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 ldpc-decodinggit 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/ldpc-decoding)<a href="https://agentmods.dev/skills/xi-zhao/openquantum/ldpc-decoding"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/ldpc-decoding/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/ldpc-decoding"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/ldpc-decoding.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 | $0.00028 | $0.00792 |
| Opus 5.5 | $0.00011 | $0.00317 |
| Sonnet 5.5 | $0.00006 | $0.00158 |
| Haiku 4.5 | $0.00003 | $0.00079 |
Grade A, and why
ldpc-decoding 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
LSD 纠错解码
原生 SDK 路径
本能力同时支持 LDPC 的原生公开 API。用户需要 自定义校验矩阵、错误模型、BP/OSD/LSD 译码器与批处理 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 ldpc-decoding 定位环境，按完整 SDK 工作流执行。模块导航：ldpc；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。
Agent 经 Harness 调用 ldpc_local 提供的 decode_ldpc_syndromes。本 Skill 负责选择和解释，不启动计算进程。
parityCheck 为 矩形二元矩阵，syndrome 数量由输入决定；每个 syndrome 长度等于校验数。假定独立同概率 bit flip。输出纠正向量、残余 syndrome 和是否满足校验；未给逻辑算符/真实错误，不能报告逻辑成功率或阈值。上游当前使用串行 LSD。
输入示例：
{
"parityCheck": [
[
1,
1,
0
],
[
0,
1,
1
]
],
"syndromes": [
[
1,
0
],
[
1,
1
]
]
}
调用前确认用户问题落在上述范围内；参数含糊且会改变物理结果时先澄清。接口不接受代码、路径、凭据或真实硬件任务。
计算 worker 默认不设置时间或输出大小上限；部署可配置资源预算，取消会终止本次计算进程组。连接层超时与硬件环境配置见资源配置。
计算前显式准备固定依赖；运行仍可能写 SDK 缓存，因此保留 workspace-write。工具未注册或连接已禁用时，检查设置中心；不要用通用执行工具绕过禁用。
依赖尚未准备时按仓库文档运行 npm run capability:paper-tools:setup；不要自行改版本或扩大输入边界来绕过失败。
所有结果保留完整输入、输入摘要、上游版本和依赖锁摘要。报告实际数值、物理假设、误差与限制；
scientificValidation=not_evaluated，比较数据不构成中央科学验收。失败时说明真实原因，不补造数据。
来源与接入范围见 论文能力说明。
依赖准备
运行前执行 node scripts/setup-paper-tools.mjs ldpc-decoding。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 3b7f0fbc2906
- 22d ago Changed · +4 lines 177c5bc23ec6
- 1mo ago Changed eccf23f85b9a
- 1mo ago First seen · 50 lines · 28 tokens per session scan A 7db43da58462
ldpc-decoding is a skill published in the GitHub repository xi-zhao/OpenQuantum (75 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 792 once invoked, about $0.0001 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 2026-09-13.
Other skills, from other repositories
academic-research
Search academic papers, scholarly articles, and research publications through SandBase. Use when asked for literature review, academic citations, scholarly research, paper discovery, or scientific evidence gathering.
rhylthyme
Schedule multi-step procedures that a person carries out against a clock, with parallel tracks, shared equipment and steps that end when the person says so. Use when turning a lab protocol, recipe, event run sheet or workout into a timed plan; when several procedures must share one instrument (one centrifuge, one…
memory-search-mcp
Long-term memory and context retrieval for AI agents. Semantic search across conversation history with care-weighted relevance.
gc-minimal-zine-poster-v0-3
Generate or analyze poetic paper-texture minimal zine posters with large negative space, a small editorial collage or focal element, experimental typography, and one clear color accent. Use when the user gives a theme, sentence, article, mood, object, photo, content brief, reference images, or an image folder and…
ask-sonner
Guide to Sonner, the React toast library — install and wire up the Toaster, pick the right toast() call, promise and loading toasts, updating, dismissing and persisting toasts, styling, theming and icons, positioning and multiple toasters. Use when working with Sonner or troubleshooting it — toasts that don't appear…
kill-ai-slop
Find and remove AI slop — the generic, machine-default visual and copy tics of vibe-coded products — from a web project. Use when the user asks to "kill AI slop", "de-slop", "remove the AI look", "make this not look AI-generated", or clean up a landing page / UI / docs that feels templated. Detects and fixes the…