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 HorizonRobotics/OE-Skills --skill lightcompress-batch-quantizegit clone --depth 1 https://github.com/HorizonRobotics/OE-SkillsWrote 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/horizonrobotics/oe-skills/lightcompress-batch-quantize)<a href="https://agentmods.dev/skills/horizonrobotics/oe-skills/lightcompress-batch-quantize"><img src="https://agentmods.dev/badge/skills/horizonrobotics/oe-skills/lightcompress-batch-quantize.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 | $0.00023 | $0.00721 |
| Opus 5 | $0.00012 | $0.00360 |
| Sonnet 5 | $0.00005 | $0.00144 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
lightcompress-batch-quantize 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 8d 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
LightCompress 批量量化实验
概述
批量执行 = 循环调用 lightcompress-quant-explore skill。本 skill 只负责编排，不重复实现单实验逻辑。
Phase 1: 解析用户输入，展开实验列表
将用户输入展开为实验的笛卡尔积：models x methods x configs。
示例输入：
对 Qwen3-0.6B、Qwen2.5-7B 跑 RTN W8A8 和 GPTQ W4A8 量化
展开为 4 个实验：
- Qwen3-0.6B + RTN W8A8
- Qwen3-0.6B + GPTQ W4A8
- Qwen2.5-7B + RTN W8A8
- Qwen2.5-7B + GPTQ W4A8
Phase 2: 检查精度缓存
调用 quant-accuracy-cache skill 查询每个实验是否已有结果。
匹配规则：model.name + quant.method + quant.w_bit + quant.a_bit。
- 命中缓存：记录结果，从待执行列表中移除
- 未命中缓存：保留在待执行列表
Phase 3: 逐个调用 lightcompress-quant-explore
对待执行列表中的每个实验：
- 调用
lightcompress-quant-exploreskill 启动实验- 不指定 GPU，让子 skill 自动选择空闲 GPU
- 等待实验真正启动（通过
nvidia-smi确认显存占用上升） - 启动下一个实验
自动回答子 skill 的交互问题：
| 子 skill 问题 | 预设答案 |
|---|---|
| 开始实验 / 取消 | 开始实验 |
| 保存到缓存 / 跳过 | 保存到缓存 |
| 缓存冲突处理 | 保留两者 |
Phase 4: 汇总结果，生成对比表
所有实验完成后，汇总为对比表：
| 模型 | 方法 | 配置 | Pretrain PPL | Fake Quant PPL | PPL 变化 | 来源 |
|------|------|------|--------------|----------------|----------|------|
| Qwen3-0.6B | RTN | W8A8 | 21.30 | 22.15 | +0.85 | 实验 |
| Qwen2.5-7B | GPTQ | W4A8 | 7.00 | 7.45 | +0.45 | 缓存 |
调用 quant-accuracy-cache 保存新产生的实验结果。
与其他 Skill 的协作
| Skill | 关系 | 说明 |
|---|---|---|
lightcompress-quant-explore |
依赖 | 单实验执行引擎，负责 GPU 选择、配置生成、实验运行 |
quant-accuracy-cache |
协作 | 实验前查询缓存跳过重复，实验后保存结果 |
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.
- 8d ago First seen · 73 lines · 23 tokens per session scan A 85f4fdc34ba0
lightcompress-batch-quantize is a skill published in the GitHub repository HorizonRobotics/OE-Skills (19 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 23 tokens to every session and 721 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 2026-08-30.
Other skills, from other repositories
arboreto
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for…
torchdrug
Build and troubleshoot TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.
deepspot-m
Generate transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Use when you need spatial gene expression in log1p-CPM for 224x224 tiles at about 20x, want to query protein-coding genes by symbol instead of a fixed panel, or want to run prediction across a whole slide after tiling with…
pyhealth
Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer…
pick-a-pii-model
Select an on-device OpenMed PII model from the committed registry by language, runtime format, and size budget, then require recall validation before deployment. Use when an agent must choose a local PII detector for CPU, Apple Silicon, or a mobile export without relying on live model discovery.
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel…