skill-xuzhougeng-wisp-science-evo2 · by Install
$ agentstack add skill-xuzhougeng-wisp-science-evo2 ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-xuzhougeng-wisp-science-evo2) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Evo 2 — DNA Language Model
Prerequisites
| Requirement | Minimum | Recommended | | ----------- | ------- | ---------------- | | Python | 3.11 | 3.12 (/
For the full result dict (`output_files`, `remote_workdir`, …), re-enter the
kernel: `c.attach_job(job_id).result()` then `c.close()`. See the
`remote-compute-ssh` / `remote-compute-modal` skill for the orchestration
details.
Inside `score_evo2.py`, point `HF_HOME` at the provider's weight-cache mount
(path is in `compute_details`) and set `HF_HUB_OFFLINE=1` so the loader
doesn't try to write `refs/` into a read-only mount. Weight footprint:
~15 GB (7B), ~80 GB (40B).
## Typical performance
| Task | 7B on H100 | Notes |
| --------------------------- | ---------- | --------------------------- |
| Model load (cached) | ~5-7 min | First call hydrates weights |
| `score_sequences`, 200×200bp| ~10-20 s | After load |
| `generate`, 1×512 nt | ~15 s | |
## Troubleshooting
| Symptom | Cause | Fix |
| ------------------------------------ | ------------------------------ | ------------------------------------------ |
| `Transformer Engine not installed` | No FP8 — falls back to bf16 | Informational only on non-H100; ignore |
| OOM on load | 40B on <80 GB GPU | Use `evo2_7b` or shard with `device_map` |
| HF tries to write `refs/main` | `HF_HOME` points at RO mount | Set `HF_HUB_OFFLINE=1` |
| `dtype mismatch` in `score_sequences`| Passing tensors not strings | Pass `list[str]`; the API tokenises for you |
---
**Next**: pair with `borzoi` to predict track-level effects of the same
variants.
## Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- **Author:** [xuzhougeng](https://github.com/xuzhougeng)
- **Source:** [xuzhougeng/wisp-science](https://github.com/xuzhougeng/wisp-science)
- **License:** Apache-2.0
- **Homepage:** https://wispscience.com/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.