Molecode
AtomFlow-AI/MoleCode
A skill your agent uses for deterministic molecule understanding, graph-level editing, generation, and validation with MoleCode — an explicit Mermaid graph in which every atom and bond is a typed…
Predict protein-ligand binding affinity using Boltz-2 model to assess molecular interactions and binding probability for drug discovery.
The automated check flagged lines worth reading first. See the safety section below.
$ npx skills add InternScience/scp --skill boltz2-binding-affinity -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install InternScience/scp boltz2-binding-affinity --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/InternScience/scp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/boltz2-binding-affinity .claude/skills/boltz2-binding-affinity && 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 "boltz2-binding-affinity" agent skill from https://github.com/InternScience/scp/tree/main/skills/boltz2-binding-affinity into .claude/skills/boltz2-binding-affinity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "boltz2-binding-affinity", 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/InternScience/scp/tree/main/skills/boltz2-binding-affinityType 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 InternScience/scp --skill boltz2-binding-affinity -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install InternScience/scp boltz2-binding-affinity --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/InternScience/scp.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/boltz2-binding-affinity .agents/skills/boltz2-binding-affinity && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "boltz2-binding-affinity" agent skill from https://github.com/InternScience/scp/tree/main/skills/boltz2-binding-affinity into .agents/skills/boltz2-binding-affinity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "boltz2-binding-affinity", 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 InternScience/scp --skill boltz2-binding-affinity -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install InternScience/scp boltz2-binding-affinity --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/InternScience/scp.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/boltz2-binding-affinity .cursor/skills/boltz2-binding-affinity && 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 "boltz2-binding-affinity" agent skill from https://github.com/InternScience/scp/tree/main/skills/boltz2-binding-affinity into .cursor/skills/boltz2-binding-affinity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "boltz2-binding-affinity", 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/InternScience/scp.git --path skills/boltz2-binding-affinity--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 InternScience/scp --skill boltz2-binding-affinity -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install InternScience/scp boltz2-binding-affinity --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/InternScience/scp.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/boltz2-binding-affinity .gemini/skills/boltz2-binding-affinity && 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 "boltz2-binding-affinity" agent skill from https://github.com/InternScience/scp/tree/main/skills/boltz2-binding-affinity into .gemini/skills/boltz2-binding-affinity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "boltz2-binding-affinity", 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 InternScience/scp boltz2-binding-affinityInstalls 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 InternScience/scp --skill boltz2-binding-affinity -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/InternScience/scp.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/boltz2-binding-affinity .github/skills/boltz2-binding-affinity && 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 "boltz2-binding-affinity" agent skill from https://github.com/InternScience/scp/tree/main/skills/boltz2-binding-affinity into .github/skills/boltz2-binding-affinity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "boltz2-binding-affinity", 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 InternScience/scp --skill boltz2-binding-affinity -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install InternScience/scp boltz2-binding-affinity --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/InternScience/scp.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/boltz2-binding-affinity .opencode/skills/boltz2-binding-affinity && 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 "boltz2-binding-affinity" agent skill from https://github.com/InternScience/scp/tree/main/skills/boltz2-binding-affinity into .opencode/skills/boltz2-binding-affinity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "boltz2-binding-affinity", 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.
boltz2-binding-affinityPredict protein-ligand binding affinity using Boltz-2 model to assess molecular interactions and binding probability for drug discovery.
Boltz2 Binding Affinity is an agent skill from InternScience/scp. Predict protein-ligand binding affinity using Boltz-2 model to assess molecular interactions and binding probability for drug discovery.
Its SKILL.md is about 1.4k 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 Drug discovery and cheminformatics. The licence is MIT.
2 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit cea5398. 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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From 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.
Boltz2 Binding Affinity loads about 1.4k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 266 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 patterns that need a careful read before installing.
sequence = 'PIVQNLQGQMVHQCISPRTLNAWVKVVEEKAFSPEVIPMFSALSCGATPQDLNTMLNTVGGHQAAMQMLKETINEEAAEWDRLHPVHAGPIAPGQMREPRGSDIAGTTAutomated 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 InternScience/scp at commit cea5398, republished under its MIT licence (© InternScience). 266 words, ~1,443 tokens.
.claude/skills/boltz2-binding-affinity/SKILL.md (or your agent's skills folder).import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class DrugSDAClient:
"""DrugSDA-Model MCP Client"""
def __init__(self, server_url: str, api_key: str):
self.server_url = server_url
self.api_key = api_key
self.session = None
async def connect(self):
"""Establish connection and initialize session"""
print(f"server url: {self.server_url}")
try:
self.transport = streamablehttp_client(
url=self.server_url,
headers={"SCP-HUB-API-KEY": self.api_key}
)
self.read, self.write, self.get_session_id = await self.transport.__aenter__()
self.session_ctx = ClientSession(self.read, self.write)
self.session = await self.session_ctx.__aenter__()
await self.session.initialize()
session_id = self.get_session_id()
print(f"✓ connect success")
return True
except Exception as e:
print(f"✗ connect failure: {e}")
import traceback
traceback.print_exc()
return False
async def disconnect(self):
"""Disconnect from server"""
try:
if self.session:
await self.session_ctx.__aexit__(None, None, None)
if hasattr(self, 'transport'):
await self.transport.__aexit__(None, None, None)
print("✓ already disconnect")
except Exception as e:
print(f"✗ disconnect error: {e}")
def parse_result(self, result):
"""Parse MCP tool call result"""
try:
if hasattr(result, 'content') and result.content:
content = result.content[0]
if hasattr(content, 'text'):
return json.loads(content.text)
return str(result)
except Exception as e:
return {"error": f"parse error: {e}", "raw": str(result)}This workflow predicts protein-ligand binding affinity using the Boltz-2 deep learning model, providing affinity probabilities and 3D complex structures.
Workflow Steps:
Implementation:
## Initialize client
client = DrugSDAClient(
"https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model",
"<your-api-key>"
)
if not await client.connect():
print("connection failed")
exit()
## Input: Protein sequence and ligand SMILES
sequence = 'PIVQNLQGQMVHQCISPRTLNAWVKVVEEKAFSPEVIPMFSALSCGATPQDLNTMLNTVGGHQAAMQMLKETINEEAAEWDRLHPVHAGPIAPGQMREPRGSDIAGTTSTLQEQIGWMTHNPPIPVGEIYKRWIILGLNKIVRMYSPTSILDIRQGPKEPFRDYVDRFYKTLRAEQASQEVKNAATETLLVQNANPDCKTILKALGPGATLEEMMTACQG'
protein = [{'chain': 'A', 'sequence': sequence}]
smiles_list = ['N[C@@H](Cc1ccc(O)cc1)C(=O)O', "CC(C)C1=CC=CC=C1"]
## Execute Boltz-2 binding affinity prediction
result = await client.session.call_tool(
"boltz_binding_affinity",
arguments={
"protein": protein,
"smiles_list": smiles_list
}
)
result_data = client.parse_result(result)
boltz_res = result_data["boltz_res"]
## Display results
for i, item in enumerate(boltz_res, 1):
print(f"{i}. SMILES: {item['smiles']}")
print(f" Affinity Probability: {item['affinity_probability']:.4f}")
print(f" Structure File: {item['cif_file']}\n")
await client.disconnect()DrugSDA-Model Server:
boltz_binding_affinity: Predict protein-ligand binding affinity using Boltz-2protein (list): List of protein chains with sequence information{'chain': str, 'sequence': str}smiles_list (list): List of ligand SMILES stringsboltz_res (list): List of binding predictionssmiles (str): Ligand SMILES stringaffinity_probability (float): Binding affinity probability (0-1)cif_file (str): Path to predicted complex structureInput:
protein: List of protein chainschain: Chain identifier (e.g., 'A', 'B')sequence: Amino acid sequence in single-letter codesmiles_list: List of SMILES strings for ligand moleculesOutput:
smiles: Ligand SMILES stringaffinity_probability: Binding probability (0-1, higher is better)cif_file: Path to predicted protein-ligand complex structure in CIF format© InternScience, 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/boltz2-binding-affinity of InternScience/scp.
Open the folder on GitHubat commit cea5398
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in InternScience/scp, which our catalogue first saw on October 7, 2026.
Boltz2 Binding Affinity 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 |
|---|---|---|---|---|---|---|
| Boltz2 Binding Affinity this skillInternScience/scp | 169 | 1 repos | ~1.4k | Automated safety check: Warn | MIT | |
| MolecodeAtomFlow-AI/MoleCode | 305 | — | ~1.9k | Automated safety check: Pass | MIT | |
| Drug DiscoveryTommy-yw/RunbookHermes | 546 | 1 repos | ~2.3k | Automated safety check: Pass | MIT | |
| DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3k | Automated safety check: Notes | MIT | |
| Biomedical Analysis Dispatchxjtulyc/MedgeClaw | 617 | 1 repos | ~2k | Automated safety check: Pass | None | |
| Edu Chem Reactionwy51ai/edulab | 1.4k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 |
AtomFlow-AI/MoleCode
A skill your agent uses for deterministic molecule understanding, graph-level editing, generation, and validation with MoleCode — an explicit Mermaid graph in which every atom and bond is a typed…
Tommy-yw/RunbookHermes
Pharmaceutical research assistant for drug discovery workflows.
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.
xjtulyc/MedgeClaw
Routes bioinformatics, drug discovery, clinical and multi-omics tasks from a chat interface to Claude Code sessions running K-Dense scientific skills, with a live dashboard per task.
wy51ai/edulab
把一个化学反应做成自包含的微观 3D 交互演示网页:左/上为 Three.js 可交互分子动画 (拖滑块看断键·成键·原子重组,分步高亮),右为 KaTeX 反应方程 + 分步讲解 + 原子守恒计数 + 可选能量-反应进程曲线。支持三入口——给定文字反应/方程、随机出题、上传图片识别后演示。
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…
InternScience/scp
Given an rsID, query multiple databases (dbSNP, FAVOR, GWAS Catalog, ClinVar, gnomAD, PharmGKB, ClinGen) for comprehensive annotation.
InternScience/scp
Use ESMFold model to predict 3D structure of the input protein sequence.
InternScience/scp
Given a protein sequence and its structure, employ ProSST model to predict mutation effects and obtain the top-k mutated sequences.
InternScience/scp
Calculate atmospheric parameters including Coriolis parameter, geostrophic wind, heat index, potential temperature, and dewpoint for meteorology and climate science.
InternScience/scp
Search biomedical literature and web content using Tavily search engine for research and clinical information.
InternScience/scp
Calculate buoyancy forces and acceleration for fluid mechanics and hydrodynamics analysis.
Categories
Predict protein-ligand binding affinity using Boltz-2 model to assess molecular interactions and binding probability for drug discovery. Boltz2 Binding Affinity is an agent skill from InternScience/scp. Predict protein-ligand binding affinity using Boltz-2 model to assess molecular interactions and binding probability for drug discovery.
Boltz2 Binding Affinity fits situations like: tasks that involve Drug discovery and cheminformatics.
Run `npx skills add InternScience/scp --skill boltz2-binding-affinity -a claude-code`. Or copy the skill folder (skills/boltz2-binding-affinity in InternScience/scp) into .claude/skills/boltz2-binding-affinity in your project. Claude Code loads it when a task matches its description.
Run `npx skills add InternScience/scp --skill boltz2-binding-affinity -a codex`. Or copy the skill folder (skills/boltz2-binding-affinity in InternScience/scp) into .agents/skills/boltz2-binding-affinity 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 InternScience/scp --skill boltz2-binding-affinity -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/boltz2-binding-affinity, .gemini/skills/boltz2-binding-affinity, .github/skills/boltz2-binding-affinity and .opencode/skills/boltz2-binding-affinity in your project.
SKILL.md names no scripts, command-line tools or credentials: Boltz2 Binding Affinity is instructions for the agent only. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md flagged 1 warning(s): contains a long base64 blob. Read the flagged lines before installing; the check is not a guarantee either way.
Boltz2 Binding Affinity is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.4k tokens (SKILL.md is roughly 5.8k 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 Boltz2 Binding Affinity: Molecode (AtomFlow-AI/MoleCode, 305 stars), Drug Discovery (Tommy-yw/RunbookHermes, 546 stars), DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars) and Biomedical Analysis Dispatch (xjtulyc/MedgeClaw, 617 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
InternScience (a GitHub organization) maintains it in InternScience/scp, which has 169 GitHub stars. The repository holds 73 skills in this directory. The repository was last updated on June 3, 2026.
Source: InternScience/scp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.