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 naity/FM4Life --skill prott5git clone --depth 1 https://github.com/naity/FM4LifeWrote 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/naity/fm4life/prott5)<a href="https://agentmods.dev/skills/naity/fm4life/prott5"><img src="https://agentmods.dev/badge/skills/naity/fm4life/prott5.svg" alt="Measured on agentmods" 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 | $0.00146 | $0.02050 |
| Opus 5 | $0.00073 | $0.01025 |
| Sonnet 5 | $0.00029 | $0.00410 |
| Haiku 4.5 | $0.00015 | $0.00205 |
Grade A, and why
prott5 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ProtT5: Protein Language Model Embeddings
Overview
ProtT5 is a family of T5-based protein language models from Rostlab, trained on millions of UniRef protein sequences. The recommended model — ProtT5-XL-U50 — is an encoder-decoder transformer with 3B parameters whose encoder produces 1024-dimensional embeddings that outperform earlier BERT-based protein models and match or exceed ESM2 on most tasks.
Primary use cases:
- Per-residue and per-protein embeddings for downstream ML
- Protein classification (localization, membrane, function)
- Regression (stability, fitness, thermostability)
- Sequence similarity search
Installation
pip install transformers torch sentencepiece
Model Selection
| Model | Type | Params | HF ID | Best for |
|---|---|---|---|---|
| ProtT5-XL-U50 | T5 encoder | 3B | Rostlab/prot_t5_xl_half_uniref50-enc |
Best default — half-precision encoder only |
| ProtT5-XL-U50 (full) | T5 enc-dec | 3B | Rostlab/prot_t5_xl_uniref50 |
When you also need the decoder |
| ProtT5-XXL-U50 | T5 encoder | 11B | Rostlab/prot_t5_xxl_uniref50 |
Maximum accuracy, multi-GPU |
| ProtBERT-BFD | BERT | ~420M | Rostlab/prot_bert_bfd |
Faster/lighter, lower quality |
| ProtBERT | BERT | ~420M | Rostlab/prot_bert |
UniRef100 trained BERT |
Use prot_t5_xl_half_uniref50-enc as the default: it's encoder-only (no decoder weights), loads in half-precision, and is what all published benchmarks use.
Critical Preprocessing
This is the most important thing to get right. ProtT5 requires two preprocessing steps that ESM2 does not:
import re
def preprocess(sequence: str) -> str:
# 1. Map rare/ambiguous amino acids to X
sequence = re.sub(r"[UZOB]", "X", sequence)
# 2. Space-separate every amino acid (ProtT5 is character-level)
return " ".join(list(sequence))
sequence = "MKTAYIAKQRQISFVK"
processed = preprocess(sequence)
# → "M K T A Y I A K Q R Q I S F V K"
Skip either step and you will get garbage embeddings — the model was trained on this exact format.
What ships with it
2 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.
- 6d ago First seen · 185 lines · 146 tokens per session scan A 27587613e98e
prott5 is a skill published in the GitHub repository naity/FM4Life (2 stars, last pushed 5mo ago), licensed MIT. It adds 146 tokens to every session and 2,050 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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