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…
Calculate different types of molecular properties based on SMILES strings, covering basic physicochemical properties, hydrophobicity, hydrogen bonding capability, molecular complexity, topological…
$ npx skills add InternScience/scp --skill drugsda-mol-properties -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install InternScience/scp drugsda-mol-properties --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/drugsda-mol-properties .claude/skills/drugsda-mol-properties && 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 "drugsda-mol-properties" agent skill from https://github.com/InternScience/scp/tree/main/skills/drugsda-mol-properties into .claude/skills/drugsda-mol-properties/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drugsda-mol-properties", 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/drugsda-mol-propertiesType 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 drugsda-mol-properties -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install InternScience/scp drugsda-mol-properties --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/drugsda-mol-properties .agents/skills/drugsda-mol-properties && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "drugsda-mol-properties" agent skill from https://github.com/InternScience/scp/tree/main/skills/drugsda-mol-properties into .agents/skills/drugsda-mol-properties/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drugsda-mol-properties", 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 drugsda-mol-properties -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install InternScience/scp drugsda-mol-properties --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/drugsda-mol-properties .cursor/skills/drugsda-mol-properties && 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 "drugsda-mol-properties" agent skill from https://github.com/InternScience/scp/tree/main/skills/drugsda-mol-properties into .cursor/skills/drugsda-mol-properties/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drugsda-mol-properties", 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/drugsda-mol-properties--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 drugsda-mol-properties -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install InternScience/scp drugsda-mol-properties --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/drugsda-mol-properties .gemini/skills/drugsda-mol-properties && 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 "drugsda-mol-properties" agent skill from https://github.com/InternScience/scp/tree/main/skills/drugsda-mol-properties into .gemini/skills/drugsda-mol-properties/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drugsda-mol-properties", 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 drugsda-mol-propertiesInstalls 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 drugsda-mol-properties -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/drugsda-mol-properties .github/skills/drugsda-mol-properties && 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 "drugsda-mol-properties" agent skill from https://github.com/InternScience/scp/tree/main/skills/drugsda-mol-properties into .github/skills/drugsda-mol-properties/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drugsda-mol-properties", 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 drugsda-mol-properties -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 drugsda-mol-properties --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/drugsda-mol-properties .opencode/skills/drugsda-mol-properties && 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 "drugsda-mol-properties" agent skill from https://github.com/InternScience/scp/tree/main/skills/drugsda-mol-properties into .opencode/skills/drugsda-mol-properties/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drugsda-mol-properties", 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.
drugsda-mol-propertiesCalculate different types of molecular properties based on SMILES strings, covering basic physicochemical properties, hydrophobicity, hydrogen bonding capability, molecular complexity, topological…
Drugsda Mol Properties is an agent skill from InternScience/scp. Calculate different types of molecular properties based on SMILES strings, covering basic physicochemical properties, hydrophobicity, hydrogen bonding capability, molecular complexity, topological structures, charge distribution, and custom complexity metrics, respectively.
Its SKILL.md is about 2.1k 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 Drug discovery and cheminformatics. The licence is MIT.
3 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 tex and 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.
Drugsda Mol Properties loads about 2.1k tokens when it runs. Until then it costs about 74 tokens; SKILL.md has 40 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 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.
The full file from InternScience/scp at commit cea5398, republished under its MIT licence (© InternScience). 40 words, ~2,124 tokens.
.claude/skills/drugsda-mol-properties/SKILL.md (or your agent's skills folder).import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class DrugSDAClient:
def __init__(self, server_url: str):
self.server_url = server_url
self.session = None
async def connect(self):
print(f"server url: {self.server_url}")
try:
self.transport = streamablehttp_client(
url=self.server_url,
headers={"SCP-HUB-API-KEY": "sk-a0033dde-b3cd-413b-adbe-980bc78d6126"}
)
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):
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):
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)}Tool 1: calculate_mol_basic_info
Compute a set of basic molecular properties for each SMILES.
Args:
smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
status (str): success/error
msg (str): message
metrics (List[dict]): List of dict, each containing feature keys.
--smiles (str): A SMILES string of smiles_list
--molecular_formula (str): Molecular formula, e.g. "C9H11NO3"
--exact_molecular_weight (float): Exact molecular weight
--molecular_weight (float): Average molecular weight
--num_heavy_atoms (int): Number of heavy atoms
--num_atoms (int): Number of total atoms
--num_bonds (int): Number of bonds
--num_valence_electrons (int): Number of valence electrons
--formal_charge (int): Number of formal chargeTool 2: calculate_mol_hydrophobicity
Compute hydrophobicity-related molecular descriptors for each SMILES.
Args:
smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
status (str): success/error
msg (str): message
metrics (List[dict]): List of dict, each containing feature keys.
--smiles (str): A SMILES string of smiles_list
--logp (float): The octanol-water partition coefficient (logP)
--molar_refractivity (float): Molar refractivityTool 3: calculate_mol_hbond
Compute hydrogen bonding-related properties for each SMILES.
Args:
smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
status (str): success/error
msg (str): message
metrics (List[dict]): List of dict, each containing several feature keys.
--smiles (str): A SMILES string of smiles_list
--num_h_donors (int): Number of hydrogen bond donors
--num_h_acceptors (int): Number of hydrogen bond acceptorsTool 4: calculate_mol_structure_complexity
Compute a set of molecular complexity descriptors for each SMILES.
Args:
smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
status (str): success/error
msg (str): message
metrics (List[dict]): List of dict, each containing feature keys.
--smiles (str): A SMILES string of smiles_list
--num_rotatable_bonds (int): Number of rotatable bonds
--num_rings (int): Number of total rings
--num_aromatic_rings (int): Number of aromatic rings
--num_aliphatic_rings (int): Number of aliphatic rings
--num_saturated_rings (int): Number of saturated rings
--num_heteroatoms (int): Number of heteroatoms
--fraction_csp3 (float): The fraction of sp³-hybridized carbon atoms (Fsp³)
--num_bridgehead_atoms (int): Number of bridgehead atomsTool 5: calculate_mol_topology
Compute a set of topological descriptors for each SMILES.
Args:
smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
status (str): success/error
msg (str): message
metrics (List[dict]): List of dict, each containing several feature keys.
--smiles (str): A SMILES string of smiles_list
--tpsa (float): Topological polar surface area
--chi0v (float): Non-valence molecular connectivity index
--chi1v (float): Non-valence molecular connectivity index
--chi2v (float): Non-valence molecular connectivity index
--chi3v (float): Non-valence molecular connectivity index
--chi4v (float): Non-valence molecular connectivity index
--chi0n (float): Non-valence molecular connectivity index
--chi1n (float): Non-valence molecular connectivity index
--chi2n (float): Non-valence molecular connectivity index
--chi3n (float): Non-valence molecular connectivity index
--chi4n (float): Non-valence molecular connectivity index
--hall_kier_alpha (float): Hall–Kier alpha value
--kappa1 (float): Kappa shape index
--kappa2 (float): Kappa shape index
--kappa3 (float): Kappa shape indexTool 6: calculate_mol_charge
Compute Gasteiger partial charges and formal charge for each SMILES.
Args:
smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
status (str): success/error
msg (str): message
metrics (List[dict]): List of dict, each containing several feature keys.
--smiles (str): A SMILES string of smiles_list
--min_gasteiger_charge (float): Minimum of Gasteiger charges
--max_gasteiger_charge (float): Maximum of Gasteiger charges
--avg_gasteiger_charge (float): Average of Gasteiger charges
--gasteiger_charge_range (float): Range of Gasteiger charges
--formal_charge (int): Formal chargeTool 7: calculate_mol_complexity
Compute custom molecular complexity-related descriptors for each SMILES.
Args:
smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
status (str): success/error
msg (str): message
metrics (List[dict]): List of dict, each containing feature keys.
--smiles (str): A SMILES string of smiles_list
--molecular_complexity (int): Molecular complexity
--aromatic_proportion (float): Aromatic proportion
--asphericity (float): AsphericityHow to use these tools:
client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
if not await client.connect():
print("connection failed")
return
## The tool can be replaced with another based on actual requirements.
response = await client.session.call_tool(
"calculate_mol_basic_info",
arguments={
"smiles_list": smiles_list
}
)
result = client.parse_result(response)
metrics = result["metrics"]
await client.disconnect() © 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/drugsda-mol-properties 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.
Drugsda Mol Properties 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 |
|---|---|---|---|---|---|---|
| Drugsda Mol Properties this skillInternScience/scp | 170 | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| MolecodeAtomFlow-AI/MoleCode | 306 | — | ~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 | |
| Biopipelineslocbp-uzh/biopipelines | 109 | — | ~2.4k | Automated safety check: Pass | MIT |
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.
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…
wu-yc/LabClaw
Retrieves chemical compound information from PubChem and ChEMBL with disambiguation, cross-referencing, and quality assessment.
InternScience/scp
Given an rsID, query multiple databases (dbSNP, FAVOR, GWAS Catalog, ClinVar, gnomAD, PharmGKB, ClinGen) for comprehensive annotation.
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.
InternScience/scp
Calculate electrical capacitance from geometric parameters and dielectric properties for circuit design.
InternScience/scp
Search ChEMBL database for molecule information by name to retrieve bioactivity data and chemical structures.
Categories
Calculate different types of molecular properties based on SMILES strings, covering basic physicochemical properties, hydrophobicity, hydrogen bonding capability, molecular complexity, topological…. Drugsda Mol Properties is an agent skill from InternScience/scp. Calculate different types of molecular properties based on SMILES strings, covering basic physicochemical properties, hydrophobicity, hydrogen bonding capability, molecular complexity, topological structures, charge distribution, and custom complexity metrics, respectively.
Drugsda Mol Properties fits situations like: tasks that involve Drug discovery and cheminformatics.
Run `npx skills add InternScience/scp --skill drugsda-mol-properties -a claude-code`. Or copy the skill folder (skills/drugsda-mol-properties in InternScience/scp) into .claude/skills/drugsda-mol-properties in your project. Claude Code loads it when a task matches its description.
Run `npx skills add InternScience/scp --skill drugsda-mol-properties -a codex`. Or copy the skill folder (skills/drugsda-mol-properties in InternScience/scp) into .agents/skills/drugsda-mol-properties 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 drugsda-mol-properties -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/drugsda-mol-properties, .gemini/skills/drugsda-mol-properties, .github/skills/drugsda-mol-properties and .opencode/skills/drugsda-mol-properties in your project.
SKILL.md names no scripts, command-line tools or credentials: Drugsda Mol Properties 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 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.
Drugsda Mol Properties is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.1k tokens (SKILL.md is roughly 8.5k 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 Drugsda Mol Properties: Molecode (AtomFlow-AI/MoleCode, 306 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 170 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.