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Agent Skill · 研究检索

tooluniverse-literature-deep-research(工具宇宙文献深度研究)

tooluniverse-literature-deep-research 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-literature-deep-research 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,权限为调用外部服务;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 15,841。

总安装 7,566周安装 325GitHub Stars 1,333下载 2,652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-literature-deep-research(工具宇宙文献深度研究)
来源仓库:https://github.com/mims-harvard/tooluniverse
仓库路径:skills/tooluniverse-literature-deep-research
安装命令:
npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-literature-deep-research
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-literature-deep-research

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。

用于深度检索学术文献,支持在生物医学领域开展系统性文献调研和综述撰写。适合在 Codex、Claude、Cursor、Gemini CLI 中执行基于关键词或主题的文献筛选与引用任务。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。

来源仓库内路径:skills/tooluniverse-literature-deep-research

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

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

tooluniverse-literature-deep-research 怎么安装?

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

tooluniverse-literature-deep-research 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-literature-deep-research 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

tooluniverse-literature-deep-research 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-literature-deep-research 用的人多吗?

公开数据是累计安装 7,566 次,近一周安装 325 次,下载 2,652 次,GitHub 1,333 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-literature-deep-research 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 1,333 星,维护活跃度相对可查。

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。

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-literature-deep-research

工具宇宙文献深度研究 · 本页

7,5661,333调用外部服务未明确有提醒

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

Literature Deep Research

Systematic literature research: disambiguate, search with collision-aware queries, grade evidence, produce structured reports.

KEY PRINCIPLES: (1) Disambiguate first (2) Right-size deliverable (3) Grade every claim T1-T4 (4) All sections mandatory even if "limited evidence" (5) Source attribution for every claim (6) English-first queries, respond in user's language (7) Report = deliverable, not search log


LOOK UP, DON'T GUESS

Search PubMed/EuropePMC FIRST before reasoning. A published paper beats memory.

Factoid search strategy:

  1. Extract KEY TERMS (most specific nouns/verbs)
  2. EuropePMC_search_articles(query="term1 term2 term3", limit=5)
  3. No results -> BROADEN (remove most restrictive term)
  4. Too many -> NARROW (add specific terms)
  5. Answer usually in abstract of top results
  6. Failed query -> try DIFFERENT TERMS/synonyms, don't repeat

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.

Workflow

Phase 0: Clarify + Mode Select → Phase 1: Disambiguate + Profile → Phase 2: Literature Search → Phase 3: Report

Phase 0: Mode Selection

ModeWhenDeliverable
FactoidSingle concrete question1-page fact-check report + bibliography
Mini-reviewNarrow topic1-3 page narrative
Full Deep-ResearchComprehensive overview15-section report + bibliography

Factoid Mode (Fast Path)

# [TOPIC]: Fact-check Report
## Question / ## Answer (with evidence rating) / ## Source(s) / ## Verification Notes / ## Limitations

Domain Detection

PatternDomainAction
Gene/protein symbolBiological targetFull bio disambiguation
Drug nameDrugDrug disambiguation (1.5)
Disease nameDiseaseDisease disambiguation (1.6)
CS/ML topicGeneral academicSkip bio tools, literature-only
Cross-domainInterdisciplinaryResolve each entity in its domain

Cross-Skill Delegation

  • Gene/protein deep-dive: tooluniverse-target-research
  • Drug profile: tooluniverse-drug-research
  • Disease profile: tooluniverse-disease-research

Use this skill for literature synthesis. Use specialized skills for entity profiling. For max depth, run both.


Phase 1: Subject Disambiguation + Profile

1.1 Biological Target Resolution

UniProt_search → UniProt_get_entry_by_accession → UniProt_id_mapping
ensembl_lookup_gene → MyGene_get_gene_annotation

1.2 Naming Collision Detection

Check first 20 results. If >20% off-topic, build negative filter: NOT [collision1] NOT [collision2]. Gene family: "ADAR" NOT "ADAR2" NOT "ADARB1". Cross-domain: add context terms.

1.3 Baseline Profile (Bio Targets)

InterPro_get_protein_domains, UniProt_get_ptm_processing_by_accession, HPA_get_subcellular_location,
GTEx_get_median_gene_expression, GO_get_annotations_for_gene, Reactome_map_uniprot_to_pathways,
STRING_get_protein_interactions, intact_get_interactions, OpenTargets_get_target_tractability_by_ensemblID

GPCR targets: delegate to tooluniverse-target-research.

