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Evidence Review

Community
scholay
evidence-review

Assess and promote corpus slices toward citation readiness. Use when deciding whether a piece of evidence can be cited, upgrading machine-translated layers to reviewed translations, building evidence matrices for writing, or prioritizing which sources to review first.

Overview

Publisherscholay
Repositoryskills
Skill nameevidence-review
Stars
139
Forks
0
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 scholay on GitHub. Read the source before you install it.

Installation

Install the Evidence Review 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.
    https://github.com/scholay/skills/tree/main/evidence-review
  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/scholay/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/evidence-review .claude/skills/evidence-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evidence Review 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 Evidence Review 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 Evidence Review 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.

Evidence Review

The core problem

Most corpus slices are created by automated pipelines: OCR, machine translation, auto-generated descriptions. They are useful for browsing, but not safe to cite. The gap between "we have 10,000 slices" and "we can write the paper" is evidence review — the deliberate process of deciding which slices are trustworthy enough to quote and cite.

Without a clear ladder between "raw" and "citation-ready," researchers either over-trust machine output or spend time reviewing everything equally. Neither is efficient.

The citation readiness ladder

LevelStatusWhat it means
0draftAuto-generated, not yet looked at by a human
1checkedSource text verified against the original document
2reviewedWorking translation confirmed accurate; can be paraphrased safely
3quote_readyCan be quoted or cited directly; citekey and page locator confirmed

Only level 3 slices should appear as direct citations in writing. Level 2 is sufficient for paraphrase and synthesis.

The key insight: levels 0 and 1 are about accuracy of the source text. Levels 2 and 3 are about the translation/working layer. They are separate judgments.

Workflow

1. Prioritize what to review

Don't review everything. Identify which slices are thesis-critical first:

  • Slices already referenced in your writing or notes
  • Slices from your highest-value sources
  • Slices that answer your core research questions directly

For each priority cluster, look at quality scores from corpus building (P1 pages need review before any other use; P2 should be sampled).

2. Per-slice review procedure

For each slice you're reviewing:

  1. Open the original source page alongside the slice card.
  2. Verify that the source text (original excerpt or OCR text) accurately represents the page. Correct OCR errors if found.
  3. Set verification_status: checked when source text is confirmed.
  4. Review the working translation: does it accurately convey the source meaning? Correct or rewrite as needed.
  5. Set translation_status: reviewed when the working layer is trusted.
  6. If this slice is suitable for direct quotation or citation:
    • Confirm the citekey exists in your bibliography
    • Confirm the page locator is correct
    • Set quote_ready: true

3. Maintain an evidence matrix

For each chapter or section you're writing, keep a simple matrix:

Slice IDChapterSectionRoleStatusNotes
SLC-0001-T003Ch22.1supports claim about Xreviewedparaphrase only
SLC-0007-T012Ch33.2direct quotationquote_readycite p.47

This makes the link between your argument and your evidence explicit and checkable.

4. Run the integrity check before writing

Before each writing session, verify:

  • Every slice you plan to cite has at least verification_status: checked
  • Every slice you plan to quote directly has quote_ready: true
  • Every citekey in your evidence matrix resolves in your bibliography

Rules

  • Do not promote a slice to quote_ready based on quality scores alone. Human review of both source text and translation is required.
  • Do not cite from machine_draft or agent_draft translation layers directly. These are working materials, not citation materials.
  • Separate source text accuracy from translation accuracy — they are different judgments, made at different stages.
  • A slice at reviewed level is safe to paraphrase. A slice at quote_ready level is safe to quote.
  • When you first use a slice in writing, add it to the evidence matrix immediately.
  • OCR-assisted image descriptions (ocr_assisted_draft) are not citation-ready visual evidence.

References

  • Read references/citation-criteria.md for the full checklist of what each ladder level requires for text and image slices.

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 Evidence Review AI skill do?

Assess and promote corpus slices toward citation readiness. Use when deciding whether a piece of evidence can be cited, upgrading machine-translated layers to reviewed translations, building evidence matrices for writing, or prioritizing which sources to review first.

Why use Evidence Review on TypingMind?

Because you install it once and use it with any model. Evidence Review 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 Evidence Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/scholay/skills/tree/main/evidence-review. 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 Evidence Review?

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 Evidence Review?

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

Is the Evidence Review AI skill free?

Yes. It is published on GitHub by scholay 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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