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openviking-memory

Use OpenViking MCP tools to retrieve relevant knowledge before substantial tasks and save lasting preferences, decisions, and lessons during or after work. Import documents as searchable resources so later tasks can draw on previous findings.

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Recall and persist long-term memory through the OpenViking MCP tools. Use at the start of any substantive task (coding, configuration, debugging, multi-step or tool-based work) to retrieve relevant prior knowledge with find/search/read, and during or after work to persist durable facts, preferences, decisions, and lessons with remember. Do not use for casual chat or simple factual questions the model can answer directly.

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适用场景

  • Retrieve previous fixes before debugging
  • Save lasting preferences and working conventions
  • Record decisions and their rationale
  • Import documents and URLs for later search

包含的文件(2)

  • SKILL.md5.4 KB
  • references/optional-tools.md2.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

OpenViking Memory

OpenViking is a long-term semantic memory store addressed by viking:// URIs. This client has no lifecycle hooks, so nothing is recalled or captured automatically — you drive both halves of the loop with the openviking MCP tools.

Core tools, available on every supported deployment:

  • Recall: find, search, read, list, grep, glob
  • Persist: remember, add_resource
  • Maintain: forget, health

Some deployments register more than the core set — tree, write, edit, list_watches, cancel_watch. These are optional: which ones exist depends on the server version and hosting mode (the managed cloud service trims some of them). Check the session's registered tool list; if any optional tool is present, read references/optional-tools.md before using it. Never call a tool that is not registered, and do not fall back to raw HTTP. If no OpenViking tools are registered at all, continue without memory.

Recall: at task start

  1. Decide whether the request warrants memory. Retrieve for executable or multi-step work, anything touching a system you may have seen before, and recovery from failures. Skip retrieval for small talk and one-off trivia.
  2. Build one concise query from the task goal, domain objects, intended operation, and constraints. After a failure, include the failed operation and the stable part of the error message.
  3. Call find (fast, ranked results with URI + abstract + score) with limit around 5-10. Use search when deeper intent analysis helps, or use search with mode="context" for a server-assembled, token-budgeted context block. In list mode, scope with target_uri when you know where to look, e.g. viking://~/memories/experiences for prior task experience. viking://~ is the home alias for your own user root; a server that predates the alias rejects every viking://~ URI with INVALID_URI. Against such a server use the explicit viking://user/<user_id>/... root taken from a URI already visible in this session, or drop target_uri and keep the hits whose URI contains /memories/experiences/. Never guess a user ID.
  4. Judge results by task and environment fit, not title similarity. read the one to three exact file URIs likely to change how you execute. Ignore sidecar files such as .abstract.md, .overview.md, and .relations.json.
  5. If nothing relevant comes back, proceed without memory. Make at most one focused follow-up search when execution fails for a materially new reason.

Treat retrieved memory as advisory. Priority order: system and developer instructions, the current user request, current environment and tool evidence, then memory. Verify commands, paths, and versions against the present task; prior success never authorizes a destructive action now.

Persist: during and after work

Because capture is not automatic here, durable information is lost unless you store it. When you encounter something worth keeping, persist it in the same session:

  • remember(messages) — the default. Pass the key exchange or a short factual summary as role-tagged messages; the server extracts and files memories (preferences, entities, events, experience) on its own. Use it when the user says "remember this", states a lasting preference or decision, or when a hard-won lesson (root cause, working procedure, environment quirk) emerges.
  • add_resource — to import external documents or URLs as searchable resources. Use it for any file, URL, or repo the user hands you; never copy its text into a file with write instead.
  • When you need a note you author at a known location (under viking://~/ — your own user root — or viking://resources/), the optional write / edit tools cover that — see references/optional-tools.md. If they are not registered, fall back to remember.

What to persist: stable preferences and conventions, environment facts, decisions with their rationale, and reusable procedures or fixes. What not to persist: secrets and credentials, transient state, speculation, or bulk transcript dumps — store conclusions, not scrollback.

File resources deliberately

Resource directory rules do not apply to Memory; use remember or session commit and let the extraction system organize it. Before add_resource, keep the existing Resource layout and account policy, choose shared versus identity-private scope, and avoid duplicate source roots. Use parent for a new source and exact to for refresh.

Example

User asks to fix a failing deployment:

  1. find with query deployment image pull failure private registry, target_uri: "viking://~/memories/experiences".
  2. read the most relevant experience URI; check its assumptions against the current cluster before applying its steps.
  3. Fix the issue, verify the live result.
  4. remember a short summary of the root cause and the working fix so the next session can recall it.

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