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Academic Verify

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garrytan
academic-verify

Verify a research claim or academic citation by tracing it through publication → methodology → raw data → independent replication. Routes through perplexity-research for the actual web lookup, then formats results as a citation-checked brain page. Use when a book/article/conversation cites a study and you want to confirm the claim is real, replicated, and accurately characterized.

Overview

Publishergarrytan
Repositorygbrain
Skill nameacademic-verify
Stars
30.1K
Forks
4.5K
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by garrytan on GitHub. Read the source before you install it.

Installation

Install the Academic Verify AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/garrytan/gbrain.git /tmp/gbrain
mkdir -p .claude/skills
cp -r /tmp/gbrain/plugin/skills/academic-verify .claude/skills/academic-verify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Academic Verify in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Academic Verify on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Academic Verify is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

academic-verify — Trace Claims to Source Data

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.

What this is

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.

When to use this

  • 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

What this skill is NOT

  • 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.

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

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.

Output: brain page format

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

# [Claim summary] — Verified

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

## The Claim

> Exact quote, exactly as stated, with source attribution.

## Trace

| 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] |

## Verdict

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

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

## Caveats

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

## See Also

- Original paper: [Title](DOI URL)
- Authors' brain pages: [Author 1](people/author-1.md), ...
- Related claims (verified or otherwise): [...]

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

DatabaseWhat it hasURL pattern
Retraction WatchRetractions, corrections, expressions of concernretractionwatch.com/?s=NAME
PubPeerAnonymous post-publication peer reviewpubpeer.com/search?q=NAME
OSFPre-registrations, open data, open materialsosf.io/search/?q=QUERY
Semantic ScholarCitation analysis, paper metadataapi.semanticscholar.org
OpenAlexOpen citation data, institutional affiliationsapi.openalex.org
Many LabsReplication results for social psychologyosf.io/wx7ck/

Standards (the rigor bar)

  • 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.

Anti-Patterns

  • ❌ 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.

Related skills

  • skills/perplexity-research/SKILL.md — the actual web-search engine this skill routes through (D7/α: pure routing, no new infrastructure)
  • skills/citation-fixer/SKILL.md — fixes citation FORMATTING; this skill checks whether the cited claim is true
  • skills/conventions/quality.md — citation + back-link rules

Contract

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.

Output Format

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).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Academic Verify AI skill do?

Verify a research claim or academic citation by tracing it through publication → methodology → raw data → independent replication. Routes through perplexity-research for the actual web lookup, then formats results as a citation-checked brain page. Use when a book/article/conversation cites a study and you want to confirm the claim is real, replicated, and accurately characterized.

Why use Academic Verify on TypingMind?

Because you install it once and use it with any model. Academic Verify is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Academic Verify in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/garrytan/gbrain/tree/master/plugin/skills/academic-verify. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Academic Verify?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Academic Verify?

As many as you like. As long as a model supports skills, you can use Academic Verify with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Academic Verify AI skill free?

Yes. It is published on GitHub by garrytan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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