技能详情（站内镜像，无评论）
作者：AIpoch @AIPOCH-AI
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v0.1.0
统计：⭐ 0 · 20 · 0 current installs · 0 all-time installs
⭐ 0
安装量（当前） 0
🛡 VirusTotal ：良性 · OpenClaw ：可疑
Package：aipoch-ai/flow-panel-designer
安全扫描（ClawHub）
- VirusTotal ：良性
- OpenClaw ：可疑
OpenClaw 评估
The skill's code is small and self-contained and appears to implement the stated panel-design functionality, but the SKILL.md and the runtime behavior disagree in several places (parameters and file I/O expectations), which is inconsistent and should be clarified before trusting it.
目的
The skill's name and description (flow cytometry panel design) match the included Python tool: FlowPanelDesigner implements fluorochrome data, overlap calculations, and a simple assignment heuristic. This is coherent for the stated purpose. However, SKILL.md documents parameters (e.g., --output / -o) and behavior (reading input files, writing output files) that the script does not implement, creating a mismatch between documentation and code.
说明范围
SKILL.md refers to file system access, input-file path validation, and an --output parameter; the shipped script only reads a --markers CLI arg (required) and prints results to stdout. Because the documentation implies file read/write and path validation but the code lacks that functionality, an agent following the prose could attempt actions not covered by the code (for example, writing files or validating paths). Also the security checklist …
安装机制
No install specification and no external dependencies: the Python script uses only the standard library (argparse, itertools). No network downloads or third-party packages are requested, which is proportionate for this utility and low risk from an installation perspective.
证书
The skill does not request environment variables, credentials, or config paths. The code contains no network calls or secret-handling logic. This is proportionate to a local data-processing tool.
持久
The skill does not request persistent or elevated privileges (always: false). There is no install step modifying agent/system configuration and no evidence it would persist beyond running the script.
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Flow Panel Designer」。简介：Design multicolor flow cytometry panels minimizing spectral overlap。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/aipoch-ai/flow-panel-designer/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
---
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