Agent skill

Alphafold

A skill your agent uses when running AlphaFold2 predictions on custom protein sequences, validating designed sequences via self-consistency, predicting binder-target complexes, or interpreting AF2…

Apache-2.0Auto-check passedResearch & Science

Install Alphafold

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill alphafold -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw alphafold --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/alphafold .claude/skills/alphafold && rm -rf skills-src

Use ~/.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/

Paste into Claude Code

Install the "alphafold" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/alphafold into .claude/skills/alphafold/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphafold", 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

$skill-installer install https://github.com/lamm-mit/scienceclaw/tree/main/skills/alphafold

Type 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.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill alphafold -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw alphafold --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/alphafold .agents/skills/alphafold && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "alphafold" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/alphafold into .agents/skills/alphafold/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphafold", 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.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill alphafold -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw alphafold --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/alphafold .cursor/skills/alphafold && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "alphafold" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/alphafold into .cursor/skills/alphafold/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphafold", 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

$ gemini skills install https://github.com/lamm-mit/scienceclaw.git --path skills/alphafold

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill alphafold -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw alphafold --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/alphafold .gemini/skills/alphafold && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "alphafold" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/alphafold into .gemini/skills/alphafold/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphafold", 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.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw alphafold

Installs 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).

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill alphafold -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/alphafold .github/skills/alphafold && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "alphafold" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/alphafold into .github/skills/alphafold/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphafold", 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.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill alphafold -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw alphafold --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/alphafold .opencode/skills/alphafold && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "alphafold" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/alphafold into .opencode/skills/alphafold/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphafold", 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.

Facts

Skill name: alphafold
GitHub stars: 244
Token cost: ~1.1k tokens
SKILL.md length: 249 words
Files: 1
Skills in repo: 85
Repo updated: Aug 21, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

A skill your agent uses when running AlphaFold2 predictions on custom protein sequences, validating designed sequences via self-consistency, predicting binder-target complexes, or interpreting AF2…

Works in 3 steps: ColabFold (Recommended for Multimers) → LocalColabFold → OpenFold (PyTorch reimplementation)
Running AlphaFold2 predictions on custom protein sequences
SKILL.md covers Requirements, Deployment Options, Key Parameters and Confidence Metrics, plus 4 more sections
Calls pip, wget and bash; reaches raw.githubusercontent.com

What it does

Alphafold is an agent skill from lamm-mit/scienceclaw. Use when running AlphaFold2 predictions on custom protein sequences, validating designed sequences via self-consistency, predicting binder-target complexes, or interpreting AF2 confidence metrics (pLDDT, pTM, ipTM).

Its SKILL.md is about 1.1k 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. It works with AlphaFold. The licence is Apache-2.0.

When your agent uses it

Running AlphaFold2 predictions on custom protein sequences
Validating designed sequences via self-consistency
Predicting binder-target complexes
Interpreting AF2 confidence metrics (pLDDT

Example prompts

“/alphafold”

Requirements

Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

ColabFold (Recommended for Multimers)
LocalColabFold
OpenFold (PyTorch reimplementation)

What it can do on your machine

Read from SKILL.md and the folder at commit ab9aba1. It shows what the files ask for, not the result of running them.

Tool permissions
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.
Runs code
Shell commands in SKILL.md call:
- pip
- wget
- bash
- python
- python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- raw.githubusercontent.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Alphafold loads about 1.1k tokens when it runs. Until then it costs about 56 tokens; SKILL.md has 249 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~56
When it runs · the whole SKILL.md, loaded when a task matches: ~1.1k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from lamm-mit/scienceclaw at commit ab9aba1, republished under its Apache-2.0 licence (© lamm-mit). 249 words, ~1,128 tokens.

Download SKILL.mdSave it as .claude/skills/alphafold/SKILL.md (or your agent's skills folder).

name: alphafold
description: Use when running AlphaFold2 predictions on custom protein sequences, validating designed sequences via self-consistency, predicting binder-target complexes, or interpreting AF2 confidence metrics (pLDDT, pTM, ipTM).

