Esmfold2
JimLiu/science-skills
Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al.
Predict binding-related effects (ADMET) using TDC models from Hugging Face
Install Tdc
$ npx skills add lamm-mit/scienceclaw --skill tdc -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install lamm-mit/scienceclaw tdc --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/lamm-mit/scienceclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tdc .claude/skills/tdc && 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 "tdc" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/tdc into .claude/skills/tdc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tdc", 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/lamm-mit/scienceclaw/tree/main/skills/tdcType 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 lamm-mit/scienceclaw --skill tdc -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install lamm-mit/scienceclaw tdc --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/tdc .agents/skills/tdc && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "tdc" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/tdc into .agents/skills/tdc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tdc", 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 lamm-mit/scienceclaw --skill tdc -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install lamm-mit/scienceclaw tdc --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/tdc .cursor/skills/tdc && 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 "tdc" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/tdc into .cursor/skills/tdc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tdc", 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/lamm-mit/scienceclaw.git --path skills/tdc--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 lamm-mit/scienceclaw --skill tdc -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install lamm-mit/scienceclaw tdc --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/tdc .gemini/skills/tdc && 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 "tdc" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/tdc into .gemini/skills/tdc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tdc", 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 lamm-mit/scienceclaw tdcInstalls 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 lamm-mit/scienceclaw --skill tdc -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/tdc .github/skills/tdc && 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 "tdc" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/tdc into .github/skills/tdc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tdc", 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 lamm-mit/scienceclaw --skill tdc -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install lamm-mit/scienceclaw tdc --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/tdc .opencode/skills/tdc && 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 "tdc" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/tdc into .opencode/skills/tdc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tdc", 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
tdc- GitHub stars
- 244
- Token cost
- ~631 tokens
- SKILL.md length
- 185 words
- Files
- 3 (incl. scripts)
- Skills in repo
- 85
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Predict binding-related effects (ADMET) using TDC models from Hugging Face
- Tasks that involve Drug discovery and cheminformatics
- SKILL.md covers Overview, Prerequisites, Usage and Parameters, plus 1 more section
- Runs Python scripts from its folder; calls conda and pip
- Tasks that involve Model hubs and datasets
What it does
Tdc is an agent skill from lamm-mit/scienceclaw. Predict binding-related effects (ADMET) using TDC models from Hugging Face
Its SKILL.md is about 630 tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `scripts/tdc_predict.py`).
It sits in AI & LLM Engineering, covering Drug discovery and cheminformatics and Model hubs and datasets. It works with Hugging Face. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Drug discovery and cheminformatics
- Tasks that involve Model hubs and datasets
Example prompts
- “/tdc”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit ab9aba1. 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
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
condapip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
huggingface.cotdcommons.ai
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
Tdc loads about 631 tokens when it runs. Until then it costs about 20 tokens; SKILL.md has 185 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.
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); the scripts in this folder are not scanned.
SKILL.md
The full file from lamm-mit/scienceclaw at commit ab9aba1, republished under its Apache-2.0 licence (© lamm-mit). 185 words, ~631 tokens.
Download SKILL.mdSave it as
.claude/skills/tdc/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- tdc
- description
- Predict binding-related effects (ADMET) using TDC models from Hugging Face
TDC – Binding Effect Prediction
Predict binding-related effects for small molecules using pre-trained models from Therapeutics Data Commons (TDC) on Hugging Face. Uses SMILES as input and returns classification or scores.
Overview
- Blood–brain barrier (BBB): Will the compound cross the BBB? (binary)
- hERG blockade: Cardiotoxicity risk – does it block hERG? (binary)
- CYP3A4 inhibition: Metabolism – does it inhibit CYP3A4? (binary)
Models: AttentiveFP (graph), CNN, or Morgan fingerprints. Same task, different architectures.
Prerequisites
Install TDC and DeepPurpose (optional; needed for prediction). See ScienceClaw requirements.txt or:
pip install PyTDC DeepPurpose
pip install 'dgl' 'torch'Usage
Run with the conda environment tdc (PyTDC/DGL are installed there). Use: conda run -n tdc python ... or activate the env first.
Predict with one model (SMILES required)
conda run -n tdc python {baseDir}/scripts/tdc_predict.py --smiles "CC(=O)OC1=CC=CC=C1C(=O)O" --model BBB_Martins-AttentiveFPPredict hERG blockade (cardiotoxicity)
conda run -n tdc python {baseDir}/scripts/tdc_predict.py --smiles "CN1C=NC2=C1C(=O)N(C(=O)N2C)C" --model herg_karim-AttentiveFPList available models
conda run -n tdc python {baseDir}/scripts/tdc_predict.py --list-modelsParameters
| Parameter | Description | Default |
|---|---|---|
--smiles | Single SMILES string | - |
--smiles-file | File with one SMILES per line | - |
--model | TDC model name (see --list-models) | BBB_Martins-AttentiveFP |
--list-models | Print available models and exit | - |
--format | Output: summary, json | summary |
Notes
- First run downloads the model from Hugging Face (cached in
~/.scienceclaw/tdc_models). - Input must be valid SMILES; get SMILES from PubChem or ChEMBL if you have a name or ID.
- References: TDC, Hugging Face tdc.
© lamm-mit, Apache-2.0. 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 2 other files (scripts) in skills/tdc of lamm-mit/scienceclaw.
- SKILL.md
- scripts/__pycache__/tdc_predict.cpython-313.pyc
- scripts/tdc_predict.py
Open the folder on GitHubat commit ab9aba1
Compare with similar skills
Tdc 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 |
|---|---|---|---|---|---|---|
| Tdc this skilllamm-mit/scienceclaw | 244 | — | ~631 | Automated safety check: Pass | Apache-2.0 | |
| Esmfold2JimLiu/science-skills | 227 | 4 repos | ~2.5k | Automated safety check: Pass | Apache-2.0 | |
| Kermt Continue PretrainNVIDIA/skills | 3.5k | 1 repos | ~4.1k | Automated safety check: Pass | Apache-2.0 | |
| Kermt EmbedNVIDIA/skills | 3.5k | 1 repos | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| ML Dataset DiscoveryOpenLAIR/dr-claw | 1.2k | — | ~741 | Automated safety check: Pass | Custom licence | |
| Hugging Sciencemajiayu000/claude-skill-registry | 666 | 2 repos | ~2.4k | Automated safety check: Notes | MIT |
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Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint.
ML Dataset Discovery
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Searches Hugging Face Hub, OpenML, GitHub and paper references for datasets that fit a research task and returns a ranked, de-duplicated table.
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Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
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Related
Works with
Categories
Questions about Tdc
What does Tdc do?
Predict binding-related effects (ADMET) using TDC models from Hugging Face. Tdc is an agent skill from lamm-mit/scienceclaw.
When should I use Tdc?
Tdc fits situations like: tasks that involve Drug discovery and cheminformatics; tasks that involve Model hubs and datasets.
How do I install Tdc in Claude Code?
Run `npx skills add lamm-mit/scienceclaw --skill tdc -a claude-code`. Or copy the skill folder (skills/tdc in lamm-mit/scienceclaw) into .claude/skills/tdc in your project. Claude Code loads it when a task matches its description.
How do I install Tdc in Codex?
Run `npx skills add lamm-mit/scienceclaw --skill tdc -a codex`. Or copy the skill folder (skills/tdc in lamm-mit/scienceclaw) into .agents/skills/tdc in your project. Codex loads it when a task matches its description.
Can I use Tdc 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 lamm-mit/scienceclaw --skill tdc -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tdc, .gemini/skills/tdc, .github/skills/tdc and .opencode/skills/tdc in your project.
What does Tdc need to run?
Going by SKILL.md and its folder, Tdc needs Python for the scripts in its folder and the command-line tools its instructions call (conda and pip). Our summary lists: Python 3.
Does Tdc access the network?
SKILL.md names 2 domains. As links in the text: huggingface.co and tdcommons.ai. This is read from the text; nothing was executed.
Is Tdc 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Tdc use?
Tdc is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Tdc use?
About 631 tokens (SKILL.md is roughly 2.5k 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 Tdc?
Skills that share tags, products or a category with Tdc: Esmfold2 (JimLiu/science-skills, 227 stars), Kermt Continue Pretrain (NVIDIA/skills, 3.5k stars), Kermt Embed (NVIDIA/skills, 3.5k stars) and ML Dataset Discovery (OpenLAIR/dr-claw, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Tdc?
lamm-mit (a GitHub user) maintains it in lamm-mit/scienceclaw, which has 244 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on August 21, 2026.
Source: lamm-mit/scienceclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.