academic-verify

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Original

English

🇨🇳

Translation

Chinese

academic-verify — Trace Claims to Source Data

academic-verify — 追踪声明至源数据

Convention: see conventions/quality.md for citation rules; every verdict cites the source data, not just the author's claim about the source data.

Convention: see conventions/brain-first.md for the lookup chain. This skill enforces brain-first by checking existing brain pages before issuing a fresh web search.

约定： 请查看conventions/quality.md了解引用规则；每个结论都需引用源数据，而非仅引用作者对源数据的声明。

约定： 请查看conventions/brain-first.md了解检索链路。本技能通过在发起新的网络搜索前先检查现有Brain页面，来遵循"Brain优先"的原则。

What this is

这是什么

A claim-verification flow for academic / research statements. When a book, article, or speaker cites a study or quotes a number, this skill traces the claim through:

claim → publication → methodology section → raw data source → independent verification

At each step, it answers:

Where does this number come from? (Self-generated? Survey? Government data?)
What's the baseline? (Reduction from what? Over what time period?)
Is the raw data available? (Public? Proprietary? "Available on request"?)
Has anyone independently verified it? (Replication study? Government audit?)
Are there confounding factors? (Other interventions, policy changes, COVID, sampling bias?)
Is the comparison fair? (Cherry-picked comparison group? Survivorship bias?)

The output is a brain page under

concepts/<claim-slug>.md

that records the claim, the trace, and the verdict — so future references to the same claim can re-use the verified analysis.

一个用于学术/研究声明的验证流程。当书籍、文章或演讲者引用某项研究或引用某个数据时，本技能会通过以下链路追踪该声明：

声明 → 出版物 → 方法章节 → 原始数据源 → 独立验证

在每个步骤中，它会解答以下问题：

这个数据来自哪里？（自行生成？调研？政府数据？）
基准是什么？（相对于什么的减少？在什么时间段内？）
原始数据是否可用？（公开？专有？"需申请获取"？）
是否有独立主体验证过？（重复研究？政府审计？）
是否存在混淆因素？（其他干预措施、政策变化、新冠疫情、抽样偏差？）
对比是否公平？（刻意挑选的对比组？幸存者偏差？）

输出结果是一个存储在

concepts/<claim-slug>.md

路径下的Brain页面，记录声明、追踪过程和结论——这样未来对同一声明的引用可以复用已验证的分析结果。

When to use this

何时使用

A book quotes a study and you want to confirm it's real and not miscited
An article makes a quantified claim ("X reduced Y by 40%") that you want traced to the source data
You're writing something that depends on a piece of research and you want to verify the underlying paper holds up
You're updating a brain page that cites a research claim and you want to record the verification status alongside

书籍引用了某项研究，你想要确认该研究真实存在且未被错误引用
文章提出了量化声明（如"X使Y减少了40%"），你想要追踪到源数据
你正在撰写依赖某份研究的内容，想要验证相关论文是否站得住脚
你正在更新引用了研究声明的Brain页面，想要同时记录验证状态

What this skill is NOT

本技能不适用于以下场景

Not adversarial / oppo work. The point is rigor, not takedown.
Not generic web research — use
```
perplexity-research
```
directly for open-ended topic exploration.
Not a brain-only lookup — that's
```
gbrain query
```
.

非对抗性/针对性调查工作。核心是严谨性，而非驳斥。
通用网络研究——如需开放式主题探索，请直接使用
```
perplexity-research
```
。
仅基于Brain的检索——那是
```
gbrain query
```
的功能。

How it works (D7/α: pure routing through perplexity-research)

工作原理（D7/α：通过perplexity-research纯路由）

academic-verify is a thin orchestrator. The actual web search is done by perplexity-research. academic-verify's job is the workflow: scoping the claim precisely, sending it through perplexity-research with citation-mode, then formatting the response into a verdict-shaped brain page.

Step 1: Scope the claim
  Pin down EXACTLY what's being claimed:
    • Quote: who said what?
    • Source: which paper / dataset / survey?
    • Number: what specific quantity is claimed?
    • Period: over what time range?

Step 2: Brain-first lookup
  gbrain query "<paper title> OR <author name> OR <claim keywords>"
  If the brain has prior verification of this claim, reuse it.

