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protein-assembly蛋白质组装

Agent Skill

protein-assembly 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

888

周安装

37

GitHub Stars

93

下载量

296
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:protein-assembly(蛋白质组装)
来源仓库:https://github.com/letta-ai/skills
仓库路径:skills/protein-assembly
安装命令:
npx skills add https://github.com/letta-ai/skills --skill protein-assembly
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/letta-ai/skills --skill protein-assembly

简介

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 支持基于关键词、任务场景或来源线索进行信息聚合与过滤,适用于蛋白质研究任务。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限范围和维护状态。
  • 安装前建议核实是否会触发联网、命令执行或文件读写等敏感操作。
  • protein-assembly 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Protein Assembly Skill

This skill provides structured guidance for designing fusion protein gBlock sequences that combine multiple protein components (antibody fragments, fluorescent proteins, enzyme domains) into a single optimized DNA construct.

When to Use This Skill

This skill applies to tasks that involve:

  • Designing fusion proteins from multiple sources (PDB, plasmids, protein databases)
  • Creating gBlock sequences with specific linker requirements
  • Codon optimization for GC content constraints
  • Combining fluorescent proteins with specific excitation/emission wavelengths
  • Assembling multi-domain proteins with N-terminal methionine removal

Structured Approach

Phase 1: Information Gathering and Cataloging

Objective: Collect ALL required sequence data before any design work begins.

  1. Inventory input files completely

- Read ALL input files in their entirety (avoid truncated reads) - For GenBank (.gb) files, parse the complete file to extract CDS/protein sequences - For FASTA files, extract all sequences with their identifiers - For PDB ID lists, note all IDs for batch retrieval

  1. Fetch external sequences systematically

- Query PDB API for each protein ID to retrieve amino acid sequences - Query relevant protein databases (e.g., fpbase for fluorescent proteins) - Document each retrieved sequence with its source and identifier

  1. Create a sequence catalog

- List all available protein sequences with clear labels - Note the source of each sequence (PDB ID, plasmid CDS, database) - Identify any missing sequences before proceeding

Phase 2: Protein Identification and Selection

Objective: Match proteins to task requirements using specific criteria.

  1. Wavelength matching for fluorescent proteins

- Search for proteins with exact wavelength matches (not approximate) - Verify both excitation AND emission peaks against requirements - Document the selected donor and acceptor proteins with rationale

  1. Binding domain identification

- Identify proteins that bind specific molecules (substrates, ligands) - Cross-reference PDB entries with known binding partners - Verify binding capability through database annotations

  1. Target protein identification

- For antibody-related tasks, identify the target antigen - Use sequence homology or database lookups as needed - Document the identification method and confidence

Phase 3: Sequence Processing

Objective: Prepare individual protein sequences for fusion.

  1. N-terminal methionine handling

- Remove N-terminal methionines from ALL internal proteins - Keep only the first protein's N-terminal methionine (if required) - Document which sequences were modified

  1. Sequence validation

- Verify each sequence is complete and valid - Check for unusual amino acids or sequence artifacts - Confirm sequences match expected lengths

Phase 4: Fusion Protein Assembly

Objective: Construct the complete fusion protein sequence.

  1. Follow the specified protein order exactly

- Do not deviate from the required arrangement - Document the order: [Protein1]-[Linker]-[Protein2]-[Linker]-...

  1. Design appropriate linkers

- Use GS (Glycine-Serine) linkers of specified length - Common patterns: (GGGGS)n or (GS)n where n provides required length - Ensure linkers fall within length constraints (e.g., 5-20 amino acids)

  1. Assemble the complete protein sequence

- Concatenate proteins with linkers in correct order - Verify the assembled sequence is continuous and valid

Phase 5: Codon Optimization and DNA Generation

Objective: Convert protein to optimized DNA sequence.

  1. Initial codon translation

- Convert each amino acid to a codon - Use a standard codon table for the target organism

  1. GC content optimization

- Calculate GC content in sliding windows (e.g., 50 nucleotides) - Identify windows outside acceptable range (e.g., 30-70%) - Swap synonymous codons to bring GC content within range - Re-verify after each swap

  1. Length verification

- Confirm DNA sequence meets length constraints (e.g., ≤3000 nt) - If too long, review design choices (linker lengths, protein selections)

Phase 6: Output Generation

Objective: Create the required output file(s).

  1. Write output immediately after assembly

- Do not delay output file creation - Write to the exact path specified in requirements

  1. Include appropriate formatting

- Follow any specified format (plain text, FASTA, etc.) - Include headers or metadata if required

  1. Verify output file exists

- Confirm the file was created successfully - Verify file contents match the designed sequence

Verification Checkpoints

After Phase 1:

  • All input files read completely (no truncation)
  • All external sequences retrieved
  • Sequence catalog is complete

After Phase 2:

  • All required proteins identified
  • Wavelength/binding requirements verified
  • Selection rationale documented

After Phase 3:

  • N-terminal methionines handled correctly
  • All sequences validated

After Phase 4:

  • Protein order matches requirements
  • Linkers meet length constraints
  • Complete fusion sequence assembled

After Phase 5:

  • GC content within range in ALL windows
  • DNA length within constraints

After Phase 6:

  • Output file exists at specified path
  • File contents are correct

Common Pitfalls

  1. Incomplete file reading

- GenBank files may be large; ensure complete parsing - Extract CDS translations, not just raw sequences

  1. Approximate wavelength matching

- Use exact values, not "close enough" matches - Verify both excitation AND emission, not just one

  1. Forgetting N-terminal methionines

- Internal proteins in fusions should have Met removed - Only the first protein retains its N-terminal Met

  1. Ignoring GC content windows

- Check ALL sliding windows, not just overall GC% - Optimize problematic regions with synonymous codons

  1. Delayed output generation

- Create output file as soon as sequence is ready - Do not continue gathering information after design is complete

  1. Information gathering loops

- Set a clear stopping point for research - Progress to execution even with incomplete information - A partial solution is better than no solution

Output-First Strategy

If time or resources are constrained:

  1. Create the output file early, even with placeholders
  2. Update the file as each component is determined
  3. Ensure a valid (if imperfect) output exists at task end

This ensures the primary deliverable exists, which can be refined with additional information.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

28.9%
按下载量换算86

Gemini CLI

22.69%
按下载量换算67

Antigravity

17.97%
按下载量换算53

windsurf

12.87%
按下载量换算38

OpenCode

6.38%
按下载量换算19

Codex

3.22%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/letta-ai/skills --skill protein-assembly;npx skills add letta-ai/skills --skill "protein-assembly" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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