Skill: aipoch-ai/flow-cytometry-gating-strategist (version 0.1.0)

--- 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)