AlphaFold2 Structure Prediction

Use when the agent needs to run AlphaFold2 for protein structure prediction and complex modeling. Covers validating designed sequences, predicting binder-target complexes, and calculating confidence metrics (pLDDT, pTM, ipTM).

Distinct from alphafold-database (which retrieves pre-computed structures) — this skill covers running AF2 predictions on custom sequences.

Requirements

Python 3.8+
CUDA 11.0+, 32 GB GPU VRAM minimum (A100 recommended)
For multimers: ColabFold recommended over local install

Deployment Options

1. ColabFold (Recommended for Multimers)

bash

pip install colabfold[alphafold]

# Single chain
colabfold_batch input.fasta output_dir/ \
    --model-type alphafold2_ptm \
    --num-recycles 3

# Complex (multimer) — comma-separate chains in FASTA header
# >complex:ChainA,ChainB
colabfold_batch complex.fasta output_dir/ \
    --model-type alphafold2_multimer_v3 \
    --num-recycles 20 \
    --num-models 5

2. LocalColabFold

bash

# Install
wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/install_colabbatch_linux.sh
bash install_colabbatch_linux.sh

# Run offline
colabfold_batch sequences.fasta results/ \
    --model-type alphafold2_multimer_v3 \
    --num-recycles 3 \
    --use-gpu-relax

3. OpenFold (PyTorch reimplementation)

bash

pip install openfold
python run_pretrained_openfold.py \
    --fasta_paths input.fasta \
    --output_dir results/ \
    --model_device cuda:0

Key Parameters

Parameter	Values	Notes
`--model-type`	`alphafold2_ptm`, `alphafold2_multimer_v3`	Use multimer for complexes
`--num-recycles`	3–20	More recycles = better accuracy, slower
`--num-models`	1–5	5 models for ensemble confidence
`--msa-mode`	`mmseqs2_uniref_env` (default), `single_sequence`	Single = no MSA, faster
`--use-gpu-relax`	flag	Amber relaxation on GPU

Confidence Metrics

python

import numpy as np
import json

# Load result JSON
with open("result_model_1.json") as f:
    result = json.load(f)

plddt = np.array(result["plddt"])           # Per-residue confidence 0-100
ptm = result["ptm"]                          # Global TM-score estimate 0-1
iptm = result.get("iptm", None)             # Interface TM-score (multimer only)
pae = np.array(result.get("pae", []))       # Predicted Aligned Error matrix

# Quality thresholds
print(f"Mean pLDDT: {plddt.mean():.1f}")    # >70 = good, >90 = excellent
print(f"pTM: {ptm:.3f}")                    # >0.5 = confident fold
if iptm:
    print(f"ipTM: {iptm:.3f}")              # >0.6 = reliable complex, >0.8 = high confidence

Self-Consistency Validation for Designed Sequences

bash

# Design → predict → measure similarity to input backbone
# 1. Generate sequences with ProteinMPNN
# 2. Predict structure of each sequence with AF2
# 3. Calculate TM-score / RMSD vs. design backbone

python3 -c "
from Bio.PDB import PDBParser, Superimposer
# Compare predicted vs. designed structure
# High TM-score (>0.8) = sequence encodes target fold
"

Output Files

File	Contents
`*_relaxed_rank_1.pdb`	Top-ranked relaxed structure
`*_unrelaxed_rank_1.pdb`	Top-ranked unrelaxed structure
`result_model_*.json`	Scores: pLDDT, pTM, ipTM, PAE matrix
`*_coverage.png`	MSA coverage plot
`*_pae.png`	PAE heatmap (low = confident)

Quality Thresholds

Metric	Poor	Acceptable	Good	Excellent
Mean pLDDT	<50	50–70	70–90	>90
pTM	<0.4	0.4–0.5	0.5–0.7	>0.7
ipTM (complex)	<0.5	0.5–0.6	0.6–0.8	>0.8
Interface PAE	>20 Å	15–20 Å	8–15 Å	<8 Å

Common Issues

Problem	Cause	Fix
Low ipTM despite high pLDDT	Chains fold well independently but don't interact	Redesign interface residues
High PAE at interface	Interface not well-determined	Add more recycles; check contact predictions
OOM on GPU	Sequence too long	Use `--chunk-size 128` or CPU for MSA
All models disagree	Disordered region or wrong fold	Check MSA depth; try `--msa-mode single_sequence`

Files

Just SKILL.md in skills/alphafold of lamm-mit/scienceclaw.

