biomed-dispatch

name: biomed-dispatch description: > 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. version: 1.0.0 metadata: openclaw: emoji: "🧬" requires: bins: ["claude"]

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:

   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 with: progress(0%), 研究概要, 分析计划(list), empty steps
   # Start server
   python "$TASK_DIR/dashboard/dashboard_serve.py" --port <free_port> &
   # Tell user the URL immediately: http://localhost:<port>/dashboard/dashboard.html
   

4. Construct the Claude Code prompt — include dashboard update instructions:
   • Which scientific skill(s) to use
   • Input file path(s)
   • Output directory: always $TASK_DIR/output/
   • Dashboard state.json path and update expectations:
     • Update progress after each step
     • Use step panels with desc, code, code_file, outputs
     • Use {"src": "/output/file.csv"} for table references (NOT inline data)
     • Image paths absolute: /output/fig1.png
   • Expected output format (table, figure, report)
5. Execute via Claude Code CLI:

   claude --dangerously-skip-permissions -p "Use available scientific skills. [TASK]. Input: [PATH]. Outputs: $TASK_DIR/output/. Update dashboard at $TASK_DIR/dashboard/state.json after each step (step panels with code + outputs). Completion: openclaw system event --text 'Done: summary' --mode now"
   

6. Monitor — if the task takes >30s, inform the user it is running in background
7. Report back — summarize results, point user to dashboard URL for details

Output handling

• Tables → summarize top rows, mention full file path
• Figures → send the image file to the user directly
• Reports → send the PDF/HTML file to the user directly
• Errors → show the error message and suggest a fix

Example dispatches

Clinical data analysis (complete flow with dashboard):

# 1. Setup
TASK_DIR=data/charls_ace
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/"
# 2. Write initial state.json
# 3. Start dashboard server
python "$TASK_DIR/dashboard/dashboard_serve.py" --port 7790 &
# 4. Dispatch to Claude Code
claude --dangerously-skip-permissions -p "分析 CHARLS 队列中 ACE 与 CVD 的关联。Input: data/charls_ace/charls.dta. Output: data/charls_ace/output/. 每步更新 dashboard state.json（step panels with code + outputs）。完成后: openclaw system event --text 'Done: ACE-CVD分析完成' --mode now"

RNA-seq differential expression:

claude --dangerously-skip-permissions -p "Use DESeq2 scientific skill. Run differential expression. Counts: /workspace/data/counts.csv, metadata: /workspace/data/meta.csv, contrast: treatment vs control. Save to /workspace/data/rnaseq/output/. Update dashboard at /workspace/data/rnaseq/dashboard/state.json."

Single-cell RNA-seq:

claude --dangerously-skip-permissions -p "Use Scanpy scientific skill. Analyze 10X data at /workspace/data/10x/. QC, clustering, markers. Save to /workspace/data/10x/output/. Update dashboard state.json with step panels."

Important rules

• Always save outputs to /workspace/outputs/ — never to /workspace/data/
• Never modify raw data files in /workspace/data/
• If the user's request is ambiguous, ask one clarifying question before dispatching
• If Claude Code returns an error about a missing package, retry with uv pip install [package] prepended to the command
• 涉及中文可视化时，在 prompt 中加入：绘图前先导入 skills/cjk-viz/scripts/setup_cjk_font.py 执行字体检测，不要硬编码字体名