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 QVerisAI/qveris-agent-toolkit --skill qverisgit clone --depth 1 https://github.com/QVerisAI/qveris-agent-toolkitWrote 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/qverisai/qveris-agent-toolkit/qveris)<a href="https://agentmods.dev/skills/qverisai/qveris-agent-toolkit/qveris"><img src="https://agentmods.dev/badge/skills/qverisai/qveris-agent-toolkit/qveris/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/qverisai/qveris-agent-toolkit/qveris"><img src="https://agentmods.dev/badge/skills/qverisai/qveris-agent-toolkit/qveris.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 57 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00058 | $0.01885 |
| Opus 5 | $0.00029 | $0.00942 |
| Sonnet 5 | $0.00012 | $0.00377 |
| Haiku 4.5 | $0.00006 | $0.00188 |
Grade A, and why
qveris scanned grade A with 1 finding 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 today.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.post( How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
For more detailed discovery query formulation, tool selection criteria, parameter handling, and error recovery, see the Agent Guidelines.
When to use QVeris
Choose among connected tools and QVeris using task fit, data quality/freshness, cost, user constraints, and call overhead. Use QVeris when at least one of these is true:
- the current environment lacks the required capability or live/structured data source;
- the correct provider or API is not known in advance;
- the task benefits from comparing providers on relevance, schema, quality, latency, or cost;
- the preferred provider is unavailable or fails and a fallback is needed;
- the user explicitly asks to discover or call a capability through QVeris.
Local computation and transformations do not need QVeris. For qualitative pages, tutorials, or factual browsing, use an available browsing tool unless structured API data or provider routing is required.
When external functionality is needed, follow this two-phase workflow. Discover, Inspect, Probe, and Call are independent protocol actions, not four mandatory steps.
Phase 1: Discover and Call Capabilities via MCP
- Identify what capability the user needs.
- Call
discoverwith a functionality description (not parameter names). Request only a few results unless comparison is necessary. - If the best discovery result already includes enough parameter guidance and cost information, call it directly with
call, passing parameters viaparams_to_tool. - Use
inspectonly when selection or valid request construction depends on contract details omitted by Discover, multiple candidates need comparison, or a host-managed metadata entry needs refreshing. - Use
probeonly when parameters need validation, a current quote is needed for a budget decision, or the user explicitly wants a preflight. Probe is not a prerequisite for Call; its quote is not a price reservation or user authorization. - Repeat or broaden the discovery query only if no suitable capability is found or a safe fallback is needed.
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.
- today Changed · +2 lines af7abd405f78
- 2d ago Changed · +31 lines · -22 tokens per session f30c2f8b48f3
- 11d ago First seen · 116 lines · 80 tokens per session scan A 201852cf8b71
qveris is a skill published in the GitHub repository QVerisAI/qveris-agent-toolkit (261 stars, last pushed yesterday), licensed MIT. It adds 58 tokens to every session and 1,885 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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