Token导航 LogoToken导航TokenDH.com
研究检索external-servicegithub未标认证来源可访问许可证需确认审计提醒

audit-oe审核原件

Agent Skill

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

总安装

198

周安装

8

GitHub Stars

10

下载量

62
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:audit-oe(审核原件)
来源仓库:https://github.com/htlin222/audit-oe-skill
仓库路径:skills/audit-oe
安装命令:
npx skills add https://github.com/htlin222/audit-oe-skill --skill audit-oe
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/htlin222/audit-oe-skill --skill audit-oe

简介

独立验证 OpenEvidence 响应中的每一条引用,使用 PubMed、bioRxiv 和 ClinicalTrials 并行查询确证来源。

  • 检测传递性引用错误和交叉引用矛盾,生成结构化准确性报告供人工复核。
  • 需连接 OpenEvidence MCP 服务器及其他生物医学数据源才能正常工作,依赖网络可达性。
  • 安装方式:通过 npx skills add 命令从指定 GitHub 仓库添加,支持 Codex、Claude、Cursor 等宿主环境。
  • 仅当用户明确要求“audit”且相关 MCP 服务已认证时才触发,避免误用消耗资源。

SKILL.md

OpenEvidence Citation Audit v2

Independently verify every citation in an OpenEvidence response using parallel PubMed/bioRxiv/ClinicalTrials lookups, detect transitive citation errors, find cross-citation contradictions, and produce a structured accuracy report.

Prerequisites

  • OpenEvidence MCP server connected and authenticated
  • PubMed MCP server available (citation verification + full text)
  • bioRxiv MCP server available (preprint verification)
  • Clinical Trials MCP server available (endpoint verification)
  • WebFetch available (DOI resolution fallback)

Trigger

Use this skill when:

  • User asks to "audit", "verify", or "check" an OpenEvidence response
  • User asks to query OpenEvidence and validate its citations
  • User wants to critically appraise OE's evidence base

Input

Either:

  1. A topic/question -- skill queries OE then audits the response
  2. An existing OE article ID -- skill fetches and audits it directly

Output Structure

{topic-slug}/
  original.md          # Raw OE extracted answer
  assets/
    citations.bib      # BibTeX block from OE
  report.md            # Structured audit report with provenance + contradictions

Known OE Failure Modes (why this skill exists)

OE uses a full-text RAG system with these indexed corpora (decoded from ROT-1 obfuscated origin field):

Origin KeyDecodedRisk
qvcnfe_bctusbdut_*pubmed_abstracts_hindex_35pct_ada3smallLow -- abstract-level claims
kbdd_gvmmufyu_tdsbqfejacc_fulltext_scrapedMedium -- full-text chunks
mbodfu_gvmmufyu_tdsbqfelancet_fulltext_scrapedHIGH -- reviews quoting others
ofkn_sfwjfx_bsujdmf_*nejm_review_article_fulltext_sftpHIGH -- reviews quoting others
hvjefmjoft_gvmmufyu_*guidelines_fulltext_usa_manualMedium -- guideline recommendations
nfejb_boopubufe_hfnjojmedia_annotated_geminiMedium -- AI-annotated figures

Primary failure mode: Transitive Citation. When a review (e.g., Nauck 2026) writes "GLP-1 RA reduce stroke by 13%" citing Kristensen 2019, OE retrieves that chunk and attributes the stroke finding to Nauck. But Nauck is just quoting -- it's not their finding. If another meta-analysis (Galli 2025) finds NO stroke benefit, OE doesn't detect the contradiction.


Workflow

Phase 1: Query OpenEvidence

1. Call oe_auth_status() -- abort if invalid
2. Call oe_ask with:
   - question: <user's topic>
   - include_bibtex: true
   - crossref_validate: true
   - wait_for_completion: true
   - timeout_sec: 120
3. Save extracted_answer_raw -> {topic-slug}/original.md
4. Save BibTeX block -> {topic-slug}/assets/citations.bib
5. Record: article_id, citationCount, crossrefValidatedCount

Phase 2: Parse, Map, and Decode Provenance

Orchestrator extracts from the structured_article (not just BibTeX):

# Access structured spans with citation metadata
sections = article.output.structured_article.articlesection_set
for section in sections:
    for para in section.articleparagraph_set:
        for span in para.articlespan_set:
            text = span.text
            for citation in span.citations:
                # Decode provenance
                raw_origin = citation.metadata.origin
                origin = ''.join(chr(ord(c) - 1) for c in raw_origin)
                impact = citation.metadata.why_cited.impact_score

For each citation, build an enhanced descriptor:

