biomed-dispatch
Dispatch biomedical research and data analysis tasks to Claude Code with K-Dense Scientific Skills. Use this skill when the user asks to run any bioinformatics, genomics, drug discovery, clinical data analysis, proteomics, multi-omics, medical imaging, or scientific computation task. Also use for literature search (PubMed, bioRxiv), pathway analysis, protein structure prediction, or scientific writing tasks.
npx skills add BioTender-max/awesome-bio-agent-skills --skill biomed-dispatch --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Biomedical Analysis Dispatch ## Purpose Bridge between the OpenClaw conversational interface and Claude Code's scientific execution environment (K-Dense Scientific Skills). ## When to use - Any bioinformatics task: RNA-seq, scRNA-seq, variant calling, sequence analysis - Drug discovery: molecular docking, virtual screening, ADMET prediction - Clinical data: survival analysis, variant interpretation, clinical trials search - Multi-omics: proteomics, metabolomics, pathway enrichment - Medical imaging: DICOM processing, digital pathology - Scientific communication: literature review, scientific writing, figure generation - Any request mentioning specific tools: DESeq2, Seurat, Scanpy, RDKit, BioPython, etc. ## Workflow 1. **Identify task type** from the user's request 2. **Locate data files** — check if user mentioned a file path; if not, list `/workspace/data/` and confirm with user 3. **Set up Dashboard** — every analysis task must have a live dashboard: ```bash TASK_DIR=data/<task_name> mkdir -p "$TASK_DIR/dashboard" "$TASK_DIR/output" cp skills/dashboard/dashboard.html "$TASK_DIR/dashboard/" cp skills/dashboard/dashboard_serve.py "$TASK_DIR/dashboard/" # Write initial state.json
- Purpose
- When to use
- Workflow
- 科学写作任务的特殊处理
- Output handling
- Example dispatches
- 输出路径约束（重要）
- Important rules
mkdir -p "$TASK_DIR/dashboard" "$TASK_DIR/output" cp skills/dashboard/dashboard.html "$TASK_DIR/dashboard/" cp skills/dashboard/dashboard_serve.py "$TASK_DIR/dashboard/" python "$TASK_DIR/dashboard/dashboard_serve.py" --port <free_port> & cd "$TASK_DIR" && claude -p "短任务描述。先读 skill 文件。完成后: openclaw system event --text 'Done: 摘要' --mode now" \ Phase 1: 文献搜索 + 大纲（5-10 分钟） Phase 2: 写正文（分章节，每章 5-10 分钟） cd writing_outputs/<task_name> && claude -p "读 outline.md 的第 1-3 节。用 Edit 工具在 manuscript.tex 中补充这些章节的正文。写完整的学术散文。" \ Phase 3: 创建 BibTeX + 添加引用（5 分钟） Phase 4: 生成图表（5-10 分钟）
What does the biomed-dispatch skill do?
Dispatch biomedical research and data analysis tasks to Claude Code with K-Dense Scientific Skills. Use this skill when the user asks to run any bioinformatics, genomics, drug discovery, clinical data analysis, proteomics, multi-omics, medical imaging, or scientific computation task. Also use for literature search (PubMed, bioRxiv), pathway analysis, protein structure prediction, or scientific writing tasks.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill biomed-dispatch --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.