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Agent Skill · 研究检索

tooluniverse-adverse-outcome-pathway(工具宇宙不良后果途径)

tooluniverse-adverse-outcome-pathway 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-adverse-outcome-pathway 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,权限为需要联网;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 41,356。

总安装 1,176周安装 50GitHub Stars 1,314下载 412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-adverse-outcome-pathway(工具宇宙不良后果途径)
来源仓库:https://github.com/mims-harvard/tooluniverse
仓库路径:skills/tooluniverse-adverse-outcome-pathway
安装命令:
npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-adverse-outcome-pathway
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-adverse-outcome-pathway

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。

tooluniverse-adverse-outcome-pathway 用于查找、检索和筛选相关信息。适用于不良结局机制分析与通路研究的科研场景。通过 npx skills add 命令从指定 GitHub 仓库安装并使用。安装前需确认权限范围、维护状态及是否触发联网或文件读写操作。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/tooluniverse-adverse-outcome-pathway

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

    它需要需要联网,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-adverse-outcome-pathway 怎么安装?

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

tooluniverse-adverse-outcome-pathway 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-adverse-outcome-pathway 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

tooluniverse-adverse-outcome-pathway 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-adverse-outcome-pathway 用的人多吗?

公开数据是累计安装 1,176 次,近一周安装 50 次,下载 412 次,GitHub 1,314 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-adverse-outcome-pathway 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 1,314 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-adverse-outcome-pathway

工具宇宙不良后果途径 · 本页

1,1761,314需要联网未明确有提醒

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

Adverse Outcome Pathway & Regulatory Risk Assessment

Distinct from drug safety (see tooluniverse-toxicology): this skill targets environmental and industrial chemicals where the focus is AOP stressor mapping, GHS classification, LD50 hazard quantification, and IARC carcinogen status — not FAERS signals or FDA drug labels.

When to Use

Apply when researcher asks about:

  • "What AOPs are associated with [pesticide/solvent/industrial chemical]?"
  • "What is the GHS hazard classification for [compound]?"
  • "What is the LD50 for [compound]?"
  • "Is [compound] a carcinogen (IARC classification)?"
  • "Which genes does [chemical] interact with (CTD)?"
  • "Regulatory risk assessment for [environmental chemical]"
  • "What diseases are associated with [chemical] exposure?"

Do NOT use for FDA-approved drugs with FAERS data — use tooluniverse-toxicology instead.

Key Tools

ToolPurposeKey Params
AOPWiki_list_aopsDiscover AOPs by keywordkeyword (organ, effect, or target name)
AOPWiki_get_aopFull AOP details: MIE, key events, stressorsaop_id (int)
PubChemTox_get_toxicity_summaryNarrative toxicity overviewcid (PubChem CID)
PubChemTox_get_ghs_classificationGHS hazard category + pictogramscid
PubChemTox_get_carcinogen_classificationIARC/NTP/EPA carcinogen statuscid
PubChemTox_get_toxicity_valuesLD50/LC50 by route and speciescid
PubChemTox_get_acute_effectsSigns and symptoms of acute exposurecid
CTD_get_chemical_gene_interactionsChemical-gene molecular interactionsinput_terms (name or MeSH ID)
CTD_get_chemical_diseasesChemical-disease associationsinput_terms
PubChem_get_CID_by_compound_nameResolve compound name to PubChem CIDname

Workflow

Phase 1: Compound Identity Resolution

Resolve chemical name to PubChem CID before all PubChemTox calls.

PubChem_get_CID_by_compound_name(name="benzo[a]pyrene")
-> cid: 9153 (use for all PubChemTox calls)

Note: CTD tools accept the chemical name directly (input_terms param) — no CID needed.

Phase 2: AOP Discovery

Find relevant AOPs by searching organ targets and mechanism keywords.

AOPWiki_list_aops(keyword="lung")          # organ-level
AOPWiki_list_aops(keyword="DNA damage")    # mechanism-level
AOPWiki_list_aops(keyword="AhR")           # receptor-level

Select 2-4 candidate AOPs from results, then retrieve full details:

AOPWiki_get_aop(aop_id=58)  # returns MIE, key events, stressors, biological plausibility

Key fields in AOPWiki_get_aop response:

  • stressors: list of chemicals that trigger this AOP (check if query compound is listed)
  • molecular_initiating_event: the first molecular perturbation
  • key_events: ordered chain of biological events
  • adverse_outcome: apical regulatory endpoint

Phase 3: Hazard Quantification (PubChemTox)

Run all four hazard queries in parallel using the resolved CID:

PubChemTox_get_ghs_classification(cid=9153)        # GHS category + pictogram
PubChemTox_get_carcinogen_classification(cid=9153)  # IARC Group 1/2A/2B/3
PubChemTox_get_toxicity_values(cid=9153)            # LD50 by route/species
PubChemTox_get_acute_effects(cid=9153)              # signs/symptoms

Note: PubChemTox_get_target_organs sometimes returns no data — treat as optional.

Phase 4: Toxicogenomics (CTD)

Map chemical to gene targets and disease associations:

CTD_get_chemical_gene_interactions(input_terms="benzo[a]pyrene")
CTD_get_chemical_diseases(input_terms="benzo[a]pyrene")

Cross-reference CTD gene targets with AOP key event genes from Phase 2.