- index: N
  authors: "LastName et al."
  title: "..."
  journal: "..."
  year: YYYY
  doi: "10.xxxx/..."
  pmid: "NNNNNNN"
  claim_text: "The exact sentence from OE span"
  strategy: pubmed_pmid | pubmed_doi | pubmed_title | biorxiv | web_doi
  # NEW v2 fields:
  oe_origin: "lancet_fulltext_scraped"
  oe_impact_score: 21.54
  risk_level: HIGH | MEDIUM | LOW
  has_quantitative_claim: true  # contains HR, CI, %, p-value
  needs_transitive_check: true  # review/guideline + quantitative = yes

Risk level assignment:

  • HIGH: origin contains "fulltext_scraped" AND (study_type is review OR guideline)
  • MEDIUM: origin contains "fulltext" OR "media_annotated"
  • LOW: origin is "pubmed_abstracts"

Phase 3: Parallel Citation Verification (Enhanced)

Launch one Agent per citation using model: "haiku".

Each agent now has ENHANCED instructions:

Step A: Verify Paper Exists (unchanged)

  • get_article_metadata with PMID → search_articlesbioRxiv get_preprint → WebFetch DOI

Step B: Fetch Content (AGGRESSIVE)

1. get_article_metadata → check for PMCID
2. If PMCID exists: ALWAYS call get_full_text_article (don't skip)
3. If no PMCID and abstract is empty/generic:
   → WebFetch("https://doi.org/{DOI}", prompt="Extract the structured abstract, key findings, and conclusions")
4. If abstract mentions NCT number:
   → Call Clinical Trials MCP: get_trial_details(nct_id)
   → Extract primary/secondary endpoints, sample size, status

Step C: Score Claim Accuracy (Multi-dimensional)

DimensionScoreWeightHow to assess
Paper exists0 or 1requiredPubMed/DOI lookup
Metadata match0-110%Author, year, journal correct?
Claim direction0-125%Does paper support the direction of the claim?
Numbers verified0-135%Specific HRs, CIs, % match?
Correct attribution0-120%Is this the paper's OWN finding (not quoting another)?
No contradiction0-110%Does any other evidence contradict? (filled in Phase 4)

Step D: Classify and Flag

- study_type: RCT | meta-analysis | cohort | review | guideline | editorial | preprint
- is_primary_source: true/false  # Did this paper GENERATE the data, or just CITE it?
- transitive_risk: true/false    # Review + quantitative claim about a specific trial
- trial_name_mentioned: "LEADER" | "SUSTAIN-6" | null  # For trace-back

Agent Output Format (v2)

CITATION_REPORT:
- citation_index: [i]
- exists: true/false
- existence_details: "..."
- correct_doi: "..."
- correct_pmid: "..."
- claim_text: "..."
- dimensions:
    metadata_match: [0-1]
    claim_direction: [0-1]
    numbers_verified: [0-1]
    correct_attribution: [0-1]
- composite_score: [0-1]
- study_type: "..."
- is_primary_source: true/false
- transitive_risk: true/false
- trial_name_mentioned: "..." or null
- sample_size: "..."
- journal: "..."
- peer_reviewed: true/false
- full_text_available: true/false
- data_source: abstract_only / full_text / doi_resolution / clinical_trials_registry
- key_findings_from_source: "..." # What the paper ACTUALLY found (for contradiction check)
- warnings: ["..."]
END_CITATION_REPORT

Phase 3b: Transitive Trace-Back (NEW)

Triggered for: citations where transitive_risk: true AND trial_name_mentioned is not null.

Launch additional haiku agents to find and verify the ORIGINAL source:

Prompt: "The review [Nauck 2026] claims 'stroke reduction 13-17%'
         referencing what appears to be [LEADER / SUSTAIN-6 / Kristensen 2019].
         Search PubMed for the ORIGINAL trial/meta-analysis.
         Verify if the number matches the original source."

Output:

TRACEBACK_REPORT:
- original_citation_index: [i]
- traced_to_pmid: "..."
- traced_to_title: "..."
- traced_to_study_type: "RCT" or "meta-analysis"
- number_matches_original: true/false
- original_finding: "..."
- attribution_correct: true/false  # Should OE have cited the original instead?
END_TRACEBACK_REPORT

Phase 4: Cross-Citation Contradiction Scan (NEW)

Launch 1 sonnet-model agent that receives ALL Phase 3 + 3b reports and:

Instructions:
1. Read all CITATION_REPORT entries
2. Extract key_findings_from_source for each
3. For EACH quantitative claim in the OE response:
   - Check if multiple citations report DIFFERENT findings on the same outcome
   - Flag contradictions with severity:
     - CRITICAL: One source confirms, another explicitly denies
     - WARNING: Sources report different magnitudes (>20% difference)
     - NOTE: Sources use different populations/timeframes (may explain difference)
4. Produce a CONTRADICTION_REPORT

