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Rebuttal Baseline

Organization
Optima-CityU
rebuttal-baseline

Build and confirm the complete paper, review, constraint, concern, and reviewer-card baseline before rebuttal generation.

Overview

PublisherOptima-CityU
RepositoryLLM4AD_Next
Skill namerebuttal-baseline
Stars
596
Forks
41
Bundled files
Instructions only
LicenseBSD-3-Clause
Links
Skill sourceRepositoryHomepage
  • 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 Optima-CityU on GitHub. Read the source before you install it.

Installation

Install the Rebuttal Baseline 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/Optima-CityU/LLM4AD_Next/tree/main/skills/autorebuttal/rebuttal-baseline
  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/Optima-CityU/LLM4AD_Next.git /tmp/LLM4AD_Next
mkdir -p .claude/skills
cp -r /tmp/LLM4AD_Next/skills/autorebuttal/rebuttal-baseline .claude/skills/rebuttal-baseline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rebuttal Baseline 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 Rebuttal Baseline 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 Rebuttal Baseline 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.

Rebuttal Baseline

Read these shared references completely before acting:

  • /app/skills/autorebuttal-shared/references/input-contract.md
  • /app/skills/autorebuttal-shared/references/reviewer-analysis.md
  • /app/skills/autorebuttal-shared/references/reviewer-model.md
  • /app/skills/autorebuttal-shared/references/rebuttal-playbook.md
  • /app/skills/autorebuttal-shared/references/evidence-policy.md
  • /app/skills/autorebuttal-shared/references/artifact-contracts.md

This author-facing phase contains two internal steps.

Step 1: Intake

Read the paper entry point and follow relevant includes. Then collect reviews from the available sources:

  1. Read every report in the review index when it is non-empty.
  2. Read any review text the author pasted into the conversation, including separately labeled reports in one message. Preserve each distinct conversation-only report in the corresponding reviewer_sources item as review_markdown, organized for display with Markdown headings and lists. Retain the supplied details and do not invent absent fields. The right-hand reviewer panel displays this Markdown after baseline publication.
  3. Merge matching reports from these sources without dropping reviewer-specific details. A forum URL alone is not review text; ask the author to paste the report or enter it in the right-hand review panel.
  4. If neither source contains a review, ask the author to paste one into the conversation or enter it in the review panel.

Assign one stable reviewer UUID per distinct report. Use the indexed UUID for saved reviews; for a conversation-only report, create a UUID and retain it in the published baseline. Preserve a human-readable reviewer label separately. Do not claim to have read a linked forum unless its review text was supplied.

Normalize the paper summary, stable reviewer UUIDs and display labels, venue, response mode, output format, author notes, forbidden claims, and unresolved questions.

Step 2: Review analysis

Split every substantive report into atomic W weaknesses, Q questions, and M minor points. Record stable concern IDs, severity, likely answer source, response move, and auditable source references. Build exactly one reviewer card per indexed report and assign every concern to exactly one matching card. Use the index reviewer_id UUID everywhere; never substitute or derive it from the human-readable display_label.

Present the compact baseline to the author. Do not draft rebuttal prose, choose a final strategy, edit paper files, or invent venue policy. Missing venue/year, the default response mode or output format, absent numeric limits, and evidence that will require an author placeholder are not generation blockers. Record such items as assumptions, warnings, or open questions and continue.

Only set ready_for_generation false when the source material is unusable for an evidence-grounded response: the paper has no substantive readable content, neither the review index nor the conversation supplies a review, or none of the collected reviews contains a substantive concern. In that case, identify the exact missing source and required author action. Otherwise publish the baseline with ready_for_generation true.

Publish the complete Baseline phase contract exactly once with publish_stage_result and idempotency key rebuttal-baseline-v1.

Frequently asked questions

What does the Rebuttal Baseline AI skill do?

Build and confirm the complete paper, review, constraint, concern, and reviewer-card baseline before rebuttal generation.

Why use Rebuttal Baseline on TypingMind?

Because you install it once and use it with any model. Rebuttal Baseline 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 Rebuttal Baseline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Optima-CityU/LLM4AD_Next/tree/main/skills/autorebuttal/rebuttal-baseline. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Rebuttal Baseline?

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 Rebuttal Baseline?

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

Is the Rebuttal Baseline AI skill free?

Yes. It is published on GitHub by Optima-CityU under the BSD-3-Clause 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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