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`.
exa-search
exa-search is an open-source data skill for Claude Code and compatible agents, published by K-Dense-AI. Its author describes it as: “Web toolkit powered by Exa, tuned for scientific and technical content. Use this skill when the user needs to search the web or fetch/extract URL content. Covers: web search (semantic lookups, research, current info —…”. 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/exa-search`.
What exa-search does
A skill for web-powered research tasks backed by Exa: web search and URL extraction. Exa's index combines high-quality keyword and semantic retrieval, which makes it well-suited to scientific, technical, and conceptual queries.
Installation
Add exa-search to your agent with:
git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/exa-searchAlways 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 exa-search is organised into these sections:
- Routing — pick the right capability
- Decision guide
- Academic source priority
- Setup
- Authentication
- Tracking header
- Files in this skill
When to use it
Reach for exa-search 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 exa-search do?
- Web toolkit powered by Exa, tuned for scientific and technical content. Use this skill when the user needs to search the web or fetch/extract URL content. Covers: web search (semantic lookups, research, current info — with optional research-paper category and academic domain filtering) and URL extraction (fetching pages, articles, academic PDFs in batch). Use this skill for web-related tasks when the user wants high-quality search or scholarly filtering via category=research paper. Triggers on requests to search, look up, fetch a page, or extract an article.
- How do I install exa-search?
- Run git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/exa-search in your agent, then reload your skills. Review the source at https://github.com/K-Dense-AI/scientific-agent-skills before installing.
- Is exa-search free to use?
- Yes. exa-search is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.
- Where does exa-search come from?
- exa-search 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.
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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.