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tooluniverse-stem-cell-organoid工具宇宙干细胞类器官

Agent Skill

tooluniverse-stem-cell-organoid 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

978

周安装

42

GitHub Stars

1,282

下载量

343
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-stem-cell-organoid(工具宇宙干细胞类器官)
来源仓库:https://github.com/mims-harvard/tooluniverse
仓库路径:skills/tooluniverse-stem-cell-organoid
安装命令:
npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-stem-cell-organoid
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-stem-cell-organoid

简介

用于查找、检索和筛选干细胞类器官相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据任务场景定位候选结果。
  • 通过 npx 命令从 GitHub 仓库安装并使用。
  • 安装前需确认权限范围和维护状态,注意可能触发联网或文件操作。
  • tooluniverse-stem-cell-organoid 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Stem Cell & Organoid Research

Pipeline for investigating stem cell biology, iPSC characterization, organoid models, and cell differentiation using ToolUniverse tools.

Reasoning Strategy

Stem cell differentiation follows developmental biology — to make any target cell type from iPSCs, the protocol must mimic the embryonic signaling pathway that generates that cell type in vivo. For neural induction: inhibit BMP and TGF-beta (dual SMAD inhibition). For cardiomyocytes: activate WNT then inhibit WNT. For pancreatic beta cells: activate Activin/Nodal → FGF → Notch inhibition → BMP in sequence. The order and timing of growth factors matters critically — adding BMP4 during neural induction will redirect cells toward mesoderm. Mouse and human stem cells differ in their signaling requirements (LIF/STAT3 for mouse naive pluripotency; FGF/ERK for human primed pluripotency), so protocols are not interchangeable. Organoids recapitulate some but not all organ features — always assess maturation state (fetal vs. adult gene expression) before drawing disease-relevance conclusions.

LOOK UP DON'T GUESS: Do not assume which markers define a target cell type or which signaling pathway drives differentiation — query CellMarker_search_by_cell_type for markers and kegg_search_pathway for the relevant pathway. Do not assume organoid fidelity; look up published CellxGene or HCA atlas data for comparison.

Key principles:

  1. Marker-based identity — stem cell identity is defined by marker expression profiles (OCT4, SOX2, NANOG for pluripotency)
  2. Differentiation is a trajectory — not a binary state; analyze intermediate progenitor stages
  3. Organoid ≠ organ — organoids recapitulate some but not all organ features; always note limitations
  4. Species matters — mouse and human stem cells differ in signaling requirements
  5. Evidence grading — T1: validated in clinical iPSC study, T2: functional assay (teratoma, engraftment), T3: marker expression only, T4: computational prediction

Core Tools

ToolUse For
CellxGene_search_datasetsFind single-cell atlas data. Requires cellxgene-census package (pip install cellxgene-census). May not be installed by default.
CellMarker_search_by_cell_typeCell type marker genes. Requires operation="search_by_cell_type", cell_name= (NOT cell_type=)
CellMarker_search_by_geneWhich cell types express a gene. Requires operation="search_by_gene", gene_symbol=
HCA_search_projectsHuman Cell Atlas organoid/development projects
GEO_search_rnaseq_datasetsFind stem cell RNA-seq datasets
kegg_search_pathwayDifferentiation signaling pathways (WNT, Notch, Hedgehog)
ReactomeAnalysis_pathway_enrichmentPathway analysis of stem cell gene sets
STRING_get_networkPluripotency/differentiation gene networks
OpenTargets_get_associated_targets_by_disease_efoIdDisease genes for organoid disease modeling
PubMed_search_articlesStem cell and organoid literature
search_clinical_trialsiPSC-based clinical trials

Workflow

Phase 0: Define the Question
  Pluripotency? Differentiation? Disease modeling? Drug screening?
    |
Phase 1: Cell Identity & Markers
  CellMarker → pluripotency/lineage markers → verify identity
    |
Phase 2: Differentiation Pathways
  KEGG/Reactome → WNT, Notch, BMP, FGF signaling
    |
Phase 3: Atlas & Dataset Discovery
  CellxGene/HCA → reference datasets for target cell type
    |
Phase 4: Disease Modeling (if applicable)
  OpenTargets → disease genes → organoid recapitulation assessment
    |
Phase 5: Report
  Evidence-graded findings with clinical translation potential

Phase 1: Cell Identity & Markers

Pluripotency markers (must be co-expressed): OCT4 (POU5F1), SOX2, NANOG (essential); SSEA-4, TRA-1-60 (human surface markers). KLF4 and MYC are Yamanaka factors but also expressed in somatic cells — do not rely on them alone. Use CellMarker_search_by_cell_type to retrieve the full validated marker set for any target cell type.

Lineage markers: Ectoderm → PAX6/SOX1 (early), MAP2/TUBB3 (neurons); Mesoderm → TBXT/MIXL1 (early), CD34 (blood); Endoderm → SOX17/FOXA2 (early), PDX1/NKX6.1 (pancreas). Retrieve current marker lists from CellMarker rather than relying on memory.

Phase 2: Differentiation Pathways

Key signaling pathways for directed differentiation:

PathwayKEGG IDRole in Stem CellsCommon Modulators
WNT signalinghsa04310Pluripotency maintenance (canonical) vs differentiation (non-canonical)CHIR99021 (activator), IWP-2 (inhibitor)
Notch signalinghsa04330Lateral inhibition, fate decisionsDAPT (gamma-secretase inhibitor)
BMP/TGF-betahsa04350Mesoderm/trophectoderm inductionBMP4 (activator), Noggin (inhibitor)
FGF signalinghsa04010Self-renewal, neural inductionbFGF (activator), SU5402 (inhibitor)
Hedgehoghsa04340Patterning, organoid maturationSAG (activator), cyclopamine (inhibitor)
Hippo/YAPhsa04390Mechanotransduction, organoid sizeVerteporfin (YAP inhibitor)

Phase 3: Atlas & Dataset Discovery

# Find stem cell single-cell datasets
CellxGene_search_datasets(query="iPSC organoid", organism="Homo sapiens")
HCA_search_projects(query="organoid")
GEO_search_rnaseq_datasets(query="iPSC differentiation neural", organism="Homo sapiens")

Phase 4: Organoid Model Assessment

Organoid fidelity scoring — how well does the organoid recapitulate the organ?

FeatureHigh Fidelity (3)Moderate (2)Low (1)
Cell type diversityAll major cell types presentMost cell types, missing rare onesOnly 1-2 cell types
ArchitectureSelf-organized, correct spatial arrangementPartial organizationDisorganized aggregate
FunctionMeasurable organ function (secretion, contraction, electrophysiology)Some functional markersMarker expression only
MaturationAdult-like gene expression profileFetal-likeESC-like (failed differentiation)
Disease relevanceRecapitulates patient phenotypeSome disease featuresNo disease phenotype

Evidence Grading

GradeCriteriaExample
T1Clinical iPSC study or approved therapyiPSC-derived RPE for macular degeneration (Mandai 2017)
T2Functional validation (teratoma, engraftment, drug response)Organoid drug screening with patient-specific response
T3Marker expression + morphologyiPSC colony expressing OCT4/SOX2/NANOG
T4Computational prediction or single-marker evidencePredicted pluripotent by gene expression classifier

Synthesis Questions

  1. Is the cell identity verified? (co-expression of 3+ pluripotency markers, or lineage-appropriate markers)
  2. Is the differentiation protocol reproducible? (published, peer-reviewed, with quantified efficiency)
  3. Does the organoid model the disease? (patient-derived iPSC shows disease phenotype in organoid)
  4. What are the translational barriers? (scalability, maturation, immune compatibility, tumorigenicity)
  5. What's the best reference dataset? (CellxGene atlas for comparison)

Limitations

  • No organoid protocol database — protocols are scattered across publications; use PubMed search
  • Maturation gap — most organoids resemble fetal, not adult tissue; always note maturation state
  • Batch variability — iPSC-derived cells vary between passages and donor lines
  • No direct culture tools — this skill analyzes published data and designs experiments; it does not control bioreactors
  • Species differences — mouse ESCs require LIF; human ESCs require bFGF. Don't mix protocols

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能力 2

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能力 3

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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

36.14%
按下载量换算124

Claude

29.98%
按下载量换算103

Cursor

19.26%
按下载量换算66

Gemini CLI

9.21%
按下载量换算32

安全审计

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Snyk

可疑

权限和风险

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安装前确认

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