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 agentmods add skills/unitarylab/quantum-practices/vqcnpx skills add unitarylab/quantum-practices --skill vqcgit clone --depth 1 https://github.com/unitarylab/quantum-practicesWrote 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/unitarylab/quantum-practices/vqc)<a href="https://agentmods.dev/skills/unitarylab/quantum-practices/vqc"><img src="https://agentmods.dev/badge/skills/unitarylab/quantum-practices/vqc.svg" alt="Measured on agentmods" 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 | $0.00061 | $0.03996 |
| Opus 5 | $0.00030 | $0.01998 |
| Sonnet 5 | $0.00012 | $0.00799 |
| Haiku 4.5 | $0.00006 | $0.00400 |
Grade A, and why
vqc 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 5d 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
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
2 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.
- 5d ago First seen · 257 lines · 61 tokens per session scan A 6e56ececf1cf
vqc is a skill published in the GitHub repository unitarylab/quantum-practices (18 stars, last pushed 22d ago), with no licence file. It adds 61 tokens to every session and 3,996 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
bio-workbench
生信分析工作台（dsh-science-workbench）的可复现分析约定。当用户要创建或维护一个可复现的生信分析项目、用 bioruncell 跑分析并出图、登记产物 provenance、对图做结构化反馈并重画（反馈→改代码→重跑→派生新版本）时使用此 skill。触发词：生信项目、分析工作台、bioruncell、可复现出图、manifest、cell 契约、反馈重画。.
ito-compute
Query live GPU inventory, submit an authenticated Itô fixed-rate RFQ, inspect RFQ or procurement status, revoke device credentials, and run explicitly gated node qualification through the separately installed canonical CLI. Use when a user asks to find H100/H200 capacity, request a fixed compute rate, check Itô…
ito-inference
Inspect the availability of model serving on a completed Itô compute booking and, when the canonical backend becomes available, hand off an explicitly confirmed serving manifest. Use after ito-compute has booked GPU nodes and the user asks for an OpenAI-compatible endpoint, ito-serve, hosted Kimi, or self-hosted…
ito-training
Inspect the availability of ML training on a completed Itô compute booking and, when the canonical backend becomes available, hand off an explicitly confirmed training manifest. Use after ito-compute has booked GPU nodes and the user wants pre-training, fine-tuning, or RL on that metal. ECC implements no training…
dev-qa
开发与质量阶段——开发实现 + QA 测试 + 安全审计（职责分离）。流水线第 4 阶段，覆盖环节⑤⑥⑦。设计确认后自动调用。.
promotion
宣传与冷启动阶段——默认不生成任何宣传物料，只询问用户要不要做、做什么样的；未获明确答复就直接跳过。流水线第 6 阶段，覆盖环节⑩。部署完成后自动调用。.