{ "ok": true, "resource": { "id": "RES_64F8DC593742", "resource_type": "skill", "canonical_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/waypoint-bio", "name": "waypoint-bio", "summary_en": "Use when working with Outpost Bio's open microbiome foundation models - the Waypoint checkpoints (Waypoint-6m, Waypoint-45m, Waypoint-170m), the Atlas pretraining corpus, the Compass eight-task benchmark, or the `waypoint` CLI from the `waypoint-bio` package. Covers embedding microbiome samples, fine-tuning on taxonomic abundance data, benchmarking a checkpoint on Compass, pretraining a GPT-2 model on taxonomic abundance profiles, and converting MetaPhlAn, Kraken2, QIIME 2, or MGnify abundance tables into waypoint format.", "summary_zh": "当使用Outpost Bio的开源微生物组基础模型时使用——Waypoint检查点（Waypoint-6m、Waypoint-45m、Waypoint-170m）、Atlas预训练语料库、Compass八任务基准，或来自`waypoint-bio`包的`waypoint` CLI。涵盖嵌入微生物组样本、在分类丰度数据上微调、在Compass上对检查点进行基准测试、在分类丰度配置文件上预训练GPT-2模型，以及将MetaPhlAn、Kraken2、QIIME 2或MGnify丰度表转换为waypoint格式。", "description_en": "This skill provides procedural knowledge for working with Outpost Bio's open microbiome foundation models, including the Waypoint checkpoints (6M, 45M, 170M parameters), the Atlas pretraining corpus, and the Compass benchmark. It covers embedding microbiome samples, fine-tuning on taxonomic abundance data, benchmarking on Compass, pretraining a GPT-2 style model on taxonomic abundance profiles, and converting abundance tables from MetaPhlAn, Kraken2, QIIME 2, and MGnify into the waypoint format. The skill is based on the SKILL.md file from the K-Dense-AI/scientific-agent-skills repository, which was inspected and found to contain detailed instructions, including setup, data format specifications, workflow steps, scientific caveats, and references. The skill declares an MIT license and requires Python 3.10+, the waypoint-bio package, network access, and a Hugging Face token with access to gated outpost-bio repositories. A GPU is recommended for pretraining and benchmarking.", "description_zh": "该技能提供了使用Outpost Bio的开源微生物组基础模型的操作知识，包括Waypoint检查点（6M、45M、170M参数）、Atlas预训练语料库和Compass基准。涵盖嵌入微生物组样本、在分类丰度数据上微调、在Compass上基准测试、在分类丰度配置文件上预训练GPT-2风格模型，以及将MetaPhlAn、Kraken2、QIIME 2和MGnify的丰度表转换为waypoint格式。该技能基于K-Dense-AI/scientific-agent-skills仓库中的SKILL.md文件，该文件经过检查，包含详细说明，包括设置、数据格式规范、工作流程步骤、科学注意事项和参考资料。该技能声明MIT许可证，需要Python 3.10+、waypoint-bio包、网络访问以及具有访问受限outpost-bio仓库权限的Hugging Face令牌。建议使用GPU进行预训练和基准测试。", "provider_name": "K-Dense-AI", "homepage_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/waypoint-bio", "endpoint_url": null, "source_url": "https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/waypoint-bio/SKILL.md", "icon_url": null, "cover_image_url": null, "capabilities": [ "data.embed", "model.finetune", "model.benchmark", "model.pretrain", "data.transform" ], "categories": [ "research", "data", "developer-tools" ], "industries": [ "health", "general", "software" ], "protocols": [ "agent-skills" ], "auth": { "type": "none", "required": false }, "pricing": { "model": "free" }, "input_schema": {}, "output_schema": {}, "metadata": { "content_language": "en", "translation_status": "original-only", "license": { "spdx_id": "MIT", "source": "https://github.com/K-Dense-AI/scientific-agent-skills/blob/1e5eeffbdad3749125afe7ab48a39694e27f181c/LICENSE" }, "github": { "repository": "K-Dense-AI/scientific-agent-skills", "owner": "K-Dense-AI", "path": "skills/waypoint-bio/SKILL.md", "commit": "1e5eeffbdad3749125afe7ab48a39694e27f181c", "stars": 42124, "forks": 3870, "updated_at": "2026-09-03T02:47:03Z" }, "skill": { "compatibility": "Requires Python 3.10+ with `waypoint-bio` (pulls torch, transformers, datasets, peft, scikit-learn). 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