Agent skill

Ipsae

by adaptyvbio in adaptyvbio/protein-design-skills

Binder design ranking using ipSAE (interprotein Score from Aligned Errors).

Install Ipsae

skills CLI

$ npx skills add adaptyvbio/protein-design-skills --skill ipsae -a claude-code

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

GitHub CLI

$ gh skill install adaptyvbio/protein-design-skills ipsae --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/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ipsae .claude/skills/ipsae && 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 "ipsae" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ipsae into .claude/skills/ipsae/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ipsae", 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/adaptyvbio/protein-design-skills/tree/main/skills/ipsae

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 adaptyvbio/protein-design-skills --skill ipsae -a codex

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

GitHub CLI

$ gh skill install adaptyvbio/protein-design-skills ipsae --agent codex

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

Manual copy

$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ipsae .agents/skills/ipsae && 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 "ipsae" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ipsae into .agents/skills/ipsae/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ipsae", 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 adaptyvbio/protein-design-skills --skill ipsae -a cursor

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

GitHub CLI

$ gh skill install adaptyvbio/protein-design-skills ipsae --agent cursor

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

Manual copy

$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ipsae .cursor/skills/ipsae && 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 "ipsae" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ipsae into .cursor/skills/ipsae/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ipsae", 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/adaptyvbio/protein-design-skills.git --path skills/ipsae

--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 adaptyvbio/protein-design-skills --skill ipsae -a gemini-cli

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

GitHub CLI

$ gh skill install adaptyvbio/protein-design-skills ipsae --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/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ipsae .gemini/skills/ipsae && 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 "ipsae" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ipsae into .gemini/skills/ipsae/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ipsae", 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 adaptyvbio/protein-design-skills ipsae

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 adaptyvbio/protein-design-skills --skill ipsae -a github-copilot

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

Manual copy

$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ipsae .github/skills/ipsae && 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 "ipsae" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ipsae into .github/skills/ipsae/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ipsae", 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 adaptyvbio/protein-design-skills --skill ipsae -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 adaptyvbio/protein-design-skills ipsae --agent opencode

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

Manual copy

$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ipsae .opencode/skills/ipsae && 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 "ipsae" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ipsae into .opencode/skills/ipsae/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ipsae", 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: ipsae
GitHub stars: 163
Used in: 4 other repos
Token cost: ~1.2k tokens
SKILL.md length: 272 words
Files: 1
Skills in repo: 24
Repo updated: Jun 11, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Binder design ranking using ipSAE (interprotein Score from Aligned Errors).

Ranking binder designs for experimental testing
SKILL.md covers Prerequisites, Overview, How to run and Key parameters, plus 7 more sections
Calls python, git and pip; reaches github.com
Filtering BindCraft

What it does

Ipsae is an agent skill from adaptyvbio/protein-design-skills. Binder design ranking using ipSAE (interprotein Score from Aligned Errors). Use this skill when: (1) Ranking binder designs for experimental testing, (2) Filtering BindCraft or RFdiffusion outputs, (3) Comparing AF2/AF3/Boltz predictions, (4) Predicting binding success rates, (5) Need better ranking than ipTM or iPAE. For structure prediction, use chai or alphafold. For QC thresholds, use protein-qc.

Its SKILL.md is about 1.2k 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 repository describes itself as: Claude Code skills for protein design. The licence is MIT.

When your agent uses it

Ranking binder designs for experimental testing
Filtering BindCraft
RFdiffusion outputs
Comparing AF2/AF3/Boltz predictions

Example prompts

“/ipsae”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 59dd633. 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:
- python
- git
- pip
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:
- github.com
Also links to:
- biorxiv.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

Ipsae loads about 1.2k tokens when it runs. Until then it costs about 102 tokens; SKILL.md has 272 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~102
When it runs · the whole SKILL.md, loaded when a task matches: ~1.2k

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 adaptyvbio/protein-design-skills at commit 59dd633, republished under its MIT licence (© adaptyvbio). 272 words, ~1,247 tokens.

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

name: ipsae
description: Binder design ranking using ipSAE (interprotein Score from Aligned Errors). Use this skill when: (1) Ranking binder designs for experimental testing, (2) Filtering BindCraft or RFdiffusion outputs, (3) Comparing AF2/AF3/Boltz predictions, (4) Predicting binding success rates, (5) Need better ranking than ipTM or iPAE. For structure prediction, use chai or alphafold. For QC thresholds, use protein-qc.
license: MIT
category: evaluation
tags: ranking, scoring, binding

ipSAE Binder Ranking

Prerequisites

Requirement	Minimum	Recommended
Python	3.8+	3.10
NumPy	1.20+	Latest
RAM	8GB	16GB

Overview

ipSAE (interprotein Score from Aligned Errors) is a scoring function for ranking protein-protein interactions predicted by AlphaFold2, AlphaFold3, and Boltz1. It separates true from false predicted complexes more reliably than ipTM, which dilutes interface confidence across disordered or accessory regions. In a separate binder meta-analysis (Overath et al. 2025), AF3 ipSAE_min gave a 1.4-fold gain in average precision over the ipAE score that RFdiffusion pipelines commonly filter on.

Paper: Dunbrack, "Rēs ipSAE loquuntur: What's wrong with AlphaFold's ipTM score and how to fix it", bioRxiv 2025.02.10.637595

How to run

Installation

bash

git clone https://github.com/DunbrackLab/IPSAE.git
cd IPSAE
pip install numpy

AlphaFold2

bash

python ipsae.py scores_rank_001.json unrelaxed_rank_001.pdb 15 15

AlphaFold3

bash

python ipsae.py fold_model_full_data_0.json fold_model_0.cif 10 10

Boltz1

bash

python ipsae.py pae_model_0.npz model_0.cif 10 10

Key parameters

Parameter	Description	Recommended
PAE file	JSON (AF2/AF3) or NPZ (Boltz)	Match predictor
Structure file	PDB or CIF structure	Match PAE
PAE cutoff	Threshold for contacts	10-15
Distance cutoff	Max CA-CA distance (A)	10-15

Output format

Two output files are generated:

Chain-pair scores (_chains.csv):

chain_A,chain_B,ipSAE_min,pDockQ,pDockQ2,LIS,n_contacts,interface_dist
A,B,0.72,0.65,0.58,0.45,42,8.5

Residue-level scores (_residues.csv):

chain,resnum,pSAE,pLDDT
A,45,0.85,92.3
A,67,0.78,88.1

Sample output

Successful run

$ python ipsae.py scores_rank_001.json design_0.pdb 10 10
Processing design_0...
Found 2 chains: A, B
Computing ipSAE scores...

Results written to:
  design_0_chains.csv
  design_0_residues.csv

Summary:
  ipSAE_min: 0.72
  pDockQ: 0.65
  LIS: 0.45
  Interface contacts: 42

What good output looks like:

ipSAE_min > 0.61 (primary filter)
pDockQ > 0.5 (supporting metric)
Reasonable number of interface contacts (20-100)

Decision tree

Should I use ipSAE?
│
├─ What are you ranking?
│  ├─ Designed binders → ipSAE ✓
│  ├─ Natural complexes → ipTM is fine
│  └─ Single proteins → Not applicable
│
├─ What predictor did you use?
│  ├─ AlphaFold2 → ipSAE ✓
│  ├─ AlphaFold3 → ipSAE ✓
│  ├─ Boltz1 → ipSAE ✓
│  ├─ Chai → ipSAE (use PAE output)
│  └─ ESMFold → Not applicable (no PAE)
│
└─ Why ipSAE over ipTM?
   ├─ Different length constructs → ipSAE ✓
   ├─ Designs with disordered regions → ipSAE ✓
   └─ Standard complexes → Either works

Recommended thresholds

Metric	Standard	Stringent	Use Case
ipSAE_min	> 0.61	> 0.70	Primary filter
LIS	> 0.35	> 0.45	Interface quality
pDockQ	> 0.5	> 0.6	Supporting

Batch processing

python

import subprocess
import os
from pathlib import Path

def score_designs(pae_dir, struct_dir, output_dir):
    """Score all designs in a directory."""
    Path(output_dir).mkdir(exist_ok=True)

    for pae_file in Path(pae_dir).glob("*_scores*.json"):
        name = pae_file.stem.replace("_scores_rank_001", "")
        struct_file = Path(struct_dir) / f"{name}.pdb"

        if struct_file.exists():
            subprocess.run([
                "python", "ipsae.py",
                str(pae_file),
                str(struct_file),
                "10", "10"
            ])

Verify

bash

ls *_chains.csv | wc -l  # Should match number of predictions

Troubleshooting

Low scores for good designs: Check PAE/distance cutoffs Missing output: Verify PAE file format matches predictor Inconsistent scores: Use same cutoffs across all designs

Error interpretation

Error	Cause	Fix
`KeyError: 'pae'`	Wrong PAE format	Check if AF2/AF3/Boltz format
`FileNotFoundError`	Structure not found	Verify file paths
`ValueError: no contacts`	No interface detected	Check chain IDs, reduce cutoffs

Next: Select top designs (ipSAE_min > 0.61) → experimental validation.

Files

Just SKILL.md in skills/ipsae of adaptyvbio/protein-design-skills.

Open the folder on GitHubat commit 59dd633

Used in 4 other repositories

We found 6 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in adaptyvbio/protein-design-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Ipsae 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.

Ipsae compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Ipsae this skilladaptyvbio/protein-design-skills	163	4 repos	~1.2k	Automated safety check: Pass	MIT	3 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
Biopipelineslocbp-uzh/biopipelines	109	—	~2.4k	Automated safety check: Pass	MIT	7 days ago
Bio DB ToolsDrugClaw/DrugClaw	125	—	~1.4k	Automated safety check: Pass	Apache-2.0	6 mo ago
Ggetdavila7/claude-code-templates	32k	11 repos	~6.3k	Automated safety check: Pass	MIT	today
Alphafold Databasedavila7/claude-code-templates	32k	10 repos	~4k	Automated safety check: Pass	MIT	today

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Questions about Ipsae

What does Ipsae do?

Binder design ranking using ipSAE (interprotein Score from Aligned Errors). Ipsae is an agent skill from adaptyvbio/protein-design-skills. Binder design ranking using ipSAE (interprotein Score from Aligned Errors).

When should I use Ipsae?

Ipsae fits situations like: ranking binder designs for experimental testing; filtering BindCraft; RFdiffusion outputs; comparing AF2/AF3/Boltz predictions.

How do I install Ipsae in Claude Code?

Run `npx skills add adaptyvbio/protein-design-skills --skill ipsae -a claude-code`. Or copy the skill folder (skills/ipsae in adaptyvbio/protein-design-skills) into .claude/skills/ipsae in your project. Claude Code loads it when a task matches its description.

How do I install Ipsae in Codex?

Run `npx skills add adaptyvbio/protein-design-skills --skill ipsae -a codex`. Or copy the skill folder (skills/ipsae in adaptyvbio/protein-design-skills) into .agents/skills/ipsae in your project. Codex loads it when a task matches its description.

Can I use Ipsae 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 adaptyvbio/protein-design-skills --skill ipsae -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ipsae, .gemini/skills/ipsae, .github/skills/ipsae and .opencode/skills/ipsae in your project.

What does Ipsae need to run?

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

Does Ipsae access the network?

SKILL.md names 2 domains. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. As links in the text: biorxiv.org. This is read from the text; nothing was executed.

Is Ipsae 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 Ipsae use?

Ipsae 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 Ipsae use?

About 1.2k tokens (SKILL.md is roughly 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 Ipsae?

Skills that share tags, products or a category with Ipsae: Alphafold Database Fetch And Analyze (google-deepmind/science-skills, 3.2k stars), Biopipelines (locbp-uzh/biopipelines, 109 stars), Bio DB Tools (DrugClaw/DrugClaw, 125 stars) and Gget (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ipsae?

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.

Install Ipsae

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

ipSAE Binder Ranking

Prerequisites

Overview

How to run

Installation

AlphaFold2

AlphaFold3

Boltz1

Key parameters

Output format

Sample output

Successful run

Decision tree

Recommended thresholds

Batch processing

Verify

Troubleshooting

Error interpretation

Files

Used in 4 other repositories

Compare with similar skills

Similar skills

More from adaptyvbio/protein-design-skills

Related

Questions about Ipsae