Agsy Revision And Rebuttal logo

Agsy Revision And Rebuttal

Community
franklee16
agsy-revision-and-rebuttal

Use when writing the response to an Agricultural Systems (AgSy) revision decision. AgSy review is single anonymized with at least two expert systems reviewers and an editor decision, and revisions often turn on model evaluation, sensitivity/uncertainty, and trade-off framing. The response must convert each reviewer without alienating the editor. Structures the response letter; it does not fabricate new results.

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameagsy-revision-and-rebuttal
Stars
225
Forks
34
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by franklee16 on GitHub. Read the source before you install it.

Installation

Install the Agsy Revision And Rebuttal 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/franklee16/academic-research-skills/tree/master/Awesome-Journal-Skills-main/Agricultural-Systems-Skills/skills/agsy-revision-and-rebuttal
  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/franklee16/academic-research-skills.git /tmp/academic-research-skills
mkdir -p .claude/skills
cp -r /tmp/academic-research-skills/Awesome-Journal-Skills-main/Agricultural-Systems-Skills/skills/agsy-revision-and-rebuttal .claude/skills/agsy-revision-and-rebuttal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agsy Revision And Rebuttal 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 Agsy Revision And Rebuttal 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 Agsy Revision And Rebuttal 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.

Revision & Rebuttal (agsy-revision-and-rebuttal)

A revise decision at AgSy is a real chance — but systems reviewers tend to press on the model (calibration, evaluation, sensitivity, uncertainty) and the trade-off / decision framing. The response letter must move every reviewer toward yes while keeping the editor confident the revision converges. Reviewers are not anonymous to you on their side (single anonymized), but keep the tone professional and evidence-based.

When to trigger

  • A major/minor revision decision arrived and you are planning the revision + response letter
  • Reviewers disagree (e.g., one wants more scenarios, another wants a simpler model)
  • A reviewer requests additional evaluation, sensitivity, or uncertainty analysis
  • Writing the cover note to the editor summarizing the revision

Strategy

  1. Read the editor's letter as the rubric. The editor signals which points are decisive — often model evaluation, uncertainty, or systems framing. Solve those first; the editor adjudicates disagreements among reviewers.
  2. One point-by-point response, every comment addressed. Quote each comment, then respond. Never skip one — silence reads as non-compliance.
  3. Concede or rebut explicitly, with evidence. For each: did what was asked (say where, with the new text/figure/table number, or the new sensitivity/uncertainty run), or push back respectfully with a systems reason (boundary, model structure, data, or scale). A well-argued disagreement beats a capitulation that weakens the analysis.
  4. Strengthen the model, don't just defend it. Most AgSy revisions improve with an added independent evaluation, a sensitivity / uncertainty analysis, or a clearer trade-off exhibit. Offer these proactively where reviewers raise doubt.
  5. Reconcile conflicting reviewers openly. When R1 wants more complexity and R2 wants parsimony, say so, choose a principled path (justified by the question and the decision relevance), and explain the tradeoff to the editor.
  6. Protect the systems contribution. Resist changes that turn the paper back into a single-factor study or dilute the trade-off/decision message. Defend scope conditions rather than over-claiming.
  7. Keep the package in sync. Update the deposited data, code, and model so new tables/figures remain reproducible (see agsy-reproducibility-and-data-policy).

Response-letter format

For each reviewer comment:

> [Quoted reviewer comment]

Response: [What we did / why we respectfully disagree, with a systems reason].
Change: [Section/page/figure-table number; new evaluation/sensitivity run where relevant].

Open with a short summary of the main changes to the editor; group by reviewer; end each entry with the location of every change so the editor can verify quickly.

Anti-patterns

  • Ignoring or merging away a comment without a visible response
  • Adding scenarios/complexity that quietly contradict the original conclusion without acknowledging it
  • Defensive or dismissive tone toward reviewers
  • "We thank the reviewer" with no actual change or argued reason
  • Letting the revised manuscript or new exhibits drift out of sync with the deposited package
  • Conceding a change that collapses the systems framing just to please one reviewer

Output format

【Editor's decisive points】addressed first? [list]
【Coverage】every reviewer comment answered? [Y/N]
【Concede vs rebut】each tagged with evidence + change location
【Model strengthened】added evaluation/sensitivity/uncertainty where pressed? [Y/N]
【Reviewer conflicts】reconciled and explained to editor? [Y/N]
【Contribution protected】systems framing + trade-off intact? [Y/N]
【Package updated】data/code/model in sync? [Y/N]
【Next】resubmit via Editorial Manager

Supplementary resources

Frequently asked questions

What does the Agsy Revision And Rebuttal AI skill do?

Use when writing the response to an Agricultural Systems (AgSy) revision decision. AgSy review is single anonymized with at least two expert systems reviewers and an editor decision, and revisions often turn on model evaluation, sensitivity/uncertainty, and trade-off framing. The response must convert each reviewer without alienating the editor. Structures the response letter; it does not fabricate new results.

Why use Agsy Revision And Rebuttal on TypingMind?

Because you install it once and use it with any model. Agsy Revision And Rebuttal 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 Agsy Revision And Rebuttal in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/franklee16/academic-research-skills/tree/master/Awesome-Journal-Skills-main/Agricultural-Systems-Skills/skills/agsy-revision-and-rebuttal. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agsy Revision And Rebuttal?

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 Agsy Revision And Rebuttal?

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

Is the Agsy Revision And Rebuttal AI skill free?

It is published on GitHub by franklee16. Check the repository for licensing terms. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