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 huaweicloud/huaweicloud-skills --skill huawei-cloud-openviking-embedding-switchgit clone --depth 1 https://github.com/huaweicloud/huaweicloud-skillsWrote 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/huaweicloud/huaweicloud-skills/huawei-cloud-openviking-embedding-switch)<a href="https://agentmods.dev/skills/huaweicloud/huaweicloud-skills/huawei-cloud-openviking-embedding-switch"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-openviking-embedding-switch/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/huaweicloud/huaweicloud-skills/huawei-cloud-openviking-embedding-switch"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-openviking-embedding-switch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00242 | $0.02834 |
| Opus 5 | $0.00121 | $0.01417 |
| Sonnet 5 | $0.00048 | $0.00567 |
| Haiku 4.5 | $0.00024 | $0.00283 |
Grade A, and why
huawei-cloud-openviking-embedding-switch 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 9d 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenViking Embedding Model Switch
概述
Switch the embedding model used by OpenViking to a local llama-server or any OpenAI-compatible endpoint, with proper vectordb index rebuild and sandbox-safe restart.
⚠️ Single-purpose skill — all operations go through the job-env-manager REST API (
http://127.0.0.1:8090). Never runopenviking-serverdirectly on the host.
OpenViking is an AI context database that uses vector embeddings for semantic search. Its embedding model is configured in ov.conf under the embedding.dense section. When switching to a different embedding model (especially one with a different vector dimension), the existing vectordb index must be deleted and rebuilt — otherwise OpenViking raises EmbeddingRebuildRequiredError on startup.
Architecture
OpenViking Embedding Model Switch
├── Detect current config (Read ov.conf embedding.dense section)
├── Validate endpoint (Check llama-server /v1/embeddings)
├── Modify ov.conf (Update provider, model, api_base, dimension)
├── Delete vectordb index (If dimension changed: rm -rf vectordb/context)
├── Restart server (Kill + exec, NOT stop/start)
└── Verify (Health + PID + dimension + log check)
┌─────────────────────────────────────────────────────┐
│ Host │
│ │
│ ┌─────────────┐ REST API ┌──────────────────┐ │
│ │ Agent │─────────────▶│ job-env-manager │ │
│ │ (this skill)│ │ :8090 │ │
│ └─────────────┘ └────────┬─────────┘ │
│ │ │
│ ┌──────────────────────────────┼──────┐ │
│ │ bwrap sandbox (openviking) │ │ │
│ │ ▼ │ │
│ │ ┌────────────────────────────────┐ │ │
│ │ │ openviking-server :1933 │ │ │
│ │ │ ├── ov.conf (embedding config)│ │ │
│ │ │ ├── vectordb/context/ │ │ │
│ │ │ └── viking/ (metadata) │ │ │
│ │ └────────────────────────────────┘ │ │
│ └──────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────┐ │
│ │ bwrap sandbox (llama) │ │
│ │ ┌────────────────────────────────┐ │ │
│ │ │ llama-server :18200 │ │ │
│ │ │ --embeddings --model bge-... │ │ │
│ │ └────────────────────────────────┘ │ │
│ └──────────────────────────────────────┘ │
│ │
│ Both sandboxes use --share-net, so 127.0.0.1 │
│ endpoints are mutually reachable. │
└─────────────────────────────────────────────────────┘
What ships with it
10 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.
- demo/example-input.json 688 B
- references/acceptance-criteria.md 1.2 KB
- references/config-reference.md 3.7 KB
- references/dataflow-diagram.md 2.5 KB
- references/guardrails.md 1.8 KB
- references/iam-policies.md 1.5 KB
- references/related-commands.md 2.0 KB
- references/troubleshooting.md 4.9 KB
- references/verification-method.md 2.8 KB
- scripts/switch-embedding-model.sh 8.6 KB runs code
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.
- 9d ago First seen · 214 lines · 242 tokens per session scan E 39bc775bbd6a
huawei-cloud-openviking-embedding-switch is a skill published in the GitHub repository huaweicloud/huaweicloud-skills (49 stars, last pushed yesterday), licensed MIT. It adds 242 tokens to every session and 2,834 once invoked, about $0.0012 per session on Opus 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 2026-09-03.
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