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Agent skill

End To End Protein Design Workflow

by BioTender-max in BioTender-max/awesome-bio-agent-skills

End-to-end protein design pipeline guide across preparation, generation, validation, and filtering.

MITAuto-check passedResearch & Science

Install End To End Protein Design Workflow

skills CLI
$ npx skills add BioTender-max/awesome-bio-agent-skills --skill end-to-end-protein-design-workflow -a claude-code

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

GitHub CLI
$ gh skill install BioTender-max/awesome-bio-agent-skills end-to-end-protein-design-workflow --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/BioTender-max/awesome-bio-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/bioclaw_hub/end-to-end-protein-design-workflow .claude/skills/end-to-end-protein-design-workflow && 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
end-to-end-protein-design-workflow
GitHub stars
200
Token cost
~1.4k tokens
SKILL.md length
374 words
Files
4 (incl. references)
Skills in repo
23
Repo updated
First seen
Licence
MIT

At a glance

End-to-end protein design pipeline guide across preparation, generation, validation, and filtering.

  • Works in 5 steps: Target preparation → Backbone generation → Sequence design → …
  • Starting a new protein design project
  • SKILL.md covers Standard binder design pipeline, Phase 1: Target preparation, Phase 2: Backbone generation and Phase 3: Sequence design, plus 8 more sections
  • Calls modal and curl; reaches files.rcsb.org

What it does

End To End Protein Design Workflow is an agent skill from BioTender-max/awesome-bio-agent-skills. End-to-end protein design pipeline guide across preparation, generation, validation, and filtering. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design-tool-selection. For QC thresholds, use protein-design-qc.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `README.md`, `references/qc-checklist.md` and `references/standard-pipeline.md`).

It sits in Research & Science, covering Protein structure and design and End-to-end testing. The repository describes itself as: A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design. The licence is MIT.

When your agent uses it

  • Starting a new protein design project
  • Need step-by-step workflow guidance
  • Understanding the full design pipeline
  • Planning compute resources and timelines

Example prompts

  • “/end-to-end-protein-design-workflow”

Requirements

  • Python 3

Workflow steps

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

  1. Target preparation
  2. Backbone generation
  3. Sequence design
  4. Structure validation
  5. Filtering and selection

What it can do on your machine

Read from SKILL.md and the folder at commit 8cbdd18. 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:

    • modal
    • curl

    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:

    • files.rcsb.org

    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

End To End Protein Design Workflow loads about 1.4k tokens when it runs, and up to ~3.3k if it reads all its reference files. Until then it costs about 116 tokens; SKILL.md has 374 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~116
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.3k

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 BioTender-max/awesome-bio-agent-skills at commit 8cbdd18, republished under its MIT licence (© BioTender-max). 374 words, ~1,429 tokens.

Download SKILL.mdSave it as .claude/skills/end-to-end-protein-design-workflow/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
end-to-end-protein-design-workflow
description
End-to-end protein design pipeline guide across preparation, generation, validation, and filtering. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design-tool-selection. For QC thresholds, use protein-design-qc.
license
MIT
category
orchestration
tags
guidance, pipeline, workflow

End-to-End Protein Design Workflow

Plain-language role: Use this skill when you want the full pipeline, from target preparation through final QC.

Standard binder design pipeline

Overview
Target Preparation --> Backbone Generation --> Sequence Design
         |                     |                     |
         v                     v                     v
    (pdb skill)          (rfdiffusion)         (proteinmpnn)
                               |                     |
                               v                     v
                        Structure Validation --> Filtering
                               |                     |
                               v                     v
                         (alphafold2-multimer/chai1-structure-prediction)      (protein-design-qc)

Phase 1: Target preparation

1.1 Obtain target structure
bash
# Download from PDB
curl -o target.pdb "https://files.rcsb.org/download/XXXX.pdb"
1.2 Clean and prepare
python
# Extract target chain
# Remove waters, ligands if needed
# Trim to binding region + 10A buffer
1.3 Select hotspots
  • Choose 3-6 exposed residues
  • Prefer charged/aromatic (K, R, E, D, W, Y, F)
  • Check surface accessibility
  • Verify residue numbering

Output: target_prepared.pdb, hotspot list

Phase 2: Backbone generation

Option A: RFdiffusion (diverse exploration)
bash
modal run modal_rfdiffusion.py \
  --pdb target_prepared.pdb \
  --contigs "A1-150/0 70-100" \
  --hotspot "A45,A67,A89" \
  --num-designs 500
Option B: BindCraft (end-to-end)
bash
modal run modal_bindcraft.py \
  --target-pdb target_prepared.pdb \
  --hotspots "A45,A67,A89" \
  --num-designs 100

Output: 100-500 backbone PDBs

Phase 3: Sequence design

For RFdiffusion backbones
bash
for backbone in backbones/*.pdb; do
  modal run modal_proteinmpnn.py \
    --pdb-path "$backbone" \
    --num-seq-per-target 8 \
    --sampling-temp 0.1
done

Output: 8 sequences per backbone (800-4000 total)

Phase 4: Structure validation

Predict complexes
bash
# Prepare FASTA with binder + target
# binder:target format for multimer

modal run modal_colabfold.py \
  --input-faa all_sequences.fasta \
  --out-dir predictions/

Output: AF2 predictions with pLDDT, ipTM, PAE

Phase 5: Filtering and selection

Apply standard thresholds
python
import pandas as pd

# Load metrics
designs = pd.read_csv('all_metrics.csv')

# Filter
filtered = designs[
    (designs['pLDDT'] > 0.85) &
    (designs['ipTM'] > 0.50) &
    (designs['PAE_interface'] < 10) &
    (designs['scRMSD'] < 2.0) &
    (designs['esm2_pll'] > 0.0)
]

# Rank by composite score
filtered['score'] = (
    0.3 * filtered['pLDDT'] +
    0.3 * filtered['ipTM'] +
    0.2 * (1 - filtered['PAE_interface'] / 20) +
    0.2 * filtered['esm2_pll']
)

top_designs = filtered.nlargest(50, 'score')

Output: 50-200 filtered candidates

Resource planning

Compute requirements
StageGPUTime (100 designs)
RFdiffusionA10G30 min
ProteinMPNNT415 min
ColabFoldA1004-8 hours
FilteringCPU15 min
Total timeline
  • Small campaign (100 designs): 8-12 hours
  • Medium campaign (500 designs): 24-48 hours
  • Large campaign (1000+ designs): 2-5 days

Quality checkpoints

After backbone generation
  • Visual inspection of diverse backbones
  • Secondary structure present
  • No clashes with target
After sequence design
  • ESM2 PLL > 0.0 for most sequences
  • No unwanted cysteines (unless intentional)
  • Reasonable sequence diversity
After validation
  • pLDDT > 0.85
  • ipTM > 0.50
  • PAE_interface < 10
  • Self-consistency RMSD < 2.0 A
Final selection
  • Diverse sequences (cluster if needed)
  • Manufacturable (no problematic motifs)
  • Reasonable molecular weight
Show full SKILL.md (149 more words)Show less

Common issues

ProblemSolution
Low ipTMCheck hotspots, increase designs
Poor diversityHigher temperature, more backbones
High scRMSDBackbone may be unusual
Low pLDDTCheck design quality

Advanced workflows

Multi-tool combination
  1. RFdiffusion for initial backbones
  2. ColabDesign for refinement
  3. ProteinMPNN diversification
  4. AF2 final validation
Iterative refinement
  1. Run initial campaign
  2. Analyze failures
  3. Adjust hotspots/parameters
  4. Repeat with insights

Inputs

  • A starting project goal, target structure context, and desired validation depth.
  • Constraints on compute, turnaround time, and expected throughput.
  • Optional prior campaign results that should shape the next iteration.

Outputs

  • An end-to-end workflow describing target prep, generation, sequence design, validation, and QC.
  • A practical execution order across the specialized protein design skills.
  • A shared mental model for where each tool fits in the campaign funnel.

Next Step

Start with pdb for target preparation, then follow the recommended generation and validation path through the linked skills.

