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Primer Design Check

对 qPCR、测序和突变引物做计算机模拟校验:接收正向/反向引物序列与可选模板,给出二聚体预测、发夹分析、脱靶扩增结果以及 Tm 与 GC% 计算。文档声明本地执行且不调用外部 API,但脱靶 BLAST 的具体实现方式未在材料中说明。

aipoch-ai
aipoch-ai
热度 33.15
最近更新 2026-05-11 17:38:29
分类:数据依赖:代码执行 / 读写文件应用场景:数据处理 / 研究

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 primer-design-check
Metadata
name
primer-design-check
description
Check primers for dimers, hairpins, and off-target amplification
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

Primer Design Check

In silico primer validation tool.

Use Cases

  • qPCR primer design
  • Sequencing primer check
  • Mutagenesis primer validation

Parameters

  • forward_primer: F sequence
  • reverse_primer: R sequence
  • template: Target genome (optional)

Returns

  • Dimer prediction
  • Hairpin analysis
  • Off-target BLAST results
  • Tm and GC% calculations

Example

Flags: Self-dimer detected at 3' end → redesign recommended

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