可见证据,不提交完整对话。 - 如果没有明确候选,或同时存在多个无法唯一判断的候选,只追问要记录哪一项;确认目标前不调用工具。
- 如果当前客户端没有
save_observation,明确说明 OMK MCP 尚未连接;不要回退为 CLI 写文件,也不要声称已经保存。 - 快捷入口只保存 observation,不自动复核、生成 sample 或写入 gold set。
从历史知识完成当前任务
用户希望参考已有知识回答问题、分析取舍或完成当前任务时,先阅读 按任务复用知识,首选 retrieve --format text 阅读原样正文,必要时用 JSON、show 和 source 精确读回。沿用用户已选的知识工作区或 CLI 已配置默认;按工具的单次可见容量调整读取量,确认没有截断后再采用。保留与任务有关的具体条件、反例、异议、请求与回应的历史关系及未决状态;建议中的限定紧随所约束的动作,并提供实际知识/来源引用。
当前 Source3 的来源引用必须包含 snapshotId、sourceVersion 两个字段。读回使用 source --snapshot <id> --source-version <digest>,可用结果在 snapshot 中;retrieve 的来源筛选也必须成对提供这两个参数。不可用或版本不符不能替换成另一个来源继续声称已核对。
命中此分支后不进入下方评测项目扫描:不查找 eval-samples,不因缺少 skill 或评测用例而运行 init、sample、doctor、eval 或 evolve。完成必要信息核对后直接交付当前任务;若用户另有评测或保存要求,再处理相应分支。CLI 不存在或不支持所需能力时,说明版本边界,不自动安装、升级或迁移数据。
维护已有知识
用户要求补充、纠正或更新已有认识时,先阅读 按任务维护知识。先检索并核对精确旧修订,只有具体缺口需要时才补选来源,再以现有 revise 追加完整新修订;通用方法与有日期/版本的案例分别表达。新来源不自动推翻旧认识,同名不证明同一知识,未确认目标时不自动归并。此分支不进入评测项目扫描,也不为更新已有知识自动重跑完整提炼。
从选定来源沉淀知识
用户要求从对话、项目材料或固定 Git 文件形成知识时,先核对已安装 CLI 是否支持 Source3、五阶段及来源复核入口;@next 不保证包含未发布 main。沿用已选工作区,不自动升级/迁移,也不进入下方评测项目扫描。已有相关知识时先按任务检索,区分需要补充旧认识与独立新案例,不按相同名称合并;明确更新已有知识时转上方维护分支。
- 可信宿主先固定有限
scope.json;选区只含获准逻辑 ID、相对路径与明确行/原记录范围,Git 先解析固定对象。模型、浏览器和来源文本不能扩大宿主授权。 preview-sources --input selections.json --scope scope.json --json零保存、零模型,核对完整选中片段及读/存/发送范围。消息选择纳入其原始记录中的可读事件,可能包括同条工具事件;未选材料不自动扩入,正文已有路径仍可能发送。capture --input preview.json --scope scope.json --json重建并核对固定预览,只保存。由saveManifest.items的精确 snapshotId/sourceVersion 生成{sources:[…]}输入;保存不授权模型发送。- 默认 workflow6 按来源清单 → 未核验写作稿 → 有据正文 → 来源覆盖 → 关联执行。先
prepare-generation --input sources.json --executor <executor> --model <model> --json,读完整 system/input/producer/模型和 requestDigest,再在已有发送授权内generate --confirm-request <digest>;首次只发送来源清单。 - 清单完成后,以同一 run 和来源再次
prepare-generation --run-id ...准备写作稿,再continue-stage --id ... --generation ... --confirm-request ...。写作稿完成后同样准备并确认正文,随后准备并确认来源覆盖。草稿与精确原件绑定,不作为来源;正文引用只能回完整原 Source3。每次只确认当前完整请求,不自动串发;零正文也查漏,不造占位正文。 - 以
source-ledger --id ... --revision ...、source-coverage-status --id ... --revision ...读精确结果;清单是可错建议,不是 Gold,覆盖可纠正清单自身遗漏。核对完整原来源、正文、排除/待定及每个未决项后,实际 Agent/人用review-source-coverage --input command.json记录完整 record_decision 或 revoke_decision command,不加 operation wrapper 或自报 actor。绑定精确分析/ledger/bodyRefs/SourceSet、CAS/幂等身份、理由及限制;缺可信实际 actor 时只读,不伪装真人。模型 all-covered/无 finding 不自动批准。 - 允许继续时,把精确 sourceCoverageDecisionRef 与 sources 放入输入,以同一 run 再
prepare-generation准备关联,然后单独确认。准备和 claim 前重验决定,失效不发送;派发后撤销保留原件,结果 pending。新正文须prepare-source-review --input {sources,bodyRefs,ledgerRef?}(通过 JSON 文件)重新覆盖和决定;完整来源不能从正文引文缩减。模型重建必须在--input提供{sourceCoverageDecisionRef},prepare/rebuild 沿用同一明确新 run,核对精确请求;手工维护不产生来源批准。 - 默认导入绑定 workflow 身份及排序去重的精确 SourceSet,复用完成、失败、未知日志;不靠新 run 绕过 unknown claim。
inspect/runs先查回执,resume零模型,未知不重发。富化完整输入超容量明确停止,不裁剪来源、清单或正文。 source-updates只读核对完整 SourceRef 及可选 replacementSelection,不保存、生成或改知识;固定 Git 不代表最新,部分清单不证明零影响。- 旧数据先保留原目录及匹配构建。
migrate-workflow仅支持健康 Source3/正文3/实体2/关联2/reviews1/tags 六类活动数据,按固定计划/摘要复制到独立目标,保留全部字节与身份;旧 journal5/来源清单/覆盖/auto 仅归档,既有 review/retain 不等于来源覆盖批准。library/carriers/entity-links 等不受支持内容先报告边界,不静默丢弃。Source2→3 保持独立映射流程。当前构建不提供自动调度或重新授权入口,只逐阶段确认。
What ships with it
3 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.
- yesterday Changed 0a5a34292152
- 2d ago Changed 5c65c8975d37
- 3d ago Changed · +7 lines 11f7012addba
- 5d ago Changed · +2 lines 7f39b6fa44e9
- 6d ago Changed · +24 lines 7a450dd2bbcb
- 8d ago Changed · +7 lines · +35 tokens per session de28f096f8ae
- 21d ago Changed 2439380164bc
- 28d ago Changed · -2 lines 3b05ecf84fcc
- 1mo ago Changed 922d8a262847
- 1mo ago First seen · 257 lines · 137 tokens per session scan B af632f8e30e9
omk is a skill published in the GitHub repository lizhiyao/oh-my-knowledge (24 stars, last pushed 2d ago), licensed MIT. It adds 172 tokens to every session and 6,817 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
omk compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| omklizhiyao/oh-my-knowledge | Skill | 24 | 2d ago | MIT | B | 172 | Claude Code, Codex |
| ai-engineering-toolkitsickn33/agentic-awesome-skills | Skill | 49k | yesterday | MIT | A | 47 | Claude Code |
| promptfoo-provider-setuppromptfoo/promptfoo | Skill | 27k | yesterday | MIT | A | 66 | Claude Code, Codex |
| prompt-masternidhinjs/prompt-master | Skill | 16k | 15d ago | MIT | B | 78 | Claude Code, Codex |
| flux2-lora-trainingAnastasiyaW/codex-claude-code-config | Skill | 214 | yesterday | MIT | A | 73 | Claude Code |
| refineSnowflake-Labs/cocoplus | Skill | 727 | 2d ago | MIT | A | 37 | Claude Code |
| deep-reviewAnastasiyaW/codex-claude-code-config | Skill | 214 | yesterday | MIT | A | 180 | Claude Code |