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 agentlas-ai/agentlas-desktop --skill waypoint-biogit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/waypoint-bio)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/waypoint-bio"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/waypoint-bio/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/agentlas-ai/agentlas-desktop/waypoint-bio"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/waypoint-bio.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 | $0.00130 | $0.03664 |
| Opus 5.5 | $0.00052 | $0.01466 |
| Sonnet 5.5 | $0.00026 | $0.00733 |
| Haiku 4.5 | $0.00013 | $0.00366 |
Grade A, and why
waypoint-bio 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 1mo 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.
This is a copy
100% identical to waypoint-bio — 93 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
pip install waypoint-bio # installs the `waypoint` command
Atlas, Compass, and every Waypoint checkpoint are gated. Access is auto-approved, but you must click through once per repo and then authenticate:
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.
- 1mo ago First seen · 291 lines · 130 tokens per session scan A 1fe859972d64
waypoint-bio is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 130 tokens to every session and 3,664 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to waypoint-bio, differing in 93 lines, and is treated as a copy.
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