Tool Parameter Reference

ToolRequiredOptionalNotes
AOPWiki_list_aopskeyword—Use organ ("liver"), effect ("apoptosis"), or receptor ("PPARalpha")
AOPWiki_get_aopaop_id—Integer ID from list_aops output
PubChemTox_get_toxicity_summarycid—PubChem CID integer
PubChemTox_get_ghs_classificationcid—Returns pictogram_labels e.g. "Health Hazard"
PubChemTox_get_carcinogen_classificationcid—IARC Group in classifications[].classification
PubChemTox_get_toxicity_valuescid—Values like "LD50 Rat oral 2400 mg/kg"
PubChemTox_get_acute_effectscid—Sometimes sparse; not all compounds have data
CTD_get_chemical_gene_interactionsinput_terms—Accepts name or MeSH ID (e.g., "D001564")
CTD_get_chemical_diseasesinput_terms—Filter DirectEvidence = "marker/mechanism" for curated
PubChem_get_CID_by_compound_namename—Returns CID + SMILES; required before PubChemTox calls

Common Patterns

# Pattern: Confirm compound is a stressor in a specific AOP
aop = AOPWiki_get_aop(aop_id=58)
stressors = [s["name"] for s in aop["data"]["stressors"]]
# Check if query chemical appears in stressors list

# Pattern: Extract curated CTD disease associations only
diseases = CTD_get_chemical_diseases(input_terms="rotenone")
curated = [d for d in diseases["data"] if d.get("DirectEvidence")]

# Pattern: GHS carcinogen check
carcinogen = PubChemTox_get_carcinogen_classification(cid=9153)
iarc = [c for c in carcinogen["data"]["classifications"] if "IARC" in c.get("source", "")]

Reasoning Framework for Result Interpretation

Evidence Grading

GradeCriteriaExample
StrongAOP in OECD-endorsed status, compound listed as stressor, CTD + AOPWiki concordantAOP 58 (AhR → liver tumor) endorsed, benzo[a]pyrene confirmed stressor
ModerateAOP under review or well-documented, compound class match but not individually listedAOP links PPARalpha activation to liver effects; query compound is a fibrate analog
WeakAOP in development, compound not listed but shares MIE target via CTD gene overlapCTD shows gene target overlap with AOP key event genes, but no direct stressor listing
InsufficientNo AOP found, no CTD gene-disease link, hazard data sparseNovel compound with no toxicological database entries

Interpretation Guidance

  • AOP weight-of-evidence assessment: OECD-endorsed AOPs have undergone expert review and represent the highest confidence mechanistic pathways. AOPs "under development" in AOPWiki may have incomplete key event relationships. Evaluate each AOP by: (1) biological plausibility of key event relationships, (2) empirical support (dose-response concordance), (3) essentiality of key events (blocking KE prevents AO).
  • Key event relationship (KER) strength: Strong KERs have dose-response and temporal concordance between upstream and downstream key events. Moderate KERs have correlative evidence. Weak KERs are based on plausibility alone. The weakest KER in the chain determines the overall AOP confidence for that pathway.
  • Stressor potency interpretation: LD50 values indicate acute toxicity (lower = more toxic). GHS categories: Cat 1 (LD50 <= 5 mg/kg, fatal), Cat 2 (5-50, fatal), Cat 3 (50-300, toxic), Cat 4 (300-2000, harmful), Cat 5 (2000-5000, may be harmful). IARC Group 1 = confirmed carcinogen, 2A = probable, 2B = possible, 3 = not classifiable. Always report route of exposure and species for LD50 values.
  • CTD integration: CTD "direct evidence" (curated marker/mechanism) is stronger than "inferred" associations. When CTD gene targets overlap with AOP key event genes, this supports the mechanistic link between the compound and the adverse outcome.
  • Regulatory context: For risk assessment, combine hazard identification (IARC, GHS) with exposure assessment. A potent carcinogen at negligible exposure may pose lower risk than a moderate toxicant at high exposure.

Synthesis Questions

  1. Is the query compound explicitly listed as a stressor in the identified AOP, or is the link inferred from shared molecular targets (CTD gene overlap)?
  2. Do the key event relationships in the AOP chain have sufficient empirical support (dose-response concordance, temporal sequence), or are there weak links that reduce confidence?
  3. Are the hazard data (LD50, GHS, IARC) consistent across sources, and do they support the severity implied by the AOP adverse outcome?
  4. Does the CTD gene-disease evidence corroborate the AOP's predicted adverse outcome, or are there discrepancies suggesting alternative pathways?
  5. For regulatory decision-making, is the combined weight of evidence (AOP mechanism + hazard quantification + exposure context) sufficient to support a risk classification?

Fallback Chains

PrimaryFallbackWhen
AOPWiki_list_aops with specific keywordBroader organ termNo results
PubChemTox_get_target_organsPubChemTox_get_toxicity_summaryReturns empty
CTD_get_chemical_diseasesCTD_get_gene_diseases + gene from CTD interactionsCompound name not recognized

平台分布

Codex

36.86%
按下载量换算152

Claude

30.56%
按下载量换算126

Cursor

16.5%
按下载量换算68

Gemini CLI

8.34%
按下载量换算34

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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