Output:

CONTRADICTION_REPORT:
- contradictions_found: N
- items:
  - outcome: "stroke reduction"
    claim_in_oe: "13-17% reduction"
    source_a: {pmid: X, finding: "no difference", origin: "jacc_fulltext"}
    source_b: {pmid: Y, finding: "13-17%", origin: "lancet_fulltext (quoting others)"}
    severity: CRITICAL
    explanation: "Source A is a comprehensive meta-analysis of 99,599 patients finding no stroke benefit. Source B is a review article quoting older, smaller meta-analyses."
END_CONTRADICTION_REPORT

Phase 5: Collation and Multi-Dimensional Report

Orchestrator computes:

MetricFormula
Citation Existence Rateexists_count / total x 100%
Mean Composite Scoreaverage(all composite_score)
Transitive Citation Ratetransitive_risk_count / total x 100%
Contradiction Countfrom Phase 4
Full-Text Verification Ratefull_text_count / total x 100%

Grading (v2)

GradeCriteria
PASS>= 90% exist AND mean composite >= 0.8 AND 0 CRITICAL contradictions
CAUTION>= 75% exist AND mean composite >= 0.6 AND <= 1 CRITICAL contradiction
FAIL< 75% exist OR mean composite < 0.6 OR > 1 CRITICAL contradictions OR fabricated citations

Report Template (v2)

# OpenEvidence Citation Audit Report

**Query:** "{question}"
**Date:** {YYYY-MM-DD}
**OE Article ID:** `{uuid}`
**OE Crossref Self-Validation:** {n}/{total}

## Executive Summary
| Metric | Result |
|--------|--------|
| Citations verified | X/N (Y%) |
| Mean composite score | Z.ZZ/1.0 |
| Transitive citations detected | M |
| Cross-citation contradictions | K (C critical) |
| Full-text verification rate | P% |

## Overall Assessment: {PASS|CAUTION|FAIL}

## Provenance Analysis
| # | Paper | OE Data Source | Risk Level | Primary Source? |
|---|-------|---------------|------------|-----------------|

## Cross-Citation Contradictions
| Outcome | OE Claims | Source A Says | Source B Says | Severity |
|---------|-----------|-------------|-------------|----------|

## Transitive Citation Trace
| # | OE Cites (Review) | Claim Actually From | Verified Against Original? |
|---|-------------------|--------------------|-----------------------------|

## Citation-by-Citation Results
| # | Paper | Exists | Composite | Direction | Numbers | Attribution | Flags |
|---|-------|--------|-----------|-----------|---------|-------------|-------|

## Detailed Findings
### Citation [i]: Author (Year) -- Score: X.XX
- Claim: "..."
- Verified: ...
- Provenance: {origin} (risk: {level})
- Attribution: Primary / Transitive (traced to: ...)
- Verdict: ...

## Evidence Strength Summary
## Methodology Notes

Error Handling

FailureAction
OE auth invalidAbort with clear message
DOI doesn't resolveTry PubMed title search, then WebSearch. Mark exists=false if all fail
Paper not in PubMedTry bioRxiv, then WebFetch on DOI
Paywalled (no PMCID)WebFetch on DOI for publisher abstract; note limitation
BibTeX metadata wrongNote discrepancy, still verify actual paper
Paper retractedFlag CRITICALLY in warnings
PreprintFlag peer_reviewed=false, check for published version
Non-academic sourceSkip verification, note in report
Agent timeoutRetry once; if still fails, report partial results
OE structured data missingFall back to BibTeX-only parsing (v1 behavior)
Origin field not decodableMark provenance as "unknown", apply MEDIUM risk

Configuration

ParameterDefaultDescription
model_verifyhaikuModel for Phase 3 verification agents
model_contradictsonnetModel for Phase 4 contradiction agent
timeout_sec120OE query timeout
max_parallel15Max concurrent verification agents
full_textaggressiveAlways attempt PMC + DOI fallback
trace_transitivetrueRun Phase 3b for reviews with quantitative claims
check_contradictionstrueRun Phase 4 cross-citation scan
clinical_trialstrueVerify RCT endpoints against registry
output_dir./{topic-slug}/Where to write results

适合场景

01

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

按任务关键词查找相关 Skills

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

38.7%
按下载量换算24

Claude

31.79%
按下载量换算20

Cursor

18.5%
按下载量换算11

Gemini CLI

8.95%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

external-service

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills