OpenViking is an open-source context database for AI agents that stores memories, resources, and skills in a browsable virtual filesystem. Agents use it to find and load relevant context in layers while inspecting how retrieval decisions were made.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add volcengine/OpenViking --skill ov-experience-memorygit clone --depth 1 https://github.com/volcengine/OpenVikingWrote 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/volcengine/openviking/ov-experience-memory)<a href="https://agentmods.dev/skills/volcengine/openviking/ov-experience-memory"><img src="https://agentmods.dev/badge/skills/volcengine/openviking/ov-experience-memory/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/volcengine/openviking/ov-experience-memory"><img src="https://agentmods.dev/badge/skills/volcengine/openviking/ov-experience-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
- Socket pass
- Snyk pass
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 | $0.00078 | $0.01480 |
| Opus 5.5 | $0.00031 | $0.00592 |
| Sonnet 5.5 | $0.00016 | $0.00296 |
| Haiku 4.5 | $0.00008 | $0.00148 |
Grade A, and why
ov-experience-memory scanned grade A with 0 findings 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 19d ago.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 19d ago Changed 9490d86db2fc
- 1mo ago Changed · +21 lines faabfa8b0b79
- 1mo ago First seen · 116 lines · 78 tokens per session scan A c9fd2cc38619
ov-experience-memory is a skill published in the GitHub repository volcengine/OpenViking (40,447 stars, last pushed yesterday), licensed AGPL-3.0. It adds 78 tokens to every session and 1,480 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
wiki
Decision-sensitive repository knowledge retrieval, authoring, maintenance, and trust audits using wiki-manager when available or an explicit local-file fallback. Use when repository knowledge could change a non-trivial decision or when the user mentions wiki, knowledge base, packets, schema reports, or wiki tools.
causal-memory
Causal memory for agents — install/setup the causal-memory MCP server, then record decisions/outcomes and recall them before acting. Trigger when the user asks to install or set up causal-memory/agent memory, when causal-memory MCP tools are available and the agent faces a non-trivial decision (architecture, debugging…
gsd-resume-work
Resume work from previous session with full context restoration.
mindmemos-cli
Give an AI agent persistent, cross-session long-term memory through MindMemOS. Covers installing and authenticating the mindmemos CLI, the full command interface (add / search / get / update / delete / feedback / dreaming) with parameters and examples, guidance on which capability to use when, plus a Python SDK…
event-review
A weekly review process for event logs and leftover task records, turning repeated problems or useful lessons into proposed memory, knowledge, or skill entries.
compress
Compress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md. Trigger: /caveman:compress or "compress memory…