New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Esm

by davila7 in davila7/claude-code-templates

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and…

MITAuto-check: warningsAI & LLM Engineering

Install Esm

The automated check flagged lines worth reading first. See the safety section below.

skills CLI
$ npx skills add davila7/claude-code-templates --skill esm -a claude-code

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

GitHub CLI
$ gh skill install davila7/claude-code-templates esm --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/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/scientific/esm .claude/skills/esm && 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/

Facts

Skill name
esm
GitHub stars
32k
Used in
10 other repos
Token cost
~2.6k tokens
SKILL.md length
643 words
Files
5 (incl. references)
Skills in repo
477
Repo updated
First seen
Licence
MIT

At a glance

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and…

  • Works in 6 steps: Protein Sequence Generation with ESM3 → Structure Prediction and Inverse Folding → Protein Embeddings with ESM C → …
  • Working with protein sequences
  • SKILL.md covers Overview, Core Capabilities, Model Selection Guide and Installation, plus 5 more sections
  • Calls uv; reaches forge.evolutionaryscale.ai

What it does

Esm is an agent skill from davila7/claude-code-templates. Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/esm-c-api.md`, `references/esm3-api.md` and `references/forge-api.md`).

It sits in AI & LLM Engineering, covering Protein structure and design, Embeddings and Creative writing and fiction. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.

When your agent uses it

  • Working with protein sequences
  • Function prediction
  • Designing novel proteins
  • Generating protein embeddings

Example prompts

  • “/esm”

Requirements

  • Python 3

Workflow steps

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

  1. Protein Sequence Generation with ESM3
  2. Structure Prediction and Inverse Folding
  3. Protein Embeddings with ESM C
  4. Function Conditioning and Annotation
  5. Chain-of-Thought Generation
  6. Batch Processing with Forge API

What it can do on your machine

Read from SKILL.md and the folder at commit 14680ec. 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:

    • uv

    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:

    • forge.evolutionaryscale.ai

    Also links to:

    • evolutionaryscale.ai
    • github.com
    • science.org
    • bit.ly
    • responsiblebiodesign.ai

    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

Esm loads about 2.6k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 127 tokens; SKILL.md has 643 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~127
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~19k

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: warnings

The automated check found patterns that need a careful read before installing.

  • WarningUses a URL shortenerSKILL.md:295
    - **Community:** Slack community at https://bit.ly/3FKwcWd

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 davila7/claude-code-templates at commit 14680ec, republished under its MIT licence (© davila7). 643 words, ~2,625 tokens.

Download SKILL.mdSave it as .claude/skills/esm/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
esm
description
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

ESM: Evolutionary Scale Modeling

Overview

ESM provides state-of-the-art protein language models for understanding, generating, and designing proteins. This skill enables working with two model families: ESM3 for generative protein design across sequence, structure, and function, and ESM C for efficient protein representation learning and embeddings.

Core Capabilities

1. Protein Sequence Generation with ESM3

Generate novel protein sequences with desired properties using multimodal generative modeling.

When to use:

  • Designing proteins with specific functional properties
  • Completing partial protein sequences
  • Generating variants of existing proteins
  • Creating proteins with desired structural characteristics

Basic usage:

python
from esm.models.esm3 import ESM3
from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig

# Load model locally
model: ESM3InferenceClient = ESM3.from_pretrained("esm3-sm-open-v1").to("cuda")

# Create protein prompt
protein = ESMProtein(sequence="MPRT___KEND")  # '_' represents masked positions

# Generate completion
protein = model.generate(protein, GenerationConfig(track="sequence", num_steps=8))
print(protein.sequence)

For remote/cloud usage via Forge API:

python
from esm.sdk.forge import ESM3ForgeInferenceClient
from esm.sdk.api import ESMProtein, GenerationConfig

# Connect to Forge
model = ESM3ForgeInferenceClient(model="esm3-medium-2024-08", url="https://forge.evolutionaryscale.ai", token="<token>")

# Generate
protein = model.generate(protein, GenerationConfig(track="sequence", num_steps=8))

See references/esm3-api.md for detailed ESM3 model specifications, advanced generation configurations, and multimodal prompting examples.

2. Structure Prediction and Inverse Folding

Use ESM3's structure track for structure prediction from sequence or inverse folding (sequence design from structure).

