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 knowledge-graphgit 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/knowledge-graph)<a href="https://agentmods.dev/skills/volcengine/openviking/knowledge-graph"><img src="https://agentmods.dev/badge/skills/volcengine/openviking/knowledge-graph/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/knowledge-graph"><img src="https://agentmods.dev/badge/skills/volcengine/openviking/knowledge-graph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector 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 | $0.00107 | $0.02482 |
| Opus 5.5 | $0.00043 | $0.00993 |
| Sonnet 5.5 | $0.00021 | $0.00496 |
| Haiku 4.5 | $0.00011 | $0.00248 |
Grade A, and why
knowledge-graph 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 25d 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.
- 25d ago Changed · +6 tokens per session 484c02000832
- 1mo ago Changed d27da40e82eb
- 1mo ago First seen · 225 lines · 101 tokens per session scan A 6f6b9476c529
knowledge-graph is a skill published in the GitHub repository volcengine/OpenViking (40,015 stars, last pushed today), licensed AGPL-3.0. It adds 107 tokens to every session and 2,482 once invoked, about $0.0004 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.
using-agent-brain
Expert Agent Brain skill for document search with BM25 keyword, semantic vector, hybrid, graph, and multi retrieval modes. Use when asked to "search documentation", "query domain", "find in docs", "bm25 search", "hybrid search", "semantic search", "graph search", "multi search", "find dependencies", "code…
configuring-agent-brain
Installation and configuration skill for Agent Brain document search system. Use when asked to "install agent brain", "setup agent brain", "configure agent brain", "setting up document search", "installing agent-brain packages", "configuring API keys", "initializing project for search", "troubleshooting agent brain"…
reward-design
A guide to defining the scores that an AI agent receives during trial-and-error training. It covers checks based on rules, human preferences, or another model, and distinguishes rewarding the final result from rewarding the steps taken.
kv-cache-design
A guide to arranging an AI agent's conversation so previously processed text can be reused. A KV cache stores intermediate results for earlier tokens, while a prompt cache reuses a stable beginning across requests.
agent-evolution
A guide to making an AI agent improve from its past runs. It covers how to judge results, extract reusable lessons, and decide whether to store them as knowledge, instructions, code, or model training.