Step 3: Invoke perplexity-research with citation-mode prompt
  Send the claim + brain context to perplexity-research with a prompt
  that explicitly asks for:
    • Original publication (title, authors, journal, year, DOI)
    • Methodology section summary
    • Raw data availability (public repo? proprietary?)
    • Independent replication status (Retraction Watch / PubPeer hits)
    • Citations of the paper that critique or contextualize it

Step 4: Format the verdict
  Write the result to concepts/<claim-slug>.md. The verdict is one of:
    • Verified — claim is accurate; raw data available; replication exists
    • Partially verified — claim correct on the underlying paper but
      methodology has known limits; record limits explicitly
    • Unverifiable — no public data, no replication; not enough to act
    • Misattributed — the claim cites a paper but the paper doesn't say that
    • Retracted / disputed — paper has known retraction or
      well-documented critique

Step 5: Cross-link to original sources
  Add the paper authors to people/ if they have brain pages, or create
  one if notable. Iron Law per conventions/quality.md.

academic-verify是一个轻量编排工具。实际的网络检索由perplexity-research完成。academic-verify的职责是工作流管理：精准界定声明范围，将其置于引用模式下发送给perplexity-research，然后将响应格式化为结论型Brain页面。

步骤1：界定声明范围
  精准确定要验证的内容：
    • 引用内容：谁提出了什么观点？
    • 来源：哪篇论文/数据集/调研？
    • 数据：声明中具体的量化指标是什么？
    • 时间范围：在什么时间段内？

步骤2：Brain优先检索
  执行gbrain query "<论文标题> OR <作者姓名> OR <声明关键词>"
  如果Brain中已有对该声明的验证记录，则直接复用。

步骤3：以引用模式调用perplexity-research
  将声明和Brain上下文发送给perplexity-research，并附带明确要求获取以下内容的提示：
    • 原始出版物（标题、作者、期刊、年份、DOI）
    • 方法章节摘要
    • 原始数据可用性（公开仓库？专有？）
    • 独立重复验证状态（Retraction Watch / PubPeer相关记录）
    • 对该论文进行批评或 contextualize 的引用文献

步骤4：格式化结论
  将结果写入concepts/<claim-slug>.md。结论分为以下几种：
    • 已验证——声明准确；原始数据可用；存在重复验证
    • 部分验证——声明基于的论文内容正确，但研究方法存在已知局限性；需明确记录这些局限性
    • 无法验证——无公开数据，无重复验证；不足以支撑后续行动
    • 错误归因——声明引用了某篇论文，但该论文并未提及相关内容
    • 已撤回/存在争议——论文已被撤回，存在重大关切声明，或有详实记录的批评反驳了核心结论

步骤5：与原始来源建立交叉链接
  如果作者已有Brain页面，则将其添加至people/目录下；若作者具有一定知名度但无Brain页面，则创建新页面。遵循conventions/quality.md中的铁律。

Output: brain page format

输出：Brain页面格式

markdown

---
title: "[Claim summary] — Verified"
type: research
date: YYYY-MM-DD
verdict: "verified|partial|unverifiable|misattributed|retracted"
brain_context_slugs: ["pages cited as context"]
---

markdown

---
title: "[声明摘要] — 已验证"
type: research
date: YYYY-MM-DD
verdict: "verified|partial|unverifiable|misattributed|retracted"
brain_context_slugs: ["作为上下文引用的页面"]
---

[Claim summary] — Verified

[声明摘要] — 已验证

One-line: the verdict + the bottom-line reason.

一句话总结：结论 + 核心原因。

The Claim

声明内容

Exact quote, exactly as stated, with source attribution.

准确引用原文，附带来源归属。

Trace

追踪过程

Step	Finding	Source
Original publication	[Title, authors, year, DOI]	[URL]
Methodology	[1-line summary; flag obvious limits]	[URL]
Raw data	[Public repo / proprietary / available-on-request]	[URL]
Independent replication	[Replication studies and their results]	[URL]
Critical citations	[Papers that critique this work]	[URL]

步骤	发现	来源
原始出版物	[标题、作者、年份、DOI]	[URL]
研究方法	[1行摘要；标记明显局限性]	[URL]
原始数据	[公开仓库 / 专有 / 需申请获取]	[URL]
独立重复验证	[重复研究及其结果]	[URL]
批判性引用	[批评该研究的论文]	[URL]

Verdict

结论

[Verified / Partially verified / Unverifiable / Misattributed / Retracted]

[1-2 paragraphs explaining WHY the verdict, with specific evidence.]

[已验证 / 部分验证 / 无法验证 / 错误归因 / 已撤回]

[1-2段解释结论的依据，附带具体证据。]

Caveats

注意事项

[Honest limits: what we couldn't verify, what would change the verdict.]

