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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.
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.
npm i -g oh-my-knowledge@next
omk --version
omk studioOpen 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 Link sources in the library, revise outdated content, then explicitly generate and save an artifact.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.
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 v4 retains three historical references and shows two changes; v5 explicitly selects two currently retained items, while old content and references remain available.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.
- 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.
- Use OMK inside agents or DSH: install the official Skill, submit MCP feedback, or use the DSH host plugin.
- Verify an agent’s file changes: use independent checks in local task acceptance, or screen candidate versions with iterative improvement.
Starter cases check the workflow; UNDERPOWERED is expected. Release decisions need your own domain cases and independent evidence. Observation signals do not establish causality; passing an evaluation does not mean an artifact took effect or a version was published.
Documentation start · Website · CLI · Runtimes and executors · Settings and environment variables · Examples
Beta APIs and storage contracts may change. Knowledge stays in the explicitly selected workspace; execution evidence and machine preferences have their own storage scopes. Back up before upgrading, then establish new evidence through the migration guide.
Use a trusted local environment. Tasks, verifiers, and custom assertions execute local code and should be reviewed first; workspace copies are not security sandboxes. Inspect the material before a model call: source records and artifacts may contain sensitive information. The local report server has no authentication and should not be exposed publicly.
See GitHub Releases for release notes and CONTRIBUTING for the contribution workflow.