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 swizardlv/openclaw_openviking_skill --skill openvikinggit clone --depth 1 https://github.com/swizardlv/openclaw_openviking_skillWrote 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/swizardlv/openclaw_openviking_skill/openviking)<a href="https://agentmods.dev/skills/swizardlv/openclaw_openviking_skill/openviking"><img src="https://agentmods.dev/badge/skills/swizardlv/openclaw_openviking_skill/openviking/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/swizardlv/openclaw_openviking_skill/openviking"><img src="https://agentmods.dev/badge/skills/swizardlv/openclaw_openviking_skill/openviking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00089 | $0.00617 |
| Opus 5.5 | $0.00036 | $0.00247 |
| Sonnet 5.5 | $0.00018 | $0.00123 |
| Haiku 4.5 | $0.00009 | $0.00062 |
Grade A, and why
openviking 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenViking Skill
Manage an OpenViking context database: index documents, semantic search, browse, and retrieve summaries.
Prerequisites
- Python 3.9+ with
openvikingpackage installed (pip install openviking) - Config file at
~/.openviking/ov.confwith valid embedding and VLM credentials - Environment variable
OPENVIKING_CONFIG_FILE=~/.openviking/ov.conf
If prerequisites are not met, guide the user through setup. See references/setup-guide.md.
Quick Reference
Index a file
python3 scripts/viking.py add /path/to/file.md
Index all files in a directory (recursive)
python3 scripts/viking.py add-dir /path/to/directory --pattern "*.md"
Semantic search
python3 scripts/viking.py search "query text" --limit 5
Browse resources
python3 scripts/viking.py ls [viking://resources/path]
Get summary
python3 scripts/viking.py abstract viking://resources/my_doc
python3 scripts/viking.py overview viking://resources/my_doc
Read full content
python3 scripts/viking.py read viking://resources/my_doc/section.md
Usage Notes
- Data directory: Defaults to
./openviking_datain the current working directory. Override with--data-dir. - File name collisions: OpenViking uses file names (not full paths) as URIs. Avoid indexing files with identical names from different directories simultaneously.
- VLM model: Use non-reasoning models (e.g.
meta/llama-3.3-70b-instruct) for the VLM. Reasoning models return content in the wrong field. - Embedding model:
nvidia/nv-embed-v1(symmetric, 4096-dim) works without extra parameters. Asymmetric models (e.g.nv-embedqa-e5-v5) requireinput_typewhich OpenViking doesn't pass.
When to Use Python API vs CLI Script
- CLI script (
scripts/viking.py): For quick one-off operations from the shell. - Python API: For complex workflows, batch operations, or integration into other scripts. See
references/python-api.md.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 74 lines · 89 tokens per session scan A 589ca26e344a
openviking is a skill published in the GitHub repository swizardlv/openclaw_openviking_skill (23 stars, last pushed 8mo ago), licensed Apache-2.0. It adds 89 tokens to every session and 617 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
AgentDB Memory Patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
pinecone-research
Agent RAG and long-term memory with Pinecone.
agent-v3-memory-specialist
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.
langchain
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG…
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
llm-wiki
Explain and apply the foundational LLM Wiki architecture and knowledge-management strategy. Use for setup, architecture, schema, or organization decisions; operational ingest/query/lint work uses dedicated skills.