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+---
+lineage_type: import
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9e8b0cb0/skills/waypoint-bio/SKILL.md
+upstream_sha: 9e8b0cb0
+imported_at: 2026-08-18
+prompt_class: catalogue
+upstream_changes: accepted
+name: waypoint-bio
+description: 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.
+license: MIT
+compatibility: Requires Python 3.10+ with `waypoint-bio` (pulls torch, transformers, datasets, peft, scikit-learn). Needs network access and a Hugging Face token with access granted to the gated outpost-bio repos. A GPU is strongly recommended for pretraining and benchmarking.
+metadata:
+  version: "1.0"
+  skill-author: K-Dense Inc.
+  upstream-version: "waypoint-bio 1.0.2 (PyPI); GitHub main 1.0.4"
+  last-reviewed: "2026-08-17"
+  openclaw:
+    primaryEnv: HF_TOKEN
+    envVars:
+      - name: HF_TOKEN
+        required: true
+        description: Hugging Face read token with access to the gated outpost-bio/Waypoint-*, outpost-bio/Atlas, and outpost-bio/Compass repos.
+---
+
+# 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](https://www.biorxiv.org/content/10.64898/2026.05.02.722381v2):
+
+| 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](#scientific-caveats). A random forest on relative abundances is the better tool
+there, and the paper says so.
+
+## Setup
+
+```bash
+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:
+
+1. Request access on each repo page you need: [Waypoint-6m](https://huggingface.co/outpost-bio/Waypoint-6m),
+   [Waypoint-45m](https://huggingface.co/outpost-bio/Waypoint-45m),
+   [Waypoint-170m](https://huggingface.co/outpost-bio/Waypoint-170m),
+   [Atlas](https://huggingface.co/datasets/outpost-bio/Atlas),
+   [Compass](https://huggingface.co/datasets/outpost-bio/Compass).
+2. Authenticate locally:
+
+   ```bash
+   hf auth login          # or: export HF_TOKEN=hf_...
+   ```
+
+A 401/403 from any subcommand almost always means access was never requested on that specific repo —
+a token alone is not enough. Use a read-scoped token. The tokenizer loads via
+`trust_remote_code=True`, so pin a `revision` if you need the remote code fixed across runs.
+
+## The waypoint data format
+
+Everything except `prepare-dataset` consumes **waypoint format**: a `.parquet` / `.csv` / `.tsv`
+whose rows are samples, with two aligned list-columns plus any label columns you need.
+
+| Column | Type | Notes |
+| --- | --- | --- |
+| `Taxa` | `list[str]` | Full lineage strings, `;`-separated: `k__Bacteria; p__Firmicutes; ...; g__Lactobacillus` |
+| `Relative Abundances` | `list[float]` | Same length as `Taxa`, same order |
+| *(any)* | scalar | Targets, covariates, or a `Split` column |
+
+Prefer parquet. CSV/TSV stores the lists as `repr` strings and round-trips through `ast.literal_eval`.
+
+**Give full lineages, not bare names.** The tokenizer extracts the genus segment (`g__`) from each
+lineage and falls back to the most specific higher rank when genus is missing. Bare names disable
+that fallback entirely.
+
+## Workflow
+
+### 1. Get your data into waypoint format
+
+If you already have a sample × taxa (or taxa × sample) abundance matrix with lineage labels:
+
+```bash
+waypoint prepare-dataset \
+    --input abundance_matrix.tsv \
+    --metadata sample_labels.csv \
+    --output dataset.parquet
+```
+
+Orientation is auto-detected from the first column header (`taxonomy`, `lineage`, `taxon`, `otu`,
+`#otu id` ⇒ taxa-as-rows); override with `--orientation`. Rows are normalised to sum to 1 unless you
+pass `--no_normalize`, and zeros are dropped unless you pass `--keep_zeros`.