[如实说明局限性：无法验证的内容、可能改变结论的因素。]

Useful databases (the agent uses these via perplexity-research)

实用数据库（Agent通过perplexity-research调用这些数据库）

Database	What it has	URL pattern
Retraction Watch	Retractions, corrections, expressions of concern	retractionwatch.com/?s=NAME
PubPeer	Anonymous post-publication peer review	pubpeer.com/search?q=NAME
OSF	Pre-registrations, open data, open materials	osf.io/search/?q=QUERY
Semantic Scholar	Citation analysis, paper metadata	api.semanticscholar.org
OpenAlex	Open citation data, institutional affiliations	api.openalex.org
Many Labs	Replication results for social psychology	osf.io/wx7ck/

数据库	内容	URL格式
Retraction Watch	撤回记录、更正内容、关切声明	retractionwatch.com/?s=NAME
PubPeer	匿名出版后同行评审	pubpeer.com/search?q=NAME
OSF	预注册信息、开放数据、开放资料	osf.io/search/?q=QUERY
Semantic Scholar	引用分析、论文元数据	api.semanticscholar.org
OpenAlex	开放引用数据、机构隶属关系	api.openalex.org
Many Labs	社会心理学重复研究结果	osf.io/wx7ck/

Standards (the rigor bar)

标准（严谨性要求）

Verified — only when the underlying paper exists, raw data is public OR an independent lab has confirmed the result, and the citing source represents the claim accurately.
Partial — paper is real and findings stand, but the citation context oversells (e.g., "X causes Y" when the paper shows correlation, or "all studies find X" when it's one underpowered study).
Unverifiable — the underlying number can't be traced to source data, no replication has been done, no independent confirmation exists. Not the same as "wrong" — say "we couldn't verify."
Misattributed — the citation points to a paper, but the paper doesn't actually say what the citation claims. Common in policy briefs.
Retracted / disputed — paper has been retracted, has a major expression-of-concern, or has well-documented critique that contradicts the headline finding.

Never claim a problem without evidence. The verification document itself is the artifact — if the claim holds up, say so plainly. If it doesn't, the trace speaks for itself.

已验证——仅当基础论文存在、原始数据公开或已有独立实验室确认结果，且引用来源对声明的表述准确时，才可标记为此类。
部分验证——论文真实存在且结论成立，但引用语境夸大了结论（例如，论文仅显示相关性，引用却称"X导致Y"；或仅为一项样本量不足的研究，引用却称"所有研究均发现X"）。
无法验证——无法追踪到基础数据的来源，未进行重复验证，无独立确认。这与"错误"不同——应表述为"我们无法验证"。
错误归因——引用指向某篇论文，但该论文实际上并未提及引用所声称的内容。这种情况在政策简报中较为常见。
已撤回/存在争议——论文已被撤回，存在重大关切声明，或有详实记录的批评反驳了核心结论。

无证据不得指出问题。验证文档本身就是凭证——如果声明成立，就直接说明；如果不成立，追踪过程会自行说明问题。

Anti-Patterns

反模式

❌ Skipping the brain-first lookup. Re-doing verification we've already done is wasted Perplexity spend.
❌ Bypassing perplexity-research and inventing the lookup. The citations from Perplexity are the evidence — without them, the verdict is just opinion.
❌ Stating "Verified" without confirming raw data availability. Replication trumps any single paper.
❌ Stating "Unverifiable" when you simply didn't look hard enough. The verdict is on the source, not on your search effort.

❌ 跳过Brain优先检索。重复我们已完成的验证工作是在浪费Perplexity的资源。
❌ 绕过perplexity-research自行进行检索。Perplexity提供的引用是证据——没有这些引用，结论只是个人观点。
❌ 未确认原始数据可用性就标记"已验证"。重复验证的优先级高于任何单一论文。
❌ 仅因检索不够深入就标记"无法验证"。结论针对的是来源，而非你的检索努力。

Related skills

Contract

契约

This skill guarantees:

Routing matches the canonical triggers in the frontmatter.
Output written under the directories listed in
```
writes_to:
```
(when applicable).

Conventions referenced (

quality.md

brain-first.md

_brain-filing-rules.md

) are followed.

Privacy contract preserved: no real names, no fork-specific filesystem path literals, no upstream-fork references.

The full behavior contract is documented in the body sections above; this section exists for the conformance test.

本技能保证：

路由符合首页中列出的标准触发条件。
输出写入
```
writes_to:
```
中列出的目录（如适用）。

遵循所引用的约定（

quality.md

、

brain-first.md

、

_brain-filing-rules.md

）。

遵守隐私契约：不使用真实姓名，不包含分支特定的文件系统路径字面量，不引用上游分支。

完整的行为契约已在上述主体部分记录；本部分用于一致性测试。

Output Format

输出格式

The skill's output shape is documented inline in the body sections above (see "Output", "Brain page format", or equivalent). The literal section header here exists for the conformance test (

test/skills-conformance.test.ts

技能的输出格式已在上述主体部分内联记录（请查看"输出"、"Brain页面格式"或等效部分）。此处的文字章节标题用于一致性测试（

test/skills-conformance.test.ts

）。