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 AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-proteinmpnngit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote 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/alterlab-ieu/alterlab-academic-skills/alterlab-proteinmpnn)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-proteinmpnn"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-proteinmpnn/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/alterlab-ieu/alterlab-academic-skills/alterlab-proteinmpnn"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-proteinmpnn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00173 | $0.01085 |
| Opus 5 | $0.00086 | $0.00543 |
| Sonnet 5 | $0.00035 | $0.00217 |
| Haiku 4.5 | $0.00017 | $0.00109 |
Grade A, and why
alterlab-proteinmpnn 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 11d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ProteinMPNN (fixed-backbone sequence design)
Overview
ProteinMPNN (Dauparas et al., Science 2022; dauparas/ProteinMPNN) solves the
inverse-folding problem: given a protein backbone (a 3D structure with no or a
placeholder sequence), it designs amino-acid sequences predicted to fold to that
backbone. It is fast, robust, runs on CPU, and is the standard "sequence" step between
backbone generation (alterlab-rfdiffusion) and structure validation
(alterlab-alphafold).
When to Use This Skill
Use this skill when the user wants to:
- Inverse-fold a backbone PDB into one or more candidate sequences.
- Redesign only selected positions while fixing the rest (partial design).
- Enforce symmetry by tying residues/chains so homo-oligomers get identical sequences.
- Bias the amino-acid composition (e.g. avoid cysteines) or use the soluble model.
- Produce the sequence step of a design → fold → score loop.
Does NOT Trigger
| Scenario | Use instead |
|---|---|
| Design a pocket/interface with a ligand, metal, or nucleic acid present | alterlab-ligandmpnn |
| Generate a new backbone (no starting structure) | alterlab-rfdiffusion |
| Refold a designed sequence to check it (validation) | alterlab-alphafold |
| Generative multimodal (sequence+structure+function) design | alterlab-esm |
Core Capabilities
1. Basic inverse folding
# Parse the PDB, then design sequences (dauparas/ProteinMPNN CLI — TODO(verify) flags/version).
# The parser lives in the repo's helper_scripts directory; run it by name:
python parse_multiple_chains.py --input_path=pdbs/ --output_path=parsed.jsonl
python protein_mpnn_run.py \
--jsonl_path parsed.jsonl --out_folder out/ \
--num_seq_per_target 8 --sampling_temp "0.1"
Lower --sampling_temp (e.g. 0.1) gives conservative, high-confidence designs; higher
temperatures increase diversity. Output FASTA headers carry the model score (lower =
better) and sequence recovery.
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.
- 11d ago First seen · 85 lines · 173 tokens per session scan A 7d4d4df13fa3
alterlab-proteinmpnn is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 6d ago), licensed MIT. It adds 173 tokens to every session and 1,085 once invoked, about $0.0009 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 2026-08-30.
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