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 xi-zhao/OpenQuantum --skill deltakit-qecgit clone --depth 1 https://github.com/xi-zhao/OpenQuantumWrote 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/xi-zhao/openquantum/deltakit-qec)<a href="https://agentmods.dev/skills/xi-zhao/openquantum/deltakit-qec"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/deltakit-qec/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/xi-zhao/openquantum/deltakit-qec"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/deltakit-qec.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 | $0.00051 | $0.00731 |
| Opus 5.5 | $0.00020 | $0.00292 |
| Sonnet 5.5 | $0.00010 | $0.00146 |
| Haiku 4.5 | $0.00005 | $0.00073 |
Grade A, and why
deltakit-qec 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 4d 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
Deltakit 纠错实验构建
原生 SDK 路径
本能力同时支持 Deltakit 的原生公开 API。用户需要 自定义码、电路、噪声、QPU 模型、采样与译码 时，由 Agent 在固定依赖环境中编写和运行任务代码，不限于下文便捷 Tool 的预设模型。
使用 node scripts/sdk-access.mjs inspect deltakit-qec 定位环境，按完整 SDK 工作流执行。模块导航：deltakit、stim、pymatching；导航不是 API 白名单。
下文关于模型、门集、输入与预算的限制描述便捷 Tool 的合同；原生 SDK 另受上游版本、可选依赖、宿主资源与用户授权约束。已禁用或被拒绝的动作不能借代码路径绕过。执行状态和科研验收分别报告。
调用 Harness 注册的 deltakit_local.run_deltakit_memory。适用于比较不同奇数宽高码片在 X/Z 存储实验中的表现，或查看 Deltakit 生成的实际含噪电路。
width和height是数据量子位码片尺寸，不是全部物理量子位数；实际资源以结果numQubits/numDetectors/numObservables为准。- 首版只用 Deltakit
ToyNoise(p)；它是合成噪声模型，不能将 p 解释为实测硬件保真度。轮数与 shots 由输入决定，保留 seed。 - Tool 生成电路后由固定 Stim 采样 detector 与 observable，再由 PyMatching MWPM 解码。报告失败数、固定 shots 分母和 Wilson 95% 区间；零失败时上界仍大于零。
- 返回的 Stim 电路及 SHA-256 是本次真实生成对象。不同码片/轮次/噪声模型之间比较时说明各自资源与统计预算。
- 单点或少数码距结果不能支持 threshold 或实时硬件性能主张。未开放云平台、专有解码器或泄漏服务。
- 当前 L1，
scientificValidation=not_evaluated；依赖须显式准备；计算可能写本地缓存。
版本、安装与验证见 接入说明。
码片宽高、存储轮数和采样数由调用方选择，适配器不额外设置人工规模上限。具体资源配置见本地计算说明。
依赖准备
运行前执行 node scripts/setup-paper-tools.mjs deltakit-qec。缺失、旧锁和已安装环境的处理见共同准备说明；Tool 不自动安装或升级依赖。
What ships with it
5 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.
- 4d ago Changed · +11 lines 280a623df32e
- 22d ago Changed · +4 lines 2eb3542c277e
- 1mo ago First seen · 20 lines · 51 tokens per session scan A c7a23c1645b8
deltakit-qec is a skill published in the GitHub repository xi-zhao/OpenQuantum (75 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 731 once invoked, about $0.0002 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 2026-09-15.
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