staff-training-health
Design healthcare staff training programs. TRIGGERS - Use when user needs help with staff-training-health related tasks.
Generate qPCR/ELISA dilution protocols with precise pipetting steps
--- name: serial-dilution-calculator description: Generate qPCR/ELISA dilution protocols with precise pipetting steps 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' --- # Serial Dilution Calculator Step-by-step dilution protocol generator. ## Use Cases - qPCR standard curves - ELISA plate setup - Drug dose responses - MIC determinations ## Parameters - `starting_conc`: Stock concentration - `final_conc`: Target concentration - `dilution_factor`: Step dilution - `total_volume`: Per well volume ## Returns - Pipetting scheme table - Required volumes - Plate layout suggestion - Common pitfall warnings ## Example "Take 10uL stock + 90uL diluent for 1:10..." ## 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--- name: serial-dilution-calculator description: Generate qPCR/ELISA dilution protocols with precise pipetting steps 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' --- # Serial Dilution Calculator Step-by-step dilution protocol generator. ## Use Cases - qPCR standard curves - ELISA plate setup - Drug dose responses - MIC determinations ## Parameters - `starting_conc`: Stock concentration - `final_conc`: Target concentration - `dilution_factor`: Step dilution - `total_volume`: Per well volume ## Returns - Pipetting scheme table - Required volumes - Plate layout suggestion - Common pitfall warnings ## Example "Take 10uL stock + 90uL diluent for 1:10..." ## 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
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Includes source scripts; review them before use. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.