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Anti Defensive Writing En

CommunityPopular
Adkid-Zephyr
anti-defensive-writing-en

Stops defensive writing across the entire paper lifecycle — writing, revising, cutting, and organizing experiments. Treats the paper as a press conference, not a project summary, lab log, or self-audit: identify the single most publishable strength of the work and build the most favorable, complete, and persuasive narrative around it. Never present everything evenly, never volunteer weakness, never write an experiment diary, never attack the paper on the reviewer's behalf. Triggers: polish my paper, revise manuscript, write abstract, write introduction, write conclusion, shorten paper, rebuttal, respond to reviewers, organize experiments, defensive writing, AI-flavored academic writing.

Overview

PublisherAdkid-Zephyr
Repositoryanti-defensive-writing-Skill
Skill nameanti-defensive-writing-en
Stars
1.2K
Forks
27
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

    Published by Adkid-Zephyr on GitHub. Read the source before you install it.

Installation

Install the Anti Defensive Writing En 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/Adkid-Zephyr/anti-defensive-writing-Skill.git /tmp/anti-defensive-writing-Skill
mkdir -p .claude/skills
cp -r /tmp/anti-defensive-writing-Skill/skills/anti-defensive-writing-en .claude/skills/anti-defensive-writing-en
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Anti Defensive Writing En 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 Anti Defensive Writing En 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 Anti Defensive Writing En 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.

The Press-Release Principle (Paper as Press Conference)

One-line principle

A paper is a press conference, not a project summary, a lab log, or a self-audit. Your job is not to present every aspect of the work evenly, but to identify its most publishable value and build the most favorable, complete, and persuasive narrative around it.

1. Narrative rules

  1. Organize the paper around strengths only. Look for what is genuinely ahead, unique, or irreplaceable: a new capability, a new problem, a new mechanism, a new perspective, broader applicability, lower cost, higher efficiency, better scalability, or a more meaningful trade-off. Content that forms no advantage does not enter the main storyline.
  2. Do not write a work report. No "we first did X, then tried Y, and Z failed" chronology. Keep only the final standing logic: why the problem matters → why existing methods fall short → what this paper provides → how the evidence supports it.
  3. Never set a contest you cannot win. If a metric is not your strength, do not build the narrative on it. Re-choose the task definition, evaluation dimension, application scenario, constraint, or comparison frame that best reflects your value. Name the game this paper actually wins.
  4. State the advantage explicitly. Do not expect reviewers to discover the contribution from a table. Explain: under which condition the method performs best, why the advantage emerges, what practical problem it solves, and why it deserves attention.
  5. Limit the comparison scope. Do not chase "wins on every dataset and metric." Make only claims your evidence firmly supports. Persuasion comes from tight claim-evidence alignment, not from the number of comparisons.
  6. Allow full story restructuring. When existing results cannot support the original narrative, do not defend it. Redefine the problem, re-order the contributions, re-pick the headline result, redesign title, abstract, introduction, and experiment structure. The story serves the strongest evidence, not the original plan.

2. Language rules (against self-undermining)

  • Banned self-weakening phrases: "unfortunately", "merely", "only achieves", "still lags far behind", "limited improvement", "severely insufficient".
  • Never say you lost. For unfavorable results, avoid "our method is weaker", "performance drops", "fails to surpass". Judge in order: ① must this result be discussed at all; ② can it be explained by different goals, constraints, or applicable scenarios; ③ does it reflect a reasonable trade-off; ④ can the claim be narrowed to avoid a head-on collision; ⑤ should the storyline change so this result no longer carries the core contribution.
  • If a result forms no advantage, do not distill it into a conclusion — and never help the reader escalate a local observation into a verdict on the whole method.

3. Experiment rules

Experiments are tools of argument, not a warehouse of results. Every experiment must carry one explicit duty:

  • prove the core method works;
  • prove the advantage comes from the key mechanism;
  • prove the method matters in the target scenario;
  • rule out the most plausible alternative explanations.

An experiment that does not strengthen the main line, distracts, or invites irrelevant disputes: delete, downplay, move, or redesign it.

4. Structure rules

Abstract and introduction = the opening of the press conference. Establish four things fast: ① an important, unsolved problem; ② the key gap in existing methods; ③ your distinctive approach; ④ your strongest result and its meaning. Do not open with implementation details, process, or a wall of background. Do not discuss limitations before the contribution is established.

The conclusion only reinforces the takeaway: what was solved, what was proposed, what was proven, why it matters. Never introduce a fresh self-negation or expanded limitations in the final paragraph.

5. Default decision rules (for any unfavorable material, in priority order)

  1. Delete content irrelevant to the core claim;
  2. Narrow the claim to avoid a pointless head-on comparison;
  3. Switch to an evaluation dimension that better reflects the value;
  4. Explain the result as a difference in goals or a reasonable trade-off;
  5. Reorganize experiments so the advantage becomes the visual and narrative center;
  6. Redefine the paper's story;
  7. Only when unavoidable and truly affecting the core conclusion, state it plainly.

6. Pre-submission checklist (never hand the reviewer a knife)

  • Does this sentence quietly expand the responsibility the paper must carry?
  • Does it raise a question nobody asked for?
  • Does it describe a local phenomenon as a universal flaw?
  • Does it use a negative judgment broader than the evidence?
  • Is any passage unfolding as "what we did" process narration?
  • Is a non-winning metric set as the main battlefield?
  • In the first five sentences of the abstract, are both the problem and the contribution present?
  • Does the conclusion smuggle in a sudden self-negation?

Do not manufacture review questions. Do not enlarge the attack surface. Do not complete the opposing side's argument for them.

Final goal

Every section, paragraph, table, and sentence should do one thing together: convince the reader that this work solves a problem worth solving, proposes a method worth noticing, and already has sufficiently clear evidence of its value. Find the advantage that truly holds, organize all material around it, and make that advantage unmistakably clear.

Frequently asked questions

What does the Anti Defensive Writing En AI skill do?

Stops defensive writing across the entire paper lifecycle — writing, revising, cutting, and organizing experiments. Treats the paper as a press conference, not a project summary, lab log, or self-audit: identify the single most publishable strength of the work and build the most favorable, complete, and persuasive narrative around it. Never present everything evenly, never volunteer weakness, never write an experiment diary, never attack the paper on the reviewer's behalf. Triggers: polish my paper, revise manuscript, write abstract, write introduction, write conclusion, shorten paper, rebu...

Why use Anti Defensive Writing En on TypingMind?

Because you install it once and use it with any model. Anti Defensive Writing En 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 Anti Defensive Writing En in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Adkid-Zephyr/anti-defensive-writing-Skill/tree/main/skills/anti-defensive-writing-en. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Anti Defensive Writing En?

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 Anti Defensive Writing En?

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

Is the Anti Defensive Writing En AI skill free?

Yes. It is published on GitHub by Adkid-Zephyr 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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