+
+`prepare-dataset` cannot read profiler output directly — MetaPhlAn uses `|` separators, Kraken2
+reports encode the hierarchy as indentation, and QIIME 2/SILVA prefixes the domain `d__` instead of
+`k__` (which the tokenizer silently ignores). Use the bundled converter for those:
+
+```bash
+python scripts/profiler_to_waypoint.py \
+    --input merged_metaphlan.tsv --format metaphlan \
+    --output dataset.parquet
+
+python scripts/profiler_to_waypoint.py \
+    --input reports/*.kreport --format kraken \
+    --output dataset.parquet
+
+python scripts/profiler_to_waypoint.py \
+    --input feature-table.tsv --format qiime2 \
+    --output dataset.parquet
+```
+
+See `references/data-preparation.md` for every input layout, rank handling, and the `d__`/`|` gotchas.
+
+### 2. Check vocabulary coverage before anything else
+
+Waypoint's vocabulary is fixed at pretraining time from Atlas. Taxa absent from it become `<unk>` and
+are **silently dropped** by `waypoint embed`; the paper names this as the models' main limitation. A
+sample whose taxa are all out-of-vocabulary yields a degenerate `[BOS][EOS]` embedding.
+
+```bash
+python scripts/vocab_coverage.py --model outpost-bio/Waypoint-6m --data dataset.parquet
+```
+
+It reports per-sample and abundance-weighted coverage and flags samples below a threshold. Treat
+median abundance-weighted coverage under ~0.8 as a reason to re-examine your taxonomy labels before
+trusting any downstream number.
+
+### 3. Embed samples
+
+```bash
+waypoint embed \
+    --model outpost-bio/Waypoint-6m \
+    --data dataset.parquet \
+    --output embeddings.parquet
+```
+
+Output is indexed by sample ID with columns `dim_0 … dim_{H-1}` (`H` = 256 for 6m, 512 for 45m,
+768 for 170m). Defaults: `--pooling last_token`, `--batch_size 32`, `--max_length 512`, device
+auto-detected (`cuda` → `mps` → `cpu`).
+
+Keep `--pooling last_token` unless you have a reason to change it: it matches how the checkpoints
+were pretrained and how `benchmark` and `finetune` pool. `mean` is a reasonable alternative for
+unsupervised use; `first_token`/`cls_token` return the BOS position and carry little signal in a
+causal LM.
+
+### 4. Fine-tune on your labels
+
+```bash
+# classification
+waypoint finetune \
+    --model outpost-bio/Waypoint-45m \
+    --data dataset.parquet \
+    --output_dir outputs/ft_disease \
+    --task_type classification \
+    --target "Disease Status" \
+    --config configs/finetune_classification.yaml
+
+# regression, with a categorical covariate one-hot appended to the pooled embedding
+waypoint finetune \
+    --model outpost-bio/Waypoint-45m \
+    --data dataset.parquet \
+    --output_dir outputs/ft_degradation \
+    --task_type regression \
+    --target "Degradation Rate" \
+    --covariate_column Drug \
+    --config configs/finetune_regression.yaml
+```
+
+Config paths resolve against the bundled `waypoint_bio/configs/` tree, so `configs/...` works from
+any directory without cloning.
+
+Defaults worth overriding for small datasets: `warmup_steps: 1000` (drop to ~50 so warmup finishes
+before early stopping), `num_epochs: 1` in the shipped configs (raise it — early stopping on
+validation loss is what actually terminates training), and `use_lora: true` when VRAM is tight
+(~1% of parameters trained; adapters are merged back before saving, so the checkpoint stays a plain
+`AutoModel`).
+
+Splits default to a random 80/10/10. **Set `split_column` to a `Split` column whenever samples are
+correlated** — repeated measures, one donor sampled over time, technical replicates — or a random
+split leaks and the test score is meaningless.
+
+Outputs land in `--output_dir`: `best_model/` (loadable by `embed`/`benchmark`),
+`test_metrics.json`, `training_log.csv` + `.html`, and `finetune_results.json`.
