Peer Review

Community

richard-kim-79

peer-review

Simulates a full academic journal peer review with EIC decision and 3 independent reviewers (Methodology Expert, Domain Specialist, Devil's Advocate). Scores 1–10, provides major/minor comments, and issues a final verdict: Accept, Minor Revision, Major Revision, or Reject. Use when the user asks for a peer review, journal-quality feedback, readiness assessment, or to simulate reviewer responses before submission.

Overview

Publisher	richard-kim-79
Repository	`archora-skills`
Skill name	`peer-review`
Stars	50
Forks	12
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by richard-kim-79 on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/richard-kim-79/archora-skills/tree/main/skills/peer-review
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.

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/richard-kim-79/archora-skills.git /tmp/archora-skills
mkdir -p .claude/skills
cp -r /tmp/archora-skills/skills/peer-review .claude/skills/peer-review

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Peer 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 Peer 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 Peer 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.

Peer Review Simulation

Simulate a rigorous journal peer review process with multiple independent reviewers.

Reviewers

Role	Focus
Editor-in-Chief (EIC)	Overall fit, quality, editorial decision
Reviewer 1 — Methodology Expert	Research design, statistical rigor, reproducibility
Reviewer 2 — Domain Specialist	Theoretical framework, literature coverage, contribution
Reviewer 3 — Devil's Advocate	Weaknesses, alternative interpretations, ethical concerns

Scoring

1–4: Reject
5–6: Major revision needed
7–8: Minor revision; publishable with changes
9–10: Accept; exceptional work

Verdicts

ACCEPT — publishable as-is
MINOR_REVISION — small changes needed; likely accepted after revision
MAJOR_REVISION — significant work required; re-review needed
REJECT — fundamental issues; not suitable for this venue

Output Format

markdown
# 🔬 Peer Review Simulation

> **Final Decision: 🟠 Major Revision** | Overall Score: **6/10**

## Editor-in-Chief Decision

**Decision:** 🟠 Major Revision
**Score:** 6/10

[3–4 sentence editorial summary]

---

## Reviewer Reports

### 🔬 Reviewer 1: Methodology Expert

**Recommendation:** 🟠 Major Revision | **Score:** 6/10

[Overall impression]

**Major Concerns:**
- [Critical issue 1]
- [Critical issue 2]

**Minor Suggestions:**
- [Minor suggestion 1]

---
[... repeat for each reviewer ...]

## 🟠 Final Decision: Major Revision

### Priority Action Items

- [ ] [Most important revision 1]
- [ ] [Most important revision 2]
- [ ] [Most important revision 3]

| Reviewer | Score | Recommendation |
|----------|-------|----------------|
| Editor-in-Chief | 6/10 | 🟠 Major Revision |
| Methodology Expert | 6/10 | 🟠 Major Revision |
| Domain Specialist | 7/10 | 🟡 Minor Revision |
| Devil's Advocate | 5/10 | 🟠 Major Revision |

Guidelines for honest reviews

Score calibration: theoretical-only papers without original data should score 5–7 max
Reviewers must disagree sometimes — a spread of 5–7 across reviewers is healthy
Major concerns must reference specific claims or sections in the paper
Minor suggestions must be genuinely minor — not disguised major concerns
Devil's Advocate must find real weaknesses, not be contrarian for its own sake

Frequently asked questions

What does the Peer Review AI skill do?

Why use Peer Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/richard-kim-79/archora-skills/tree/main/skills/peer-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Peer 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 Peer Review?

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

Is the Peer Review AI skill free?

Yes. It is published on GitHub by richard-kim-79 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Peer Review Simulation

Reviewers

Scoring

Verdicts

Output Format

Guidelines for honest reviews

Frequently asked questions

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