waypoint-bio
2 findings (1 high, 1 medium), mainly command injection. Most recent scan 2026-10-21.
Second scanner: NVIDIA SkillSpector
Independent static run, no language model, over the same files as the Cisco scan above. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
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.
- Package
- 8 files, 89 KB
- SKILL.md
- 1957 words, 12+ sections
- Code
- Python (2)
- License
- MIT
- Compatibility
- Requires Python 3.10+ and waypoint-bio; the documented compatibility stack uses Transformers 4.57.6 and PEFT 0.18.1. Network access and approved Hugging Face access are needed for Hub downloads, but not for local conversion. Training benefits from a GPU.
SKILL.md covers: Waypoint: Outpost Bio's Open Microbiome Foundation Models · Overview · When to use · Setup · The waypoint data format · Workflow · 1. Get your data into waypoint format · 2. Check vocabulary coverage before anything else · 3. Embed samples · 4. Fine-tune on your labels · classification · regression, with a categorical covariate one-hot appended to the pooled embedding
Findings
HIGH Python code block uses eval/exec Command Injectionbehavioral
Code block in references/python-api.md at line 137 contains potentially dangerous Python code.
references/python-api.md:137
model = AutoModel.from_pretrained(model_path).eval()MEDIUM Python code block executes shell commands Command Injectionbehavioral
Code block in references/python-api.md at line 228 contains potentially dangerous Python code.
references/python-api.md:228
subprocess.run([What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 16 KB |
references/cli-reference.md | doc | 11 KB |
references/compass-benchmark.md | doc | 7.9 KB |
references/data-preparation.md | doc | 11 KB |
references/python-api.md | doc | 10 KB |
references/upstream-review.md | doc | 5.3 KB |
scripts/profiler_to_waypoint.py | script | 18 KB |
scripts/vocab_coverage.py | script | 9.4 KB |
External hosts referenced
huggingface.co github.com www.biorxiv.org arxiv.org biom-format.org doi.org export.arxiv.org join.slack.com pypi.org www.outpost.bio
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Package contents recorded 2026-10-22 at commit 593377bb0487. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Bytecode analyzer.
- Pipeline analyzer.
- Correlation analyzer.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://github.com/K-Dense-AI/scientific-agent-skills
- Raw scan result: skills-waypoint-bio-scan.json
- Badge for your README:
- Request a re-scan