+
+### 5. Benchmark on Compass
+
+```bash
+waypoint benchmark --model outpost-bio/Waypoint-6m --output_dir outputs/benchmark
+waypoint benchmark --model outputs/pretrain/best_model --tasks 1 6 --output_dir outputs/smoke
+```
+
+Fine-tunes a fresh head per task and writes `benchmark_results.json`. Classification tasks score
+macro-F1; the one regression task scores R² clamped to [0, 1]; `final_score` is the unweighted mean
+across tasks. Full task table, metric keys, and result-file schema: `references/compass-benchmark.md`.
+
+### 6. Pretrain
+
+```bash
+waypoint pretrain \
+    --model_config configs/models/gpt2-45m.yaml \
+    --pretrain_config configs/pretraining.yaml \
+    --output_dir outputs/pretrain_45m
+```
+
+Downloads Atlas, builds a taxonomic tokenizer from the corpus, computes per-token abundance
+mean/std for z-score ordering, then trains with next-token prediction and early stopping. Add
+`--data my_corpus.parquet` to pretrain on your own waypoint-format corpus instead, and
+`--max_samples N` for a smoke test.
+
+Nine architectures ship, from `gpt2-6m.yaml` (8 layers, 256 hidden) to `gpt2-170m.yaml` (24 layers,
+768 hidden); per-head dimension is fixed at 64 throughout. `references/cli-reference.md` has the
+full table and every config key.
+
+## Scientific caveats
+
+These are load-bearing. Ignoring them produces numbers that look fine and mean nothing.
+
+- **Below ~1,000 labelled examples, Waypoint underperforms a random forest on raw abundances.** The
+  paper's crossover against the RF baseline sits near **10,000** training examples. Fit the baseline
+  first; only adopt the transformer if it wins on your data.
+- **Out-of-vocabulary taxa are dropped, not flagged.** Every Compass dataset carries some. Run
+  `scripts/vocab_coverage.py` and report the coverage alongside your results.
+- **45M, not 170M, was the best benchmark model.** Pretraining loss keeps falling with scale, but
+  downstream Compass score does not — start at 6m or 45m and only scale up if it demonstrably helps.
+- **Genus-level tokenisation is the default**, so species-level distinctions are collapsed. Changing
+  `taxon_rank` requires re-pretraining, not just re-tokenising.
+- **Compositional data.** Relative abundances are constrained to sum to 1; differences in one taxon
+  induce apparent changes in others. This affects interpretation of any per-taxon attribution.
+- **Batch and study effects dominate microbiome data.** Atlas spans MGnify pipelines v1.0–v5.0 and
+  four sequencing modalities. Never let a study or run boundary coincide with your label boundary.
+- **Not a clinical or diagnostic tool.** The model cards state this explicitly.
+
+## References
+
+- `references/cli-reference.md` — every subcommand flag, every config key, the model-size table.
+- `references/compass-benchmark.md` — the eight tasks, filters, metrics, `benchmark_results.json` schema.
+- `references/data-preparation.md` — waypoint format, profiler conversions, taxonomy string rules.
+- `references/python-api.md` — using the tokenizer, datasets, heads, and checkpoints from Python.
+
+## Scripts
+
+- `scripts/profiler_to_waypoint.py` — MetaPhlAn / Kraken2 / QIIME 2 / generic lineage tables → waypoint format.
+- `scripts/vocab_coverage.py` — tokenizer coverage report for a waypoint-format file.
+
+## Upstream
+
+Code [github.com/Outpost-Bio/waypoint](https://github.com/Outpost-Bio/waypoint) ·
+package `waypoint-bio` ·
+paper [bioRxiv 2026.05.02.722381](https://www.biorxiv.org/content/10.64898/2026.05.02.722381v2) ·
+community [Waypoint Slack](https://join.slack.com/t/outpostbio-waypoint/shared_invite/zt-3w6ivgtba-WJOCkdxiISxQpwVq9ZZxTA) ·
+contact `waypoint@outpost.bio`.
+
+Cite Treloar, N. J., Ur-Rehman, S., Yang, J., & Outpost Bio (2026). *Learning the Language of the
+Microbiome with Transformers.* bioRxiv. Per-artefact DOIs are listed at
+[outpost.bio/citations](https://www.outpost.bio/citations).