synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 synthetic-sciences/openscience --skill waypoint-biogit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/waypoint-bio)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/waypoint-bio"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/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/synthetic-sciences/openscience/waypoint-bio"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/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 | $0.03734 |
| Opus 5.5 | $0.00052 | $0.01494 |
| Sonnet 5.5 | $0.00026 | $0.00747 |
| Haiku 4.5 | $0.00013 | $0.00373 |
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 — 100 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 — 298 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
6 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 Changed · +5 lines b536201a757e
- 1mo ago First seen · 293 lines · 130 tokens per session scan A 61adf4275ca0
waypoint-bio is a skill published in the GitHub repository synthetic-sciences/openscience (4,025 stars, last pushed today), licensed Apache-2.0. It adds 130 tokens to every session and 3,734 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 100 lines, and is treated as a copy.
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