Open the folder on GitHubat commit ab9aba1

Compare with similar skills

Alphafold 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.

Alphafold compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Alphafold this skilllamm-mit/scienceclaw	244	—	~1.1k	Automated safety check: Pass	Apache-2.0	1 mo ago
Alphafold Database Fetch And Analyzegoogle-deepmind/science-skills	3.2k	2 repos	~1.2k	Automated safety check: Pass	Apache-2.0	22 days ago
Alphafoldadaptyvbio/protein-design-skills	163	4 repos	~1.2k	Automated safety check: Pass	MIT	3 mo ago
Chaiadaptyvbio/protein-design-skills	163	4 repos	~1.5k	Automated safety check: Pass	MIT	3 mo ago
Biopipelineslocbp-uzh/biopipelines	109	—	~2.4k	Automated safety check: Pass	MIT	7 days ago
Rfdiffusionadaptyvbio/protein-design-skills	163	4 repos	~2.3k	Automated safety check: Pass	MIT	3 mo ago

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Questions about Alphafold

What does Alphafold do?

A skill your agent uses when running AlphaFold2 predictions on custom protein sequences, validating designed sequences via self-consistency, predicting binder-target complexes, or interpreting AF2…. Alphafold is an agent skill from lamm-mit/scienceclaw. Use when running AlphaFold2 predictions on custom protein sequences, validating designed sequences via self-consistency, predicting binder-target complexes, or interpreting AF2 confidence metrics (pLDDT, pTM, ipTM).

When should I use Alphafold?

Alphafold fits situations like: running AlphaFold2 predictions on custom protein sequences; validating designed sequences via self-consistency; predicting binder-target complexes; interpreting AF2 confidence metrics (pLDDT.

How do I install Alphafold in Claude Code?

Run `npx skills add lamm-mit/scienceclaw --skill alphafold -a claude-code`. Or copy the skill folder (skills/alphafold in lamm-mit/scienceclaw) into .claude/skills/alphafold in your project. Claude Code loads it when a task matches its description.

How do I install Alphafold in Codex?

Run `npx skills add lamm-mit/scienceclaw --skill alphafold -a codex`. Or copy the skill folder (skills/alphafold in lamm-mit/scienceclaw) into .agents/skills/alphafold in your project. Codex loads it when a task matches its description.

Can I use Alphafold in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lamm-mit/scienceclaw --skill alphafold -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/alphafold, .gemini/skills/alphafold, .github/skills/alphafold and .opencode/skills/alphafold in your project.

What does Alphafold need to run?

Going by SKILL.md and its folder, Alphafold needs the command-line tools its instructions call (pip, wget, bash, python and python3). Our summary lists: Python 3.

Does Alphafold access the network?

SKILL.md names 1 domain. In commands or code: raw.githubusercontent.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Alphafold safe to install?

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.

What licence does Alphafold use?

Alphafold is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Alphafold use?

About 1.1k tokens (SKILL.md is roughly 4.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Alphafold?

Skills that share tags, products or a category with Alphafold: Alphafold Database Fetch And Analyze (google-deepmind/science-skills, 3.2k stars), Alphafold (adaptyvbio/protein-design-skills, 163 stars), Chai (adaptyvbio/protein-design-skills, 163 stars) and Biopipelines (locbp-uzh/biopipelines, 109 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Alphafold?

lamm-mit (a GitHub user) maintains it in lamm-mit/scienceclaw, which has 244 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on August 21, 2026.

Source: lamm-mit/scienceclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Alphafold

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

AlphaFold2 Structure Prediction

Requirements

Deployment Options

1. ColabFold (Recommended for Multimers)

2. LocalColabFold

3. OpenFold (PyTorch reimplementation)

Key Parameters

Confidence Metrics

Self-Consistency Validation for Designed Sequences

Output Files

Quality Thresholds

Common Issues

Files

Compare with similar skills

Similar skills

More from lamm-mit/scienceclaw

Related

Questions about Alphafold