Alphafold Database Fetch And Analyze
google-deepmind/science-skills
Retrieve and analyze AlphaFold predicted structures for a protein.
Structure prediction with Protenix, an open AlphaFold3 reproduction.
$ npx skills add adaptyvbio/protein-design-skills --skill protenix -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install adaptyvbio/protein-design-skills protenix --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/protenix .claude/skills/protenix && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "protenix" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/protenix into .claude/skills/protenix/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protenix", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/protenixType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add adaptyvbio/protein-design-skills --skill protenix -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install adaptyvbio/protein-design-skills protenix --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/protenix .agents/skills/protenix && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "protenix" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/protenix into .agents/skills/protenix/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protenix", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add adaptyvbio/protein-design-skills --skill protenix -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install adaptyvbio/protein-design-skills protenix --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/protenix .cursor/skills/protenix && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "protenix" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/protenix into .cursor/skills/protenix/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protenix", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/adaptyvbio/protein-design-skills.git --path skills/protenix--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add adaptyvbio/protein-design-skills --skill protenix -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install adaptyvbio/protein-design-skills protenix --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/protenix .gemini/skills/protenix && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "protenix" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/protenix into .gemini/skills/protenix/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protenix", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install adaptyvbio/protein-design-skills protenixInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add adaptyvbio/protein-design-skills --skill protenix -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/protenix .github/skills/protenix && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "protenix" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/protenix into .github/skills/protenix/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protenix", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add adaptyvbio/protein-design-skills --skill protenix -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install adaptyvbio/protein-design-skills protenix --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/protenix .opencode/skills/protenix && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "protenix" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/protenix into .opencode/skills/protenix/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protenix", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
protenixStructure prediction with Protenix, an open AlphaFold3 reproduction.
Protenix is an agent skill from adaptyvbio/protein-design-skills. Structure prediction with Protenix, an open AlphaFold3 reproduction. Use this skill when: (1) Predicting complex structures with an AF3-class model, (2) Wanting an open alternative to AF3 alongside Boltz and Chai, (3) Validating designed binder-target complexes. For QC thresholds, use protein-qc. For ipSAE ranking, use ipsae.
Its SKILL.md is about 700 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Protein structure and design. The repository describes itself as: Claude Code skills for protein design. The licence is MIT.
Read from SKILL.md and the folder at commit 59dd633. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
gituvFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
github.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Protenix loads about 695 tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 227 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from adaptyvbio/protein-design-skills at commit 59dd633, republished under its MIT licence (© adaptyvbio). 227 words, ~695 tokens.
.claude/skills/protenix/SKILL.md (or your agent's skills folder).Protenix is ByteDance's open PyTorch
reproduction of AlphaFold3 (Apache 2.0). It is an AF3-class complex predictor, useful
next to boltz and chai for cross-checking designed complexes. Runnable through
biomodals.
Use Protenix-v2 for antibody-antigen complexes. The v2 model (464M params, April
2026) adds 9 to 13 percentage points of antibody-antigen accuracy over v1 at the
DockQ > 0.23 threshold and is more sample-efficient (v2 at 5 seeds exceeds v1 at 1000).
Select it with --model-name protenix-v2. For general complexes, the v1 base model is
fine.
| Requirement | Value |
|---|---|
| Runner | Modal (biomodals) |
| GPU | L40S (default; GPU env var) |
| Setup | See Getting started |
git clone https://github.com/hgbrian/biomodals && cd biomodals
printf '>protein|A\nMAWTPLLLLLLSHCTGSLSQ...\n' > target.faa
uv run --with modal modal run modal_protenix.py \
--input-faa target.faa \
--seeds 42 \
--no-use-msa| Parameter | Default | Description |
|---|---|---|
--input-faa | one required | FASTA input (or --input-json) |
--seeds | 42 | Comma-separated seeds |
--use-msa / --no-use-msa | MSA on | Pass --no-use-msa for single-sequence |
--model-name | v1 base | Set protenix-v2 for antibody-antigen complexes |
--use-mini | off | Switch to the smaller protenix_mini model |
--out-dir | ./out/protenix | Output directory |
| Need | Tool |
|---|---|
| Affinity head (small molecules) | boltz (Boltz-2) |
| Fastest, ligand support | chai |
| Open AF3 reproduction | protenix (v1 base) |
| Antibody-antigen complexes | protenix-v2 |
Ranking a shortlist across more than one predictor is more reliable than trusting a single model.
| Issue | Cause | Fix |
|---|---|---|
| Missing input error | No --input-faa/--input-json | Provide one |
| Slow run | MSA enabled | Add --no-use-msa |
| OOM | Large complex | Use --use-mini or a larger GPU |
Next: Rank with ipsae, filter with protein-qc.