Structure prediction:

python
from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig

# Predict structure from sequence
protein = ESMProtein(sequence="MPRTKEINDAGLIVHSP...")
protein_with_structure = model.generate(
    protein,
    GenerationConfig(track="structure", num_steps=protein.sequence.count("_"))
)

# Access predicted structure
coordinates = protein_with_structure.coordinates  # 3D coordinates
pdb_string = protein_with_structure.to_pdb()

Inverse folding (sequence from structure):

python
# Design sequence for a target structure
protein_with_structure = ESMProtein.from_pdb("target_structure.pdb")
protein_with_structure.sequence = None  # Remove sequence

# Generate sequence that folds to this structure
designed_protein = model.generate(
    protein_with_structure,
    GenerationConfig(track="sequence", num_steps=50, temperature=0.7)
)
3. Protein Embeddings with ESM C

Generate high-quality embeddings for downstream tasks like function prediction, classification, or similarity analysis.

When to use:

  • Extracting protein representations for machine learning
  • Computing sequence similarities
  • Feature extraction for protein classification
  • Transfer learning for protein-related tasks

Basic usage:

python
from esm.models.esmc import ESMC
from esm.sdk.api import ESMProtein

# Load ESM C model
model = ESMC.from_pretrained("esmc-300m").to("cuda")

# Get embeddings
protein = ESMProtein(sequence="MPRTKEINDAGLIVHSP...")
protein_tensor = model.encode(protein)

# Generate embeddings
embeddings = model.forward(protein_tensor)

Batch processing:

python
# Encode multiple proteins
proteins = [
    ESMProtein(sequence="MPRTKEIND..."),
    ESMProtein(sequence="AGLIVHSPQ..."),
    ESMProtein(sequence="KTEFLNDGR...")
]

embeddings_list = [model.logits(model.forward(model.encode(p))) for p in proteins]

See references/esm-c-api.md for ESM C model details, efficiency comparisons, and advanced embedding strategies.

4. Function Conditioning and Annotation

Use ESM3's function track to generate proteins with specific functional annotations or predict function from sequence.

Function-conditioned generation:

python
from esm.sdk.api import ESMProtein, FunctionAnnotation, GenerationConfig

# Create protein with desired function
protein = ESMProtein(
    sequence="_" * 200,  # Generate 200 residue protein
    function_annotations=[
        FunctionAnnotation(label="fluorescent_protein", start=50, end=150)
    ]
)

# Generate sequence with specified function
functional_protein = model.generate(
    protein,
    GenerationConfig(track="sequence", num_steps=200)
)
5. Chain-of-Thought Generation

Iteratively refine protein designs using ESM3's chain-of-thought generation approach.

python
from esm.sdk.api import GenerationConfig

# Multi-step refinement
protein = ESMProtein(sequence="MPRT" + "_" * 100 + "KEND")

# Step 1: Generate initial structure
config = GenerationConfig(track="structure", num_steps=50)
protein = model.generate(protein, config)

# Step 2: Refine sequence based on structure
config = GenerationConfig(track="sequence", num_steps=50, temperature=0.5)
protein = model.generate(protein, config)

# Step 3: Predict function
config = GenerationConfig(track="function", num_steps=20)
protein = model.generate(protein, config)
6. Batch Processing with Forge API

Process multiple proteins efficiently using Forge's async executor.

python
from esm.sdk.forge import ESM3ForgeInferenceClient
import asyncio

client = ESM3ForgeInferenceClient(model="esm3-medium-2024-08", token="<token>")

# Async batch processing
async def batch_generate(proteins_list):
    tasks = [
        client.async_generate(protein, GenerationConfig(track="sequence"))
        for protein in proteins_list
    ]
    return await asyncio.gather(*tasks)

# Execute
proteins = [ESMProtein(sequence=f"MPRT{'_' * 50}KEND") for _ in range(10)]
results = asyncio.run(batch_generate(proteins))

See references/forge-api.md for detailed Forge API documentation, authentication, rate limits, and batch processing patterns.

Model Selection Guide

ESM3 Models (Generative):

  • esm3-sm-open-v1 (1.4B) - Open weights, local usage, good for experimentation
  • esm3-medium-2024-08 (7B) - Best balance of quality and speed (Forge only)
  • esm3-large-2024-03 (98B) - Highest quality, slower (Forge only)

ESM C Models (Embeddings):

  • esmc-300m (30 layers) - Lightweight, fast inference
  • esmc-600m (36 layers) - Balanced performance
  • esmc-6b (80 layers) - Maximum representation quality

Selection criteria:

  • Local development/testing: Use esm3-sm-open-v1 or esmc-300m
  • Production quality: Use esm3-medium-2024-08 via Forge
  • Maximum accuracy: Use esm3-large-2024-03 or esmc-6b
  • High throughput: Use Forge API with batch executor
  • Cost optimization: Use smaller models, implement caching strategies

Installation

Basic installation:

bash
uv pip install esm

With Flash Attention (recommended for faster inference):

bash
uv pip install esm
uv pip install flash-attn --no-build-isolation

For Forge API access:

bash
uv pip install esm  # SDK includes Forge client

No additional dependencies needed. Obtain Forge API token at https://forge.evolutionaryscale.ai

Show full SKILL.md (270 more words)Show less

Common Workflows

For detailed examples and complete workflows, see references/workflows.md which includes:

  • Novel GFP design with chain-of-thought
  • Protein variant generation and screening
  • Structure-based sequence optimization
  • Function prediction pipelines
  • Embedding-based clustering and analysis

References

This skill includes comprehensive reference documentation:

  • references/esm3-api.md - ESM3 model architecture, API reference, generation parameters, and multimodal prompting
  • references/esm-c-api.md - ESM C model details, embedding strategies, and performance optimization
  • references/forge-api.md - Forge platform documentation, authentication, batch processing, and deployment
  • references/workflows.md - Complete examples and common workflow patterns

These references contain detailed API specifications, parameter descriptions, and advanced usage patterns. Load them as needed for specific tasks.

Best Practices

For generation tasks:

  • Start with smaller models for prototyping (esm3-sm-open-v1)
  • Use temperature parameter to control diversity (0.0 = deterministic, 1.0 = diverse)
  • Implement iterative refinement with chain-of-thought for complex designs
  • Validate generated sequences with structure prediction or wet-lab experiments

For embedding tasks:

  • Batch process sequences when possible for efficiency
  • Cache embeddings for repeated analyses
  • Normalize embeddings when computing similarities
  • Use appropriate model size based on downstream task requirements

For production deployment:

  • Use Forge API for scalability and latest models
  • Implement error handling and retry logic for API calls
  • Monitor token usage and implement rate limiting
  • Consider AWS SageMaker deployment for dedicated infrastructure

Resources and Documentation

Responsible Use

ESM is designed for beneficial applications in protein engineering, drug discovery, and scientific research. Follow the Responsible Biodesign Framework (https://responsiblebiodesign.ai/) when designing novel proteins. Consider biosafety and ethical implications of protein designs before experimental validation.

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in cli-tool/components/skills/scientific/esm of davila7/claude-code-templates.

  • SKILL.md
  • references/esm-c-api.md
  • references/esm3-api.md
  • references/forge-api.md
  • references/workflows.md

Open the folder on GitHubat commit 14680ec

Used in 10 other repositories

We found 28 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 10 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Esm 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.

Esm compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Esm this skilldavila7/claude-code-templates32k10 repos~2.6kAutomated safety check: WarnMIT
Esmfold2JimLiu/science-skills2274 repos~2.5kAutomated safety check: PassApache-2.0
Esmmajiayu000/claude-skill-registry6661 repos~1.7kAutomated safety check: PassMIT
EsmNeverSight/learn-skills.dev2161 repos~1.1kAutomated safety check: PassMIT
Multimodal Embedding Serving Devopen-edge-platform/edge-ai-libraries169—~1.2kAutomated safety check: PassApache-2.0
Esmadaptyvbio/protein-design-skills163—~2kAutomated safety check: PassMIT

Similar skills

  • Esmfold2

    JimLiu/science-skills

    Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al.

    227 GitHub starsUsed in 4 repos~2.5k tokens
    AI & LLM EngineeringAuto-check passed

  • Esm

    majiayu000/claude-skill-registry

    Toolkit for protein language models (ESM3 for multimodal generative protein design; ESM C for efficient embeddings).

    666 GitHub starsUsed in 1 repo~1.7k tokens
    AI & LLM EngineeringAuto-check passed

  • Esm

    NeverSight/learn-skills.dev

    ESM2 protein language model for embeddings and sequence scoring.

    216 GitHub starsUsed in 1 repo~1.1k tokens
    AI & LLM EngineeringAuto-check passed

  • Multimodal Embedding Serving Dev

    open-edge-platform/edge-ai-libraries

    Develop the Multimodal Embedding Serving microservice itself — Poetry install, run the existing tests, navigate the wrapper/registry/handler architecture, add a new model family, and build the image…

    169 GitHub stars~1.2k tokensUpdated today
    AI & LLM EngineeringAuto-check passed

  • Esm

    adaptyvbio/protein-design-skills

    ESM protein language models for embeddings, sequence scoring, structure prediction, and binder design.

    163 GitHub stars~2k tokensUpdated 3 mo ago
    AI & LLM EngineeringAuto-check passed

  • Unimol

    jinzhezenggroup/computational-chemistry-agent-skills

    A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in…

    148 GitHub starsUsed in 1 repo~1.5k tokens
    AI & LLM EngineeringAuto-check passed

More from davila7/claude-code-templates

All 477 skills in this repo

  • Perplexity Web Search

    davila7/claude-code-templates

    Runs web-grounded searches through Perplexity's Sonar models over OpenRouter for current events, recent literature and cited facts beyond the model's training cutoff.

    32k GitHub starsUsed in 12 repos~3.5k tokens
    Auto-check: notes

  • Neuropixels Data Analysis

    davila7/claude-code-templates

    Analyzes Neuropixels recordings from SpikeGLX or Open Ephys through preprocessing, drift correction, Kilosort4 spike sorting, quality metrics and curation.

    32k GitHub starsUsed in 10 repos~2.8k tokens
    Auto-check passed

  • Scientific Venue Templates

    davila7/claude-code-templates

    Supplies LaTeX templates and formatting rules for journals, conferences, posters, and grant proposals, then can check a draft against them.

    32k GitHub starsUsed in 9 repos~5.1k tokens
    Auto-check: notes

  • Brand Voice Content Creator

    davila7/claude-code-templates

    Analyzes a brand's existing writing to lock in a consistent voice, then builds SEO blog posts and platform-specific social content around it.

    32k GitHub starsUsed in 3 repos~1.9k tokens
    Auto-check passed

  • CAPA Officer

    davila7/claude-code-templates

    Guides corrective and preventive action (CAPA) work in a quality management system, from initiation and root cause analysis through effectiveness verification.

    32k GitHub starsUsed in 1 repo~2k tokens
    Auto-check passed

  • Fda Consultant Specialist

    davila7/claude-code-templates

    Senior FDA consultant and specialist for medical device companies including HIPAA compliance and requirement management.

    32k GitHub starsUsed in 1 repo~2.7k tokens
    Auto-check passed

Categories

Questions about Esm

What does Esm do?

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and…. Esm is an agent skill from davila7/claude-code-templates. Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations).

When should I use Esm?

Esm fits situations like: working with protein sequences; function prediction; designing novel proteins; generating protein embeddings.

How do I install Esm in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill esm -a claude-code`. Or copy the skill folder (cli-tool/components/skills/scientific/esm in davila7/claude-code-templates) into .claude/skills/esm in your project. Claude Code loads it when a task matches its description.

How do I install Esm in Codex?

Run `npx skills add davila7/claude-code-templates --skill esm -a codex`. Or copy the skill folder (cli-tool/components/skills/scientific/esm in davila7/claude-code-templates) into .agents/skills/esm in your project. Codex loads it when a task matches its description.

Can I use Esm 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 davila7/claude-code-templates --skill esm -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/esm, .gemini/skills/esm, .github/skills/esm and .opencode/skills/esm in your project.

What does Esm need to run?

Going by SKILL.md and its folder, Esm needs the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Esm access the network?

SKILL.md names 6 domains. In commands or code: forge.evolutionaryscale.ai; the agent is likely to contact it when it follows the instructions. As links in the text: evolutionaryscale.ai, github.com, science.org, bit.ly and responsiblebiodesign.ai. This is read from the text; nothing was executed.

Is Esm safe to install?

Our automated static check of SKILL.md flagged 1 warning(s): uses a url shortener. Read the flagged lines before installing; the check is not a guarantee either way.

What licence does Esm use?

Esm is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Esm use?

About 2.6k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 16k tokens, read only when the agent opens those files.

What are the alternatives to Esm?

Skills that share tags, products or a category with Esm: Esmfold2 (JimLiu/science-skills, 227 stars), Esm (majiayu000/claude-skill-registry, 666 stars), Esm (NeverSight/learn-skills.dev, 216 stars) and Multimodal Embedding Serving Dev (open-edge-platform/edge-ai-libraries, 169 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Esm?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,463 GitHub stars. The repository holds 477 skills in this directory. The repository was last updated on October 8, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.