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Results Section Revision

Community
Boom5426
results-section-revision

Use when revising a scientific manuscript Results section whose figures, evidence, and main claims are mostly fixed, but subsection flow, paragraph openings, reader guidance, and argumentative progression still feel abrupt, repetitive, or figure-by-figure.

Overview

PublisherBoom5426
RepositoryNature-Paper-Skills
Skill nameresults-section-revision
Stars
570
Forks
48
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 Boom5426 on GitHub. Read the source before you install it.

Installation

Install the Results Section Revision 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/Boom5426/Nature-Paper-Skills/tree/main/skills/core/results-section-revision
  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/Boom5426/Nature-Paper-Skills.git /tmp/Nature-Paper-Skills
mkdir -p .claude/skills
cp -r /tmp/Nature-Paper-Skills/skills/core/results-section-revision .claude/skills/results-section-revision
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Results Section Revision 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 Results Section Revision 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 Results Section Revision 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.

Results Section Revision

Overview

Use this skill for late-stage Results revision when the science is mostly stable but the writing architecture is not. It is narrower than scientific-writing and manuscript-optimizer: the job here is to repair subsection titles, bridge paragraphs, paragraph openings, and local argumentative flow.

Use scientific-writing for general prose drafting or rewriting. Use manuscript-optimizer when the claim hierarchy, evidence chain, or figure logic is still unstable. Use this skill when the section is largely right but still reads like stitched figure captions rather than a controlled argument. Run anti-defensive-writing after it when Results carries audit, defensive or self-critical writing.

Quick Checks

Before revising any Results subsection, check:

  1. Is the title selling the conclusion rather than the method?
  2. Does the first sentence of each paragraph state that paragraph's point?
  3. Are openings like we asked, we examined, or to test this overused?
  4. Are abstract words masking a more specific noun?
  5. Is the relationship to the previous paragraph explicit?
  6. Is the paragraph only reporting a result, or advancing the argument?
  7. Does the paragraph close by stating what changes in interpretation?
  8. Does the subsection end in a way that naturally leads into the next one?
  9. Are the first and last sentences of each subsection written for a broad peer, stating the question and what the result means in the reader's terms, while the body carries the technical detail? See the Readers section of paper-workflow's references/section-contracts.md.

Subsection Pattern

Each Results subsection should answer two reader questions in order:

  1. Why is this analysis needed now?
  2. What does it establish?

If the second question appears before the first, the subsection will feel abrupt.

The first paragraph should bridge from the previous subsection and state why the next analysis is necessary. Later paragraphs should each do one job: sharpen a finding, separate related findings, show a consequence for evaluation or prioritization, or explain what a comparison actually distinguishes.

Paragraph Openings

Prefer openings that either orient the reader or state a concrete result:

  • The molecular branch first showed ...
  • The same pattern was also evident in ...
  • A further question was whether ...
  • Cross-representation analyses showed ...

Avoid repeated procedural scaffolds:

  • To test this ...
  • We next asked ...
  • We then examined ...

At the start of a new paragraph, avoid vague pronouns unless the referent is unmistakable. Replace this structure or that mismatch with the explicit noun phrase.

Style Rules

  • One paragraph, one job.
  • Use explicit contrasts early.
  • Surface key quantitative anchors before interpretation.
  • Keep the register controlled, direct, and specific.
  • End each subsection with a sentence that changes the reader's expectation for the next subsection.

Common Failure Modes

  • method-like subsection titles
  • first paragraphs that announce a result without a rationale
  • several paragraphs in a row with the same procedural opening
  • abstract nouns that blur what is actually being compared
  • summary sentences that restate the result but not its implication
  • provenance qualifiers inside Results ("post hoc", "added after the original evaluation", "locked")
  • implementation checks reported as findings ("verified", "confirmed to be orthogonal")
  • setup, safeguard, caveat, control and exception chained before the result; state the setup in one sentence, then the result

Frequently asked questions

What does the Results Section Revision AI skill do?

Use when revising a scientific manuscript Results section whose figures, evidence, and main claims are mostly fixed, but subsection flow, paragraph openings, reader guidance, and argumentative progression still feel abrupt, repetitive, or figure-by-figure.

Why use Results Section Revision on TypingMind?

Because you install it once and use it with any model. Results Section Revision 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 Results Section Revision in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Boom5426/Nature-Paper-Skills/tree/main/skills/core/results-section-revision. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Results Section Revision?

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 Results Section Revision?

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

Is the Results Section Revision AI skill free?

Yes. It is published on GitHub by Boom5426 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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