How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
deepchem
deepchem is an open-source data skill for Claude Code and compatible agents, published by K-Dense-AI. Its author describes it as: “Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best fo…”. The project has 34k stars on GitHub and is available under the MIT license. Add it to your setup with `git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/deepchem`.
What deepchem does
DeepChem is a comprehensive Python library for applying machine learning to chemistry, materials science, and biology. Enable molecular property prediction, drug discovery, materials design, and biomolecule analysis through specialized neural networks, molecular featurization methods, and pretrained models.
Installation
Add deepchem to your agent with:
git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/deepchemAlways review a skill's source before installing it. This command comes from the skill's public repository; the linked repo is the source of truth for exact setup steps.
What's inside
The SKILL.md for deepchem is organised into these sections:
- Overview
- When to Use This Skill
- Core Capabilities
- Example Scripts
- 1. predictsolubility.py
- 2. graphneuralnetwork.py
- 3. transferlearning.py
- Common Patterns and Best Practices
- Pattern 1: Always Use Scaffold Splitting for Molecules
- Pattern 2: Normalize Features and Targets
- Pattern 3: Start Simple, Then Scale
- Pattern 4: Handle Imbalanced Data
When to use it
Reach for deepchem when you want data help from your agent without writing the same instructions every session. Load the skill and the agent picks it up automatically for relevant tasks.
Strengths
- Clear MIT license — safe to read and adapt
- Ships in K-Dense-AI/scientific-agent-skills, an established project with 33,821 GitHub stars
- Actively maintained (recent commits)
Topics
Frequently asked questions
- What does deepchem do?
- Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
- How do I install deepchem?
- Run git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/deepchem in your agent, then reload your skills. Review the source at https://github.com/K-Dense-AI/scientific-agent-skills before installing.
- Is deepchem free to use?
- Yes. deepchem is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.
- Where does deepchem come from?
- deepchem ships inside K-Dense-AI/scientific-agent-skills, a repository that contains 41 catalogued skills in total. The repository's 33,821 GitHub stars apply to that whole collection, not to this skill on its own.
Related skills
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Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.