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Agent skill

Smiles To Cas Conversion

by InternScience in InternScience/scp

Convert SMILES strings to CAS registry numbers using material informatics tools to identify chemical substances.

MITAuto-check passedResearch & Science

Install Smiles To Cas Conversion

skills CLI
$ npx skills add InternScience/scp --skill smiles-to-cas-conversion -a claude-code

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

GitHub CLI
$ gh skill install InternScience/scp smiles-to-cas-conversion --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/InternScience/scp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/smiles-to-cas-conversion .claude/skills/smiles-to-cas-conversion && 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
smiles-to-cas-conversion
GitHub stars
170
Used in
1 other repo
Token cost
~798 tokens
SKILL.md length
59 words
Files
1
Skills in repo
73
Repo updated
First seen
Licence
MIT

At a glance

Convert SMILES strings to CAS registry numbers using material informatics tools to identify chemical substances.

  • Works in 2 steps: MCP Server Definition → SMILES to CAS Conversion Workflow
  • Tasks that involve Drug discovery and cheminformatics
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Smiles To Cas Conversion is an agent skill from InternScience/scp. Convert SMILES strings to CAS registry numbers using material informatics tools to identify chemical substances.

Its SKILL.md is about 800 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.

When your agent uses it

  • Tasks that involve Drug discovery and cheminformatics

Example prompts

  • “/smiles-to-cas-conversion”

Requirements

  • Python 3

Workflow steps

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

  1. MCP Server Definition
  2. SMILES to CAS Conversion Workflow

What it can do on your machine

Read from SKILL.md and the folder at commit cea5398. 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

    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.

  • Network

    No URLs in SKILL.md.

    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

Smiles To Cas Conversion loads about 798 tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 59 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~798

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 InternScience/scp at commit cea5398, republished under its MIT licence (© InternScience). 59 words, ~798 tokens.

Download SKILL.mdSave it as .claude/skills/smiles-to-cas-conversion/SKILL.md (or your agent's skills folder).
name
smiles-to-cas-conversion
description
Convert SMILES strings to CAS registry numbers using material informatics tools to identify chemical substances.
license
MIT license
metadata.skill-author
PJLab

SMILES to CAS Conversion

Usage

1. MCP Server Definition
python
import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

class MaterialToolsClient:
    """SciToolAgent-Mat 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):
        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()
            return True
        except Exception as e:
            print(f"✗ connect failure: {e}")
            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)
        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'):
                    try:
                        return json.loads(content.text)
                    except:
                        return content.text
            return str(result)
        except Exception as e:
            return {"error": f"parse error: {e}", "raw": str(result)}
2. SMILES to CAS Conversion Workflow

Convert SMILES notation to CAS registry numbers.

Implementation:

python
## Initialize client
client = MaterialToolsClient(
    "https://scp.intern-ai.org.cn/api/v1/mcp/30/SciToolAgent-Mat",
    "<your-api-key>"
)

if not await client.connect():
    print("connection failed")
    exit()

## Input: SMILES strings
smiles_list = [
    "CCO",           # Ethanol
    "CC(=O)O",       # Acetic acid
    "c1ccccc1"       # Benzene
]

print("SMILES to CAS Conversion:")
for smiles in smiles_list:
    result = await client.session.call_tool(
        "SMILESToCAS",
        arguments={"smiles": smiles}
    )
    result_data = client.parse_result(result)
    print(f"SMILES: {smiles}")
    print(f"Result: {result_data}\n")

await client.disconnect()
Tool Descriptions

SciToolAgent-Mat Server:

  • SMILESToCAS: Convert SMILES to CAS registry number
    • Args:
      • smiles (str): SMILES notation
    • Returns: CAS registry number
Use Cases
  • Chemical substance identification
  • Regulatory compliance checking
  • Material safety data sheet lookup
  • Chemical inventory management

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

Files

Just SKILL.md in skills/smiles-to-cas-conversion of InternScience/scp.

Open the folder on GitHubat commit cea5398

Used in 2 other repositories

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.

Compare with similar skills

Smiles To Cas Conversion 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.

Smiles To Cas Conversion compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Smiles To Cas Conversion this skillInternScience/scp1701 repos~798Automated safety check: PassMIT
MolecodeAtomFlow-AI/MoleCode306—~1.9kAutomated safety check: PassMIT
Drug DiscoveryTommy-yw/RunbookHermes5461 repos~2.3kAutomated safety check: PassMIT
DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills48k1 repos~3kAutomated safety check: NotesMIT
Biomedical Analysis Dispatchxjtulyc/MedgeClaw6171 repos~2kAutomated safety check: PassNone
Biopipelineslocbp-uzh/biopipelines109—~2.4kAutomated safety check: PassMIT

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Categories

Questions about Smiles To Cas Conversion

What does Smiles To Cas Conversion do?

Convert SMILES strings to CAS registry numbers using material informatics tools to identify chemical substances. Smiles To Cas Conversion is an agent skill from InternScience/scp. Convert SMILES strings to CAS registry numbers using material informatics tools to identify chemical substances.

When should I use Smiles To Cas Conversion?

Smiles To Cas Conversion fits situations like: tasks that involve Drug discovery and cheminformatics.

How do I install Smiles To Cas Conversion in Claude Code?

Run `npx skills add InternScience/scp --skill smiles-to-cas-conversion -a claude-code`. Or copy the skill folder (skills/smiles-to-cas-conversion in InternScience/scp) into .claude/skills/smiles-to-cas-conversion in your project. Claude Code loads it when a task matches its description.

How do I install Smiles To Cas Conversion in Codex?

Run `npx skills add InternScience/scp --skill smiles-to-cas-conversion -a codex`. Or copy the skill folder (skills/smiles-to-cas-conversion in InternScience/scp) into .agents/skills/smiles-to-cas-conversion in your project. Codex loads it when a task matches its description.

Can I use Smiles To Cas Conversion 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 InternScience/scp --skill smiles-to-cas-conversion -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/smiles-to-cas-conversion, .gemini/skills/smiles-to-cas-conversion, .github/skills/smiles-to-cas-conversion and .opencode/skills/smiles-to-cas-conversion in your project.

What does Smiles To Cas Conversion need to run?

SKILL.md names no scripts, command-line tools or credentials: Smiles To Cas Conversion is instructions for the agent only. Our summary lists: Python 3.

Does Smiles To Cas Conversion access the network?

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.

Is Smiles To Cas Conversion 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 Smiles To Cas Conversion use?

Smiles To Cas Conversion 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 Smiles To Cas Conversion use?

About 798 tokens (SKILL.md is roughly 3.2k 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 Smiles To Cas Conversion?

Skills that share tags, products or a category with Smiles To Cas Conversion: 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.

Who maintains Smiles To Cas Conversion?

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.