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`.
clinical-decision-support
clinical-decision-support is an open-source data skill for Claude Code and compatible agents, published by K-Dense-AI. Its author describes it as: “Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation an…”. 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/clinical-decision-support`.
What clinical-decision-support does
This skill produces **research, evaluation, documentation, and governance artifacts only**.
Installation
Add clinical-decision-support to your agent with:
git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/clinical-decision-supportAlways 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 clinical-decision-support is organised into these sections:
- Hard Safety Boundary
- In Scope
- Data Gate
- Required Artifact Header
- Workflow
- 1. Frame the Research Question
- 2. Select the Artifact
- 3. Run Locally
- 4. Human Review
- GRADE Evidence Profiles
- Aggregate Model and Biomarker Evaluation
- Cohort Tables
When to use it
Reach for clinical-decision-support 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 clinical-decision-support do?
- Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.
- How do I install clinical-decision-support?
- Run git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/clinical-decision-support in your agent, then reload your skills. Review the source at https://github.com/K-Dense-AI/scientific-agent-skills before installing.
- Is clinical-decision-support free to use?
- Yes. clinical-decision-support is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.
- Where does clinical-decision-support come from?
- clinical-decision-support 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.