© adaptyvbio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/protenix of adaptyvbio/protein-design-skills.
Open the folder on GitHubat commit 59dd633
We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in adaptyvbio/protein-design-skills, which our catalogue first saw on October 7, 2026.
Protenix next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Protenix this skilladaptyvbio/protein-design-skills | 163 | 2 repos | ~695 | Automated safety check: Pass | MIT | |
| Alphafold Database Fetch And Analyzegoogle-deepmind/science-skills | 3.2k | 2 repos | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Pymol VisualizationChatMol/ChatMol | 372 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Complexa Binder DesignNVIDIA-BioNeMo/bionemo-agent-toolkit | 478 | — | ~3.1k | Automated safety check: Notes | Apache-2.0 | |
| DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3k | Automated safety check: Notes | MIT | |
| Biopipelineslocbp-uzh/biopipelines | 109 | — | ~2.4k | Automated safety check: Pass | MIT |
google-deepmind/science-skills
Retrieve and analyze AlphaFold predicted structures for a protein.
ChatMol/ChatMol
Generate publication-quality molecular visualization images using PyMOL.
NVIDIA-BioNeMo/bionemo-agent-toolkit
Run a complete protein binder design campaign with NVIDIA Proteina-Complexa: resolve a target structure and hotspots from a name/sequence/PDB, co-design binder sequence+structure with reward-guided…
K-Dense-AI/scientific-agent-skills
Predicts how small molecules bind to a protein with DiffDock, covering batch docking, pose ranking by confidence and checks on the results; not for binding affinity.
locbp-uzh/biopipelines
Design and run computational protein and ligand workflows on a GPU: binder and enzyme design, de novo backbone generation, inverse folding and sequence redesign, structure prediction, protein-ligand…
NVIDIA-BioNeMo/bionemo-agent-toolkit
Orchestrate an end-to-end de novo protein binder design campaign against a protein target by composing BioNeMo NIM skills.
adaptyvbio/protein-design-skills
Validate protein designs using AlphaFold2 structure prediction.
adaptyvbio/protein-design-skills
End-to-end binder design using BindCraft hallucination. An agent skill from adaptyvbio/protein-design-skills.
adaptyvbio/protein-design-skills
All-atom protein design using BoltzGen diffusion model. An agent skill from adaptyvbio/protein-design-skills.
adaptyvbio/protein-design-skills
Structure prediction using Chai-1, a foundation model for molecular structure.
adaptyvbio/protein-design-skills
End-to-end guidance for protein design pipelines. An agent skill from adaptyvbio/protein-design-skills.
adaptyvbio/protein-design-skills
Quality control metrics and filtering thresholds for protein design.
Categories
Structure prediction with Protenix, an open AlphaFold3 reproduction. Protenix is an agent skill from adaptyvbio/protein-design-skills. Structure prediction with Protenix, an open AlphaFold3 reproduction.
Protenix fits situations like: predicting complex structures with an AF3-class model; wanting an open alternative to AF3 alongside Boltz and Chai; validating designed binder-target complexes.
Run `npx skills add adaptyvbio/protein-design-skills --skill protenix -a claude-code`. Or copy the skill folder (skills/protenix in adaptyvbio/protein-design-skills) into .claude/skills/protenix in your project. Claude Code loads it when a task matches its description.
Run `npx skills add adaptyvbio/protein-design-skills --skill protenix -a codex`. Or copy the skill folder (skills/protenix in adaptyvbio/protein-design-skills) into .agents/skills/protenix in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add adaptyvbio/protein-design-skills --skill protenix -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/protenix, .gemini/skills/protenix, .github/skills/protenix and .opencode/skills/protenix in your project.
Going by SKILL.md and its folder, Protenix needs the command-line tools its instructions call (git and uv).
SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Protenix is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 695 tokens (SKILL.md is roughly 2.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Protenix: Alphafold Database Fetch And Analyze (google-deepmind/science-skills, 3.2k stars), Pymol Visualization (ChatMol/ChatMol, 372 stars), Complexa Binder Design (NVIDIA-BioNeMo/bionemo-agent-toolkit, 478 stars) and DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
adaptyvbio (a GitHub organization) maintains it in adaptyvbio/protein-design-skills, which has 163 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on June 11, 2026.
Source: adaptyvbio/protein-design-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.