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Evidence Driven Writing

CommunityPopular
Norman-bury
evidence-driven-writing

Use when writing or revising Introduction, Related Work, background, literature synthesis, or any section where references must drive claims

Overview

PublisherNorman-bury
Repositoryresearch-writing-skill
Skill nameevidence-driven-writing
Stars
3.2K
Forks
214
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by Norman-bury on GitHub. Read the source before you install it.

Installation

Install the Evidence Driven Writing 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/Norman-bury/research-writing-skill.git /tmp/research-writing-skill
mkdir -p .claude/skills
cp -r /tmp/research-writing-skill/skills/evidence-driven-writing .claude/skills/evidence-driