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 bayeslabs-rsi/Svatah --skill web_searchgit clone --depth 1 https://github.com/bayeslabs-rsi/SvatahWrote 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/bayeslabs-rsi/svatah/web_search)<a href="https://agentmods.dev/skills/bayeslabs-rsi/svatah/web_search"><img src="https://agentmods.dev/badge/skills/bayeslabs-rsi/svatah/web_search/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/bayeslabs-rsi/svatah/web_search"><img src="https://agentmods.dev/badge/skills/bayeslabs-rsi/svatah/web_search.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 | $0.00013 | $0.00248 |
| Opus 5 | $0.00006 | $0.00124 |
| Sonnet 5 | $0.00003 | $0.00050 |
| Haiku 4.5 | $0.00001 | $0.00025 |
Grade A, and why
web_search 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 11d 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.
What it actually says
Web Search Skill
Purpose
Search the web to find information, best practices, latest techniques, and solutions relevant to the current optimization experiment.
When to Use
- During
/sva:researchto understand best practices and latest methods for the target domain - During
/sva:optimizewhen subagents need external knowledge to form better hypotheses - When encountering unfamiliar error patterns or library behaviors
- When looking for state-of-the-art approaches to beat the current frontier
How to Use
Run:
python -c "from sva.web_search import search; import json; print(json.dumps(search('{QUERY}', max_results=5), indent=2))"
Replace {QUERY} with your search query. Results include title, URL, and snippet.
If TAVILY_API_KEY is set, the skill uses Tavily. Otherwise it falls back to DuckDuckGo.
Rules
- Keep queries specific and technical
- Use results to inform hypotheses, not to copy-paste code blindly
- Always cite sources in experiment annotations
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.
- 11d ago First seen · 33 lines · 13 tokens per session scan A 774b5c6f45f9
web_search is a skill published in the GitHub repository bayeslabs-rsi/Svatah (3 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 13 tokens to every session and 248 once invoked, about $0.0001 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 2026-08-31.
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