© BioTender-max, 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 3 other files (references) in skills/bioclaw_hub/end-to-end-protein-design-workflow of BioTender-max/awesome-bio-agent-skills.

  • SKILL.md
  • README.md
  • references/qc-checklist.md
  • references/standard-pipeline.md

Open the folder on GitHubat commit 8cbdd18

Compare with similar skills

End To End Protein Design Workflow 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.

End To End Protein Design Workflow compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
End To End Protein Design Workflow this skillBioTender-max/awesome-bio-agent-skills200—~1.4kAutomated safety check: PassMIT
Alphafold Database Fetch And Analyzegoogle-deepmind/science-skills3.2k2 repos~1.2kAutomated safety check: PassApache-2.0
Alphafoldadaptyvbio/protein-design-skills1643 repos~1.2kAutomated safety check: PassMIT
Pymol VisualizationChatMol/ChatMol373—~1.2kAutomated safety check: PassMIT
Complexa Binder DesignNVIDIA-BioNeMo/bionemo-agent-toolkit479—~3.1kAutomated safety check: NotesApache-2.0
Bindcraftadaptyvbio/protein-design-skills1643 repos~1.3kAutomated safety check: PassMIT

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Categories

Questions about End To End Protein Design Workflow

What does End To End Protein Design Workflow do?

End-to-end protein design pipeline guide across preparation, generation, validation, and filtering. End To End Protein Design Workflow is an agent skill from BioTender-max/awesome-bio-agent-skills. End-to-end protein design pipeline guide across preparation, generation, validation, and filtering.

When should I use End To End Protein Design Workflow?

End To End Protein Design Workflow fits situations like: starting a new protein design project; need step-by-step workflow guidance; understanding the full design pipeline; planning compute resources and timelines.

How do I install End To End Protein Design Workflow in Claude Code?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill end-to-end-protein-design-workflow -a claude-code`. Or copy the skill folder (skills/bioclaw_hub/end-to-end-protein-design-workflow in BioTender-max/awesome-bio-agent-skills) into .claude/skills/end-to-end-protein-design-workflow in your project. Claude Code loads it when a task matches its description.

How do I install End To End Protein Design Workflow in Codex?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill end-to-end-protein-design-workflow -a codex`. Or copy the skill folder (skills/bioclaw_hub/end-to-end-protein-design-workflow in BioTender-max/awesome-bio-agent-skills) into .agents/skills/end-to-end-protein-design-workflow in your project. Codex loads it when a task matches its description.

Can I use End To End Protein Design Workflow 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 BioTender-max/awesome-bio-agent-skills --skill end-to-end-protein-design-workflow -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/end-to-end-protein-design-workflow, .gemini/skills/end-to-end-protein-design-workflow, .github/skills/end-to-end-protein-design-workflow and .opencode/skills/end-to-end-protein-design-workflow in your project.

What does End To End Protein Design Workflow need to run?

Going by SKILL.md and its folder, End To End Protein Design Workflow needs the command-line tools its instructions call (modal and curl). Our summary lists: Python 3.

Does End To End Protein Design Workflow access the network?

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

Is End To End Protein Design Workflow 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 End To End Protein Design Workflow use?

End To End Protein Design Workflow is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does End To End Protein Design Workflow use?

About 1.4k tokens (SKILL.md is roughly 5.7k 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 1.9k tokens, read only when the agent opens those files.

What are the alternatives to End To End Protein Design Workflow?

Skills that share tags, products or a category with End To End Protein Design Workflow: Alphafold Database Fetch And Analyze (google-deepmind/science-skills, 3.2k stars), Alphafold (adaptyvbio/protein-design-skills, 164 stars), Pymol Visualization (ChatMol/ChatMol, 373 stars) and Complexa Binder Design (NVIDIA-BioNeMo/bionemo-agent-toolkit, 479 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains End To End Protein Design Workflow?

BioTender-max (a GitHub user) maintains it in BioTender-max/awesome-bio-agent-skills, which has 200 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on July 1, 2026.

Source: BioTender-max/awesome-bio-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.