安全扫描（ClawHub）

• VirusTotal ：良性
• OpenClaw ：可疑

OpenClaw 评估

The skill mostly matches its stated purpose (local recommendations for flow-cytometry gating) but contains inconsistencies between its manifest (claims of network/API use and high risk) and the declared requirements (no network, no env vars), and the shipped script is truncated so I cannot fully confirm there are no hidden network or exfiltration behaviors.

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 Cytometry Gating Strategist」。简介：Recommend optimal flow cytometry gating strategies for specific cell types and …。
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-cytometry-gating-strategist/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
name: flow-cytometry-gating-strategist
description: Recommend optimal flow cytometry gating strategies for specific cell
  types and fluorophores
version: 1.0.0
category: Bioinfo
tags: []
author: AIPOCH
license: MIT
status: Draft
risk_level: High
skill_type: Hybrid (Tool/Script + Network/API)
owner: AIPOCH
reviewer: ''
last_updated: '2026-02-06'
---

# Skill: Flow Cytometry Gating Strategist

Recommend optimal flow cytometry gating strategies for given cell types and fluorophores.

## Basic Information

- **ID**: 103
- **Name**: Flow Cytometry Gating Strategist
- **Purpose**: Flow cytometry data analysis and gating strategy recommendations

## Usage

### Command Line

```bash
# Recommended format: comma-separated cell types and fluorophores
python scripts/main.py "CD4+ T cells,CD8+ T cells" "FITC,PE,APC"

# Or specify parameters separately
python scripts/main.py --cell-types "CD4+ T cells,CD8+ T cells" --fluorophores "FITC,PE,APC"

# Support more options
python scripts/main.py 
  --cell-types "B cells" 
  --fluorophores "FITC,PE,PerCP-Cy5.5,APC" 
  --instrument "BD FACSCanto II" 
  --purpose "cell sorting"
```

## Parameters

| Parameter | Type | Default | Required | Description |
|-----------|------|---------|----------|-------------|
| `--cell-types` | string | - | Yes | Comma-separated list of cell types (e.g., "CD4+ T cells,CD8+ T cells") |
| `--fluorophores` | string | - | Yes | Comma-separated list of fluorophores (e.g., "FITC,PE,APC") |
| `--instrument` | string | - | No | Flow cytometer model (e.g., "BD FACSCanto II") |
| `--purpose` | string | analysis | No | Purpose (analysis, cell sorting, screening) |
| `--output`, `-o` | string | stdout | No | Output file path for JSON results |

### Output Format

```json
{
  "recommended_strategy": {
    "name": "Sequential Gating Strategy",
    "description": "Gating based on FSC-A/SSC-A, followed by fluorescence intensity analysis",
    "steps": [
      {
        "step": 1,
        "gate": "FSC-A vs SSC-A",
        "purpose": "Identify target cell population, exclude debris and dead cells",
        "recommendation": "Set oval gate in lymphocyte region"
      }
    ]
  },
  "fluorophore_recommendations": [
    {
      "fluorophore": "FITC",
      "channel": "BL1",
      "detector": "530/30",
      "considerations": ["May spillover with GFP"]
    }
  ],
  "panel_optimization": {
    "suggestions": ["Recommend pairing weakly expressed antigens with bright fluorophores"],
    "avoid_combinations": ["FITC and GFP used simultaneously"]
  },
  "compensation_notes": ["FITC and PE require careful compensation"],
  "quality_control": ["Recommend setting FMO controls", "Use viability dyes to exclude dead cells"]
}
```

## Supported Cell Types

- **T cells**: CD4+ T cells, CD8+ T cells, Treg cells, Th1, Th2, Th17, γδ T cells
- **B cells**: B cells, Plasma cells, Memory B cells, Naive B cells
- **Myeloid cells**: Monocytes, Macrophages, Dendritic cells, Neutrophils, Eosinophils
- **Stem cells**: HSC, MSC, iPSC
- **Tumor cells**: Tumor cells, Cancer stem cells
- **Others**: NK cells, NKT cells, Platelets, Erythrocytes

## Supported Fluorophores

| Fluorophore | Excitation Wavelength | Emission Wavelength | Detection Channel |
|------|---------|---------|---------|
| FITC | 488nm | 525nm | BL1 |
| PE | 488nm | 575nm | YL1/BL2 |
| PerCP | 488nm | 675nm | RL1 |
| PerCP-Cy5.5 | 488nm | 695nm | RL1 |
| PE-Cy7 | 488nm | 785nm | RL2 |
| APC | 640nm | 660nm | RL1 |
| APC-Cy7 | 640nm | 785nm | RL2 |
| BV421 | 405nm | 421nm | VL1 |
| BV510 | 405nm | 510nm | VL2 |
| BV605 | 405nm | 605nm | VL3 |
| BV650 | 405nm | 650nm | VL4 |
| BV785 | 405nm | 785nm | VL6 |
| DAPI | 355nm | 461nm | UV |
| PI | 488nm | 617nm | YL2 |

## Gating Strategy Types

### 1. Sequential Gating
Applicable scenario: Simple immunophenotyping analysis
- FSC-A/SSC-A → Exclude debris/dead cells → Fluorescence intensity analysis

### 2. Boolean Gating
Applicable scenario: Complex cell subset analysis
- Use logical operators (AND, OR, NOT) to define cell populations

### 3. Dimensionality Reduction Gating
Applicable scenario: High-dimensional data (>15 colors)
- t-SNE/UMAP visualization-assisted gating

### 4. Unsupervised Clustering
Applicable scenario: Discovery of unknown cell populations
- FlowSOM, PhenoGraph and other algorithms

## Notes

1. **Spectral Overlap Compensation**: Multi-color panels must undergo compensation calculation
2. **Control Setup**: Must use FMO (fluorescence minus one) and isotype controls
3. **Dead Cell Exclusion**: Strongly recommend using viability dyes
4. **Instrument Calibration**: Perform QC and standard bead detection before experiments

## Dependencies

- Python 3.8+
- No external dependencies (pure Python standard library)

## Version

v1.0.0 - Initial version, supports basic gating strategy recommendations

## Risk Assessment

| Risk Indicator | Assessment | Level |
|----------------|------------|-------|
| Code Execution | Python scripts with tools | High |
| Network Access | External API calls | High |
| File System Access | Read/write data | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Data handled securely | Medium |

## Security Checklist

- [ ] No hardcoded credentials or API keys
- [ ] No unauthorized file system access (../)
- [ ] Output does not expose sensitive information
- [ ] Prompt injection protections in place
- [ ] API requests use HTTPS only
- [ ] Input validated against allowed patterns
- [ ] API timeout and retry mechanisms implemented
- [ ] Output directory restricted to workspace
- [ ] Script execution in sandboxed environment
- [ ] Error messages sanitized (no internal paths exposed)
- [ ] Dependencies audited
- [ ] No exposure of internal service architecture
## 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