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数据与研究

waypoint-bio

k-dense-ai/scie…t-skills

Supports work 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.

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安装命令

npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill waypoint-bio

SKILL.md

Waypoint: Outpost Bio's Open Microbiome Foundation Models

Overview

Outpost Bio open-sourced three artefacts under Apache 2.0, described in Treloar et al., bioRxiv 2026.05.02.722381:

Artefact What it is Hugging Face
Waypoint GPT-2-style causal LMs over taxonomic tokens, 6M–170M params outpost-bio/Waypoint-6m, -45m, -170m
Atlas 539,308 microbiome samples scraped from MGnify (485,377 pretrain / 53,931 benchmark) outpost-bio/Atlas
Compass Eight downstream tasks over four studies outpost-bio/Compass

The unifying idea: a microbiome sample is a sentence. Each taxon is one token, tokens are ordered by descending abundance z-score, and the model is trained with next-token prediction. A pretrained checkpoint then supplies sample-level embeddings or a fine-tuning backbone for prediction tasks.

All of it is driven by one CLI, waypoint, with five subcommands: prepare-dataset, embed, finetune, benchmark, pretrain.

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