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Academic Defensive Writing Auditor

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Worigin0314
academic-defensive-writing-auditor

Detect and rewrite defensive, reviewer-facing, disclaimer-heavy academic prose while preserving necessary scientific caveats and evidence boundaries.

Overview

PublisherWorigin0314
Repositoryacademic-defensive-writing-auditor
Skill nameacademic-defensive-writing-auditor
Stars
124
Forks
4
Bundled files
7
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.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Worigin0314 on GitHub. Read the source before you install it.

Installation

Install the Academic Defensive Writing Auditor 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/Worigin0314/academic-defensive-writing-auditor/tree/main
  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/Worigin0314/academic-defensive-writing-auditor.git \
  .claude/skills/academic-defensive-writing-auditor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Academic Defensive Writing Auditor 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 Academic Defensive Writing Auditor 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 Academic Defensive Writing Auditor 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.

Academic Defensive Writing Auditor

Purpose

Audit academic writing for language that sounds defensive, anxious, reviewer-facing, over-explanatory, legally disclaiming, or artificially self-protective.

The goal is not to remove legitimate scientific uncertainty or make claims stronger than the evidence supports.

Prefer:

claim → evidence → concise boundary

over:

anticipated objection → disclaimer → justification → weakened claim → another disclaimer

A scientific caveat is useful when it changes how evidence should be interpreted. A defensive caveat is harmful when it mainly anticipates reviewer criticism.

Defensive Writing Taxonomy

D1. Reviewer-facing prebuttal

Signals:

  • "We emphasize that..."
  • "It should be noted that..."
  • "Importantly, we do not claim..."
  • "To avoid misunderstanding..."
  • "One might argue that..."
  • "A potential concern is..."

Action: State the technical fact directly.

D2. Repeated non-claim disclaimers

Signals:

  • "We do not claim that..."
  • "This should not be interpreted as..."
  • "This does not establish..."
  • "We make no claim regarding..."

Action: State the supported claim positively and scope it once.

D3. Caveat stacking

Signals: Multiple although / while / despite / within / only / may / might / potentially qualifiers around one claim.

Action: Keep the strongest supported claim and move one material limitation to the proper section.

D4. Excusing imperfect results

Signals:

  • "The lower score is likely due to..."
  • "This degradation is expected because..."
  • "The result remains encouraging despite..."
  • "Given the difficulty of the setting..."

Action: Report the result first. Explain only when supported by evidence or diagnostics.

D5. Justifying omitted experiments

Signals:

  • "We did not include X because..."
  • "X is unnecessary because..."
  • "Such a comparison would be unfair..."
  • "We leave this out since..."

Action: If material, state the omission once as a limitation. Otherwise describe the evaluation protocol rather than arguing with an imagined reviewer.

D6. Fairness self-defense

Signals:

  • "For a fair comparison..."
  • "To ensure fairness..."
  • "This provides a fair setting..."
  • "We believe this is sufficient..."

Action: Describe the controlled variable directly.

Example: Bad:

For a fair comparison, all methods use the same backbone.

Better:

All methods use the same backbone.

D7. Preliminary / limited-scope repetition

Repeated use of:

  • preliminary
  • initial
  • suggestive
  • exploratory
  • proof-of-concept
  • within-scope
  • indicative

Action: Use these labels only when technically necessary.

D8. Legalistic disclaimer prose

Long lists of things the paper should not be interpreted as claiming.

Action: Convert them into short positive scope statements.

D9. Irrelevant defensive disclosure

Reporting exploratory failures or procedural detours that do not affect validity, reproducibility, or interpretation.

Action: Keep only details that matter scientifically. Move the rest to supplement or repository logs.

D10. Promotional compensation

Signals:

  • strong
  • favorable
  • compelling
  • practical
  • reliable
  • safe
  • auditable
  • comprehensive
  • robust
  • promising
  • encouraging

Action: Replace adjectives with measurements, protocol facts, or named properties.

D11. AI-style automatic summary sentences

Signals:

  • "Together, these results demonstrate..."
  • "Taken together, these findings..."
  • "This provides a concrete foundation for..."
  • "These results further validate..."

Action: Delete if redundant, or replace with the single specific inference justified by the data.

D12. Evidence-boundary over-signaling

Pattern:

The evidence supports X within the evaluated scope, but does not establish Y.

Action: Usually prefer:

Under the evaluated setting, X.

Mention untested generalization once in Limitations if material.

D13. Absolute defensive claims

Signals:

  • "Any adaptive method would fail..."
  • "This guarantees..."
  • "It is impossible..."
  • "There is no need..."
  • "No method can..."

Action: Use the exact empirical or theoretical condition supporting the statement.

D14. Contribution-by-relabeling language

Signals:

  • introducing new terminology for a standard operation;
  • repeatedly defending novelty;
  • "our method is not simply X applied to Y."

Action: Describe the actual technical delta directly.

Scientific Caveat vs Defensive Wording Test

For every suspicious sentence:

  1. Validity: Would removing it make the interpretation materially misleading?
  2. Evidence: Does it report a supported condition, uncertainty, or limitation?
  3. Reviewer simulation: Is its main purpose to answer an imagined criticism?
  4. Positive reformulation: Can it be stated as what was evaluated, observed, or outside scope?
  5. Duplication: Has the same caveat already appeared elsewhere?

Classify each case as:

  • NECESSARY_CAVEAT
  • DEFENSIVE
  • MIXED
  • CLEAN

Severity

  • Critical: rebuttal-like, apologetic, argumentative, or legalistic.
  • Major: repeated caveating, result excuses, omitted-experiment defense, promotional compensation.
  • Minor: local stylistic defensiveness.

Audit Procedure

Pass 1 — Detection

Mark D1–D14 patterns sentence by sentence.

Pass 2 — Necessity classification

Assign NECESSARY_CAVEAT / DEFENSIVE / MIXED / CLEAN.

Pass 3 — Reviewer reaction

For DEFENSIVE or MIXED cases, explain likely reviewer perception in one sentence.

Pass 4 — Rewrite

Rules:

  1. Put the empirical or technical fact first.
  2. Prefer positive scope statements.
  3. Keep at most one material caveat per claim.
  4. Replace adjectives with measurements.
  5. Describe controls rather than calling them fair.
  6. Do not explain weak results without evidence.
  7. Do not strengthen claims beyond the evidence.
  8. Preserve necessary uncertainty.
  9. Prefer deletion when a sentence adds no technical information.

Pass 5 — Paper-level audit

Check:

  • repeated limitations;
  • abstract disclaimers;
  • limitations inside contribution bullets;
  • reviewer-facing result interpretation;
  • rebuttal-like discussion;
  • self-weakening conclusion;
  • defended omissions;
  • vague praise;
  • repeated AI-style summary sentences.

Required Output Format

LocationOriginalTypeSeverityNecessary caveat?Why it feels defensiveLikely reviewer reactionRecommended actionRewrite

Then provide:

Paper-level defensive-writing score

0–10:

  • 0–2: clean
  • 3–4: mild
  • 5–6: noticeable
  • 7–8: strong
  • 9–10: rebuttal-like / disclaimer-heavy

Highest-priority removals

List the 3–10 edits most likely to improve reviewer perception.

Necessary caveats to preserve

List caveats that should not be deleted.

Repeated phrase audit

Count repeated constructions when the full paper is available.

Rewrite Style

Use:

  • compact academic English,
  • direct subject–verb structure,
  • measurable statements,
  • exact scope conditions,
  • neutral interpretation.

Do not replace defensive writing with hype.

Defense-Only Mode

When asked to inspect only defensive writing:

  • do not score novelty;
  • do not judge technical correctness except as needed to preserve caveats;
  • do not recommend extra experiments unless the text explicitly defends a missing one;
  • focus on framing, style, and claim discipline.

Full-Paper Cleanup Mode

When given a complete manuscript:

  1. Audit Abstract and Introduction first.
  2. Audit contribution bullets.
  3. Audit Results and Discussion.
  4. Audit Limitations for redundancy.
  5. Audit Conclusion for unnecessary self-weakening.
  6. Return a ranked edit list.
  7. If requested, provide a cleaned version preserving technical meaning.

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 Academic Defensive Writing Auditor AI skill do?

Detect and rewrite defensive, reviewer-facing, disclaimer-heavy academic prose while preserving necessary scientific caveats and evidence boundaries.

Why use Academic Defensive Writing Auditor on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Worigin0314/academic-defensive-writing-auditor/tree/main. 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 Academic Defensive Writing Auditor?

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 Academic Defensive Writing Auditor?

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

Is the Academic Defensive Writing Auditor AI skill free?

Yes. It is published on GitHub by Worigin0314 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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