1.5 Drug Disambiguation

Identity: OpenTargets_get_drug_chembId_by_generic_name, ChEMBL_get_drug, PubChem_get_CID_by_compound_name, drugbank_get_drug_basic_info_by_drug_name_or_id Targets: ChEMBL_get_drug_mechanisms, OpenTargets_get_associated_targets_by_drug_chemblId, DGIdb_get_drug_gene_interactions Safety: OpenTargets_get_drug_adverse_events_by_chemblId, OpenTargets_get_drug_indications_by_chemblId, search_clinical_trials

1.6 Disease Disambiguation

OpenTargets disease search → EFO/MONDO IDs
DisGeNET_get_disease_genes, DisGeNET_search_disease
CTD_get_disease_chemicals

1.7 Compound Queries (e.g., "metformin in breast cancer")

Resolve both entities, then cross-reference via CTD_get_chemical_gene_interactions, CTD_get_chemical_diseases, OpenTargets drug-target/drug-disease tools. Intersect shared targets/pathways.

1.8 General Academic / 1.9 Interdisciplinary

Non-bio: skip bio tools, use ArXiv/DBLP/OSF. Cross-domain: resolve bio entities with 1.1-1.3, search CS/general in parallel, merge and cross-reference.


Phase 2: Literature Search

Methodology stays internal. Report shows findings, not process.

2.1 Query Strategy

Step 1: Seeds (15-30 core papers): domain-specific title searches with date/sort filters. Step 2: Citation expansion: PubMed_get_cited_by, EuropePMC_get_citations/references, PubMed_get_related, SemanticScholar_get_recommendations, OpenCitations_get_citations Step 3: Collision-filtered broader queries: "[TERM]" AND ([context]) NOT [collision]

2.2 Literature Tools

Biomedical: PubMed_search_articles, PMC_search_papers, EuropePMC_search_articles, PubTator3_LiteratureSearch Biology (ecology/evolution/plant): EuropePMC as PRIMARY (PubMed returns 0-1 for non-clinical biology). Also openalex_literature_search. CS/ML: ArXiv_search_papers, DBLP_search_publications, SemanticScholar_search_papers General: openalex_literature_search, Crossref_search_works, CORE_search_papers, DOAJ_search_articles Preprints: BioRxiv_get_preprint, MedRxiv_get_preprint, OSF_search_preprints, EuropePMC_search_articles(source='PPR') Multi-source: advanced_literature_search_agent (12+ DBs; needs Azure key -- fallback: query PubMed+ArXiv+SemanticScholar+OpenAlex individually) Citation impact: iCite_search_publications (RCR/APT), iCite_get_publications (by PMID), scite_get_tallies (support/contradict). PubMed-only; for CS use SemanticScholar.

2.3-2.4 Full-Text & PubMed Zero-Result Fallback

Full-text: see FULLTEXT_STRATEGY.md for three-tier strategy.

CRITICAL: PubMed returns 0 for ~30% of valid queries. Always retry with EuropePMC when PubMed returns empty. This is not optional.

2.5 Tool Failure / OA Handling

Retry once -> fallback tool. Key fallbacks: PubMed_get_cited_by -> EuropePMC_get_citations -> OpenCitations. OA: Unpaywall if configured, else Europe PMC/PMC/OpenAlex flags.


Phase 3: Evidence Grading

TierLabelBio ExampleCS/ML Example
T1MechanisticCRISPR KO + rescue, RCTFormal proof, controlled ablation
T2FunctionalsiRNA knockdown phenotypeBenchmark with baselines
T3AssociationGWAS, screen hitObservational, case study
T4MentionReview articleSurvey, workshop abstract

Inline: Target X regulates Y [T1: PMID:12345678]. Per theme: summarize evidence distribution.


Report Output

FileMode
[topic]_report.mdFull
[topic]_factcheck_report.mdFactoid
[topic]_bibliography.json + .csvAll

Progressive update: create report with all section headers immediately. Fill after each phase. Write Executive Summary LAST.

Use 15-section template from REPORT_TEMPLATE.md. Domain adaptations: bio (architecture/expression/GO/disease), drug (properties/MOA/PK/safety), disease (epi/patho/genes/treatments), general (history/theories/evidence/applications).


Communication

Brief progress updates only: "Resolving identifiers...", "Building paper set...", "Grading evidence..." Do NOT expose: raw tool outputs, dedup counts, search round details.


References

  • TOOL_NAMES_REFERENCE.md -- 123 tools with parameters
  • REPORT_TEMPLATE.md -- template, domain adaptations, bibliography, completeness checklist
  • FULLTEXT_STRATEGY.md -- three-tier full-text verification
  • WORKFLOW.md -- compact cheat-sheet
  • EXAMPLES.md -- worked examples

平台分布

Codex

37.5%
按下载量换算995

Claude

29.19%
按下载量换算774

Cursor

19.54%
按下载量换算518

Gemini CLI

9.49%
按下载量换算252

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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