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Flow Panel Designer

该 Skill 为流式细胞术 panel 设计工具,根据目标标志物、仪器型号和颜色数量,输出最优荧光染料分配、光谱溢漏预测、补偿对照列表和 panel 验证检查结果。通过本地 Python 脚本运行,需传入命令行参数,结果可写到标准输出或指定文件。

aipoch-ai
aipoch-ai
热度 79.50
最近更新 2026-05-11 17:34:16
分类:数据依赖:代码执行 / 读写文件应用场景:研究 / 数据处理 / 自动化脚本

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add flow-panel-designer
Metadata
name
flow-panel-designer
description
Design multicolor flow cytometry panels minimizing spectral overlap
version
1.0.0
category
Wet Lab
tags
[]
author
AIPOCH
license
MIT
status
Draft
risk_level
Medium
skill_type
Tool/Script
owner
AIPOCH
reviewer
last_updated
2026-02-06

Flow Panel Designer

Fluorophore selection optimizer.

Use Cases

  • Multicolor panel design (10+ colors)
  • Compensation planning
  • Marker-fluorophore matching
  • Spectral flow setup

Parameters

Parameter Type Default Required Description
--markers string - Yes Comma-separated target antigens
--instrument string - No Cytometer model
--n-colors int 8 No Number of fluorophores
--output, -o string stdout No Output file path

Returns

  • Optimal fluorophore assignments
  • Spillover predictions
  • Compensation control list
  • Panel validation checks

Example

T-cell panel: CD3-BV421, CD4-FITC, CD8-PE...

Risk Assessment

Risk Indicator Assessment Level
Code Execution Python/R scripts executed locally Medium
Network Access No external API calls Low
File System Access Read input files, write output files Medium
Instruction Tampering Standard prompt guidelines Low
Data Exposure Output files saved to workspace Low

Security Checklist

  • No hardcoded credentials or API keys
  • No unauthorized file system access (../)
  • Output does not expose sensitive information
  • Prompt injection protections in place
  • Input file paths validated (no ../ traversal)
  • Output directory restricted to workspace
  • Script execution in sandboxed environment
  • Error messages sanitized (no stack traces exposed)
  • Dependencies audited

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

  • Successfully executes main functionality
  • Output meets quality standards
  • Handles edge cases gracefully
  • Performance is acceptable

Test Cases

  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
    • Performance optimization
    • Additional feature support