ov-memory-troubleshoot
Diagnose OpenViking memory issues by tracing backward from a memory file to its archive memory_diff.json and, when needed, session messages. Read-only; use for…
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
$ npx -y skills add volcengine/openviking --skill openviking-memory --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/openviking-memoryContext preview
The summary Claude sees to decide when to auto-load this skill.
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
name: openviking-memory description: 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.
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:
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](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.
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.
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:
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.
resources.
`viking://~/` — your own user root — or shared reference material under `viking://resources/`), the optional `write` / `edit` tools cover that — see [references/optional-tools.md](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.
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.
Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.
Repo: volcengine/openviking
Diagnose OpenViking memory issues by tracing backward from a memory file to its archive memory_diff.json and, when needed, session messages. Read-only; use for…
GitHub 国内访问加速 skill,使用 githubproxy.cc 代理加速 GitHub 仓库克隆、文件下载、Raw 文件访问等操作。使用场景:(1) 需要 git clone GitHub 仓库时加速,(2) 下载 GitHub Release 文件、Raw 文件、Archive 压缩包时加速,(3)…
Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.