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Agent Skill · 研究检索

tooluniverse-structural-variant-analysis(工具宇宙结构变异分析)

tooluniverse-structural-variant-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。

tooluniverse-structural-variant-analysis 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 21,898。

总安装 4,725周安装 193GitHub Stars 1,291下载 1,513核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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-structural-variant-analysis(工具宇宙结构变异分析)
来源仓库:https://github.com/mims-harvard/tooluniverse
仓库路径:skills/tooluniverse-structural-variant-analysis
安装命令:
npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-structural-variant-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-structural-variant-analysis

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,适合快速定位候选结果。可根据关键词、任务场景或来源线索在多种宿主中调用。通过 GitHub 安装,支持 Codex、Claude、Cursor、Gemini CLI 等宿主环境。安装前建议确认权限范围、维护状态及是否触发联网或文件读写操作。

可结合来源仓库和原始 README 进一步核验具体用法。

来源仓库内路径:skills/tooluniverse-structural-variant-analysis

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

tooluniverse-structural-variant-analysis 怎么安装?

在终端执行 npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-structura…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

tooluniverse-structural-variant-analysis 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

tooluniverse-structural-variant-analysis 需要什么权限?

权限级别是只读。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。

tooluniverse-structural-variant-analysis 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

tooluniverse-structural-variant-analysis 用的人多吗?

公开数据是累计安装 4,725 次,近一周安装 193 次,下载 1,513 次,GitHub 1,291 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

tooluniverse-structural-variant-analysis 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 来源仓库有 1,291 星,维护活跃度相对可查。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
tooluniverse-structural-variant-analysis

工具宇宙结构变异分析 · 本页

4,7251,291只读未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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.

Structural Variant Analysis Workflow

Systematic analysis of structural variants (deletions, duplications, inversions, translocations, complex rearrangements) for clinical genomics interpretation using ACMG-adapted criteria.

LOOK UP DON'T GUESS - Always retrieve ClinGen HI/TS scores, gnomAD frequencies, and ClinVar evidence from tools. Do not infer dosage sensitivity from gene function alone.

KEY PRINCIPLES:

  1. Report-first approach - Create SV_analysis_report.md FIRST, then populate progressively
  2. ACMG-style classification - Pathogenic/Likely Pathogenic/VUS/Likely Benign/Benign with explicit evidence
  3. Evidence grading - Grade all findings by confidence level (High/Moderate/Limited)
  4. Dosage sensitivity critical - Gene dosage effects drive SV pathogenicity
  5. Breakpoint precision matters - Exact gene disruption vs dosage-only effects
  6. Population context essential - gnomAD SVs for frequency assessment
  7. English-first queries - Always use English terms in tool calls. Respond in the user's language

Triggers

Use this skill when users:

  • Ask about structural variant interpretation
  • Have CNV data from array or sequencing
  • Ask "is this deletion/duplication pathogenic?"
  • Need ACMG classification for SVs
  • Want to assess gene dosage effects
  • Ask about chromosomal rearrangements
  • Have large-scale genomic alterations requiring interpretation

SV Pathogenicity Reasoning (Start Here)

Before any tool call, apply this reasoning to frame the analysis:

SV pathogenicity depends on what the SV disrupts. A deletion removing an entire gene is likely pathogenic if the gene is haploinsufficient. A duplication is pathogenic if the gene is dosage-sensitive. An inversion is pathogenic only if it disrupts a coding region or regulatory element at the breakpoint.

Work through these questions in order:

1. What type is the SV, and what disruption mechanism does it cause?

  • Deletion: loss of one copy. Pathogenic if any contained gene is haploinsufficient (ClinGen HI score 3, pLI >= 0.9). A deletion of a dosage-insensitive gene in a gene-dense region may be benign even if large.
  • Duplication: gain of one copy. Pathogenic if any contained gene is dosage-sensitive (ClinGen TS score 3). Duplications can also disrupt gene regulation if tandem (disrupts reading frame at junction) or if they separate a gene from its enhancer.
  • Inversion: no copy number change. Pathogenic only at the breakpoints: if one breakpoint falls within an exon (truncation) or separates a gene from its regulatory element. Inversions entirely within gene-poor, regulatory-poor regions are often benign.
  • Translocation: pathogenic if a breakpoint disrupts a coding region or creates a pathogenic fusion gene. Balanced translocations in parents of affected children warrant special scrutiny.
  • Complex rearrangements: assess each segment and each breakpoint independently.

2. Is the disrupted gene dosage-sensitive?

  • ClinGen HI score 3 = definitive haploinsufficiency (deletion of this gene is pathogenic)
  • ClinGen HI score 2 = likely haploinsufficient
  • pLI >= 0.9 = strong LoF intolerance (supporting haploinsufficiency)
  • ClinGen TS score 3 = definitive triplosensitivity (duplication is pathogenic)
  • If no ClinGen data: use OMIM inheritance (autosomal dominant = often dosage-sensitive) as weaker evidence

3. Does the population frequency contextualize the SV?

  • =1% frequency in gnomAD SV = BA1 (likely benign unless phenotype is extreme)
  • <0.01% = supports pathogenicity (PM2)
  • Present in unaffected parents = weak evidence against pathogenicity, but not conclusive

4. Is there clinical precedent?

  • Identical SV in ClinVar as Pathogenic/Likely Pathogenic = strong evidence (PS1)
  • De novo occurrence = strong evidence for pathogenicity (PS2)
  • Phenotype match to known gene-disease association = supporting evidence (PP4)

Document this reasoning before computing the final score.


Workflow Overview

Phase 1: SV IDENTITY & CLASSIFICATION
  Normalize coordinates (hg19/hg38), determine type (DEL/DUP/INV/TRA/CPX),
  calculate size, assess breakpoint precision

Phase 2: GENE CONTENT ANALYSIS
  Identify fully contained genes, partially disrupted genes (breakpoint within),
  flanking genes (within 1 Mb), annotate function and disease associations

Phase 3: DOSAGE SENSITIVITY ASSESSMENT
  ClinGen HI/TS scores, pLI scores, OMIM inheritance patterns,
  gene-disease validity levels

Phase 4: POPULATION FREQUENCY CONTEXT
  gnomAD SV database, ClinVar known SVs, DECIPHER patient cases,
  reciprocal overlap calculation (>=70% = same SV)

Phase 5: PATHOGENICITY SCORING
  Quantitative 0-10 scale: gene content (40%), dosage sensitivity (30%),
  population frequency (20%), clinical evidence (10%)

Phase 6: LITERATURE & CLINICAL EVIDENCE
  PubMed searches, DECIPHER cohort analysis, functional evidence

Phase 7: ACMG-ADAPTED CLASSIFICATION
  Apply SV-specific evidence codes, calculate final classification,
  generate clinical recommendations

Phase 1: SV Identity & Classification

Goal: Standardize SV notation and classify type.

Capture: chromosome(s), coordinates (start/end in hg19/hg38), SV size, SV type (DEL/DUP/INV/TRA/CPX), breakpoint precision, inheritance pattern (de novo/inherited/unknown).

For SV type definitions, scoring tables, and ACMG code details, see CLASSIFICATION_GUIDE.md.


Phase 2: Gene Content Analysis

Goal: Annotate all genes affected by the SV.

Tools:

  • ensembl_lookup_gene - gene structure, coordinates, exons
  • NCBIGene_search - official symbol, aliases, description
  • Gene_Ontology_get_term_info - biological process, molecular function
  • OMIM_search, OMIM_get_entry - disease associations, inheritance
  • DisGeNET_search_gene - gene-disease association scores

Classify genes as: fully contained (entire gene in SV), partially disrupted (breakpoint within gene), or flanking (within 1 Mb of breakpoints).

For implementation pseudocode, see ANALYSIS_PROCEDURES.md Phase 2.


Phase 3: Dosage Sensitivity Assessment

Goal: Determine if affected genes are dosage-sensitive.

Tools:

  • ClinGen_search_dosage_sensitivity - HI/TS scores (0-3, gold standard)
  • ClinGen_search_gene_validity - gene-disease validity level
  • gnomad_search_variants - pLI scores for LoF intolerance
  • OMIM_get_entry - inheritance pattern (AD suggests dosage sensitivity)

Interpret scores using the reasoning above. ClinGen HI/TS score 3 = definitive; score 2 = likely; score 1 = little evidence; score 0 = no evidence. Do not equate AD inheritance with haploinsufficiency without ClinGen support.


Phase 4: Population Frequency Context

Goal: Determine if SV is common (likely benign) or rare (supports pathogenicity).

Tools:

  • gnomad_search_variants - population SV frequencies
  • ClinVar_search_variants - known pathogenic/benign SVs
  • ClinGen_search_dosage_sensitivity - patient SVs with phenotypes

Use >=70% reciprocal overlap to define "same" SV for comparison. A frequency >=1% triggers BA1 unless there is very strong clinical evidence to override.


Phase 5: Pathogenicity Scoring

Goal: Quantitative pathogenicity assessment on 0-10 scale.

Four components weighted: gene content (40%), dosage sensitivity (30%), population frequency (20%), clinical evidence (10%).

Score mapping: 9-10 = Pathogenic, 7-8 = Likely Pathogenic, 4-6 = VUS, 2-3 = Likely Benign, 0-1 = Benign.

For detailed scoring breakdowns and implementation, see CLASSIFICATION_GUIDE.md and ANALYSIS_PROCEDURES.md Phase 5.


Phase 6: Literature & Clinical Evidence

Goal: Find case reports, functional studies, and clinical validation.

Tools:

  • PubMed_search_articles - peer-reviewed literature
  • EuropePMC_search_articles - additional coverage
  • ClinGen_search_dosage_sensitivity - patient case database

Search strategies: gene-specific dosage sensitivity papers, SV-specific case reports, phenotype-gene associations. See ANALYSIS_PROCEDURES.md Phase 6.


Phase 7: ACMG-Adapted Classification

Goal: Apply ACMG/ClinGen criteria adapted for SVs and generate a final classification with explicit evidence summary.

The LLM knows the ACMG criteria codes and combination rules. Apply them to the evidence gathered in Phases 1-6. Key points to verify with tool data:

  • PVS1 applies to deletions of genes with ClinGen HI score >= 2 or pLI >= 0.9
  • PS2 requires confirmed de novo status (check parental genotypes if available)
  • PM2 requires absence from population databases at >=70% reciprocal overlap

For complete evidence code tables and classification algorithm, see CLASSIFICATION_GUIDE.md.


Output

Create report using the template in REPORT_TEMPLATE.md. Name files as:

SV_analysis_[TYPE]_chr[CHR]_[START]_[END]_[GENES].md

Required Tools Reference

  • ClinGen_search_dosage_sensitivity - HI/TS scores (required for all deletions/duplications)
  • ClinGen_search_gene_validity - gene-disease validity (required)
  • ClinVar_search_variants - known pathogenic/benign SVs (required)
  • ensembl_lookup_gene - gene coordinates, structure (required)
  • OMIM_search, OMIM_get_entry - gene-disease associations (required)
  • gnomad_search_variants - population frequency and pLI (required)
  • DisGeNET_search_gene - additional disease associations (recommended)
  • PubMed_search_articles - literature evidence (recommended)
  • Gene_Ontology_get_term_info - gene function (supporting)

When NOT to Use This Skill

  • Single nucleotide variants (SNVs) - Use tooluniverse-variant-interpretation
  • Small indels (<50 bp) - Use variant interpretation skill
  • Somatic variants in cancer - Different framework needed
  • Mitochondrial variants - Specialized interpretation required
  • Repeat expansions - Different mechanism

Use this skill for structural variants >=50 bp requiring dosage sensitivity assessment and ACMG-adapted classification.


Reference Files

  • EXAMPLES.md - Sample SV interpretations with worked examples
  • CLASSIFICATION_GUIDE.md - ACMG criteria, scoring system, evidence codes, special scenarios, clinical recommendations
  • REPORT_TEMPLATE.md - Full report template with section structure and file naming
  • ANALYSIS_PROCEDURES.md - Detailed implementation pseudocode for each phase

External References

平台分布

Codex

39.27%
按下载量换算594

Claude

28.27%
按下载量换算428

Cursor

20.37%
按下载量换算308

Gemini CLI

9.28%
按下载量换算140

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

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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