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tooluniverse-metagenomics-analysis工具宇宙宏基因组分析

Agent Skill

tooluniverse-metagenomics-analysis 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

1,077

周安装

44

GitHub Stars

1,283

下载量

345
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

2

许可证

unknown

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:tooluniverse-metagenomics-analysis(工具宇宙宏基因组分析)
来源仓库:https://github.com/mims-harvard/tooluniverse
仓库路径:skills/tooluniverse-metagenomics-analysis
安装命令:
npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-metagenomics-analysis
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-metagenomics-analysis

简介

用于宏基因组学数据分析与检索,支持在微生物组研究中获取物种和功能注释信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中处理环境样本或临床样本的微生物组成查询。
  • 通过 npx skills add 命令从 GitHub 安装,需确认仓库权限及是否涉及网络访问或外部 API 调用。
  • 建议在使用前核实数据来源更新频率,避免依赖过期或未经验证的信息。
  • 注意该技能主要用于信息检索,不直接提供分析结果,需结合上下文进行解读和应用。

SKILL.md

Metagenomics & Microbiome Analysis

Integrated pipeline for exploring microbiome studies, classifying taxa, assessing genome quality, linking microbial composition to clinical phenotypes, and interpreting findings through pathway analysis and literature context.

Guiding principles:

  1. Study context first -- understand biome, sequencing method, and metadata before diving into taxa
  2. Taxonomic consistency -- GTDB taxonomy as reference standard; reconcile NCBI where needed
  3. Genome quality matters -- CheckM completeness/contamination thresholds determine trustworthy MAGs
  4. Interpretation over enumeration -- explain what taxa mean for the biological question
  5. English-first queries -- use English terms in tool calls

LOOK UP, DON'T GUESS

When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.


COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

Core Databases

DatabaseBest For
MGnifyProcessed metagenomics studies, taxonomic/functional results
GTDBStandardized bacterial/archaeal taxonomy, species-level resolution
GMrepoGut species-to-human-health phenotype associations
ENARaw sequencing datasets and study metadata
KEGGPathway mapping for microbial functional annotations
PubMed/EuropePMCPublished microbiome-disease studies
CTDChemical-microbiome-disease relationships

Workflow

Phase 0: Parse query → organism, biome, phenotype, or accession
Phase 1: Study Discovery → MGnify_search_studies, ENAPortal_search_studies
Phase 2: Taxonomic Classification → GTDB_search_genomes, GTDB_get_species, GTDB_search_taxon
Phase 3: Genome Quality → MGnify_search_genomes, MGnify_get_genome (CheckM metrics)
Phase 4: Functional Annotation → MGnify GO terms + KEGG pathway mapping
Phase 5: Clinical Associations → GMrepo species-phenotype links
Phase 6: Literature → PubMed/EuropePMC + CTD gene-disease
Phase 7: Interpretation & Report Synthesis

Key Phase Notes

Phase 1: ENA requires structured queries (e.g., study_title="*IBD*"), not free text. If ENA fails, fall back to MGnify.

Phase 2: GTDB uses its own naming (e.g., s__Bacteroides_A fragilis vs NCBI Bacteroides fragilis). Always note discrepancies. Use GTDB_search_taxon(operation="search_taxon", query=name).

Phase 3 - Quality tiers (MIMAG):

  • High: >= 90% complete, <= 5% contamination, rRNA + >= 18 tRNAs
  • Medium: >= 50% complete, <= 10% contamination
  • Low: below medium -- flag but don't exclude

Phase 4 - Functional interpretation: Don't just list GO terms. Connect to biology:

Functional CategoryKey KEGG PathwaysSignificance
SCFA productionmap00650, map00640Gut barrier, anti-inflammatory
LPS biosynthesismap00540Pro-inflammatory, endotoxemia
Bile acid metabolismmap00120Fat absorption, FXR signaling
Tryptophan metabolismmap00380Serotonin, AhR, immune
Vitamin biosynthesismap00730/740/760Host nutritional contribution

Use kegg_search_pathway(keyword=...) (NOT query). Pathway IDs need organism prefix (hsa, ko, eco), NOT bare map.

Phase 5: GMrepo uses MeSH terms: "Crohn Disease" not "IBD", "Colitis, Ulcerative" not "UC", "Colorectal Neoplasms" not "colorectal cancer". Try NCBI taxon IDs if species name fails.

Phase 6 - Evidence grading:

  • Strong: Meta-analysis or >5 studies, consistent direction
  • Moderate: 2-5 studies consistent, or 1 large cohort
  • Preliminary: Single study or conflicting
  • Mechanistic only: In vitro/animal, no human epidemiology

Phase 7 - Report: Executive summary, study landscape, GTDB taxonomy, functional interpretation (not GO term lists), clinical relevance with evidence grades, mechanistic model, genome catalog with quality tiers, data gaps.


Edge Cases & Fallbacks

  • Taxon not in GTDB: Try partial search or fall back to MGnify (NCBI taxonomy)
  • No GMrepo data: Normal for non-gut organisms; use literature
  • GMrepo 0 results: Use formal MeSH terms or NCBI taxon IDs
  • No KEGG match: Check MetaCyc or literature

Limitations

  • GMrepo: Gut-only
  • GTDB: Bacteria/Archaea only
  • ENA: Raw data only, strict query syntax
  • No sequence analysis: Queries databases, not raw FASTQ/FASTA

适合场景

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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

能力概览

能力 1

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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

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

Codex

35.39%
按下载量换算122

Claude

30.43%
按下载量换算105

Cursor

18.3%
按下载量换算63

Gemini CLI

9.04%
按下载量换算31

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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来源信息

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