Agent skill · k-dense-ai
waypoint-bio
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.
What it needs
About 7k tokens when loaded.
What this skill does
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. When to use Embedding 16S/shotgun taxonomic profiles into fixed-size vectors for clustering, visualisation, or a downstream classifier. Fine-tuning a Waypoint checkpoint to predict a phenotype, treatment, or continuous readout from community composition. Scoring your own microbiome model against Compass so the number is comparable to the paper. Pretraining a taxonomic language model on Atlas or on your own corpus. Converting profiler output (MetaPhlAn, Kraken2/Bracken, QIIME 2, MGnify TSVs) into the input format these tools expect. Do not reach for this when you have fewer than ~1,000 labelled samples — see Scientific caveats. A random forest on relative abundances is the better tool there, and the paper says so. Setup Atlas, Compass, and every Waypoint checkpoint are gated. Access is auto-approved, but you must click through once per repo and then authenticate: 1. Request access on each repo page you need: Waypoint-6m, Waypoint-45m, Waypoint-170m, Atlas, Compass. 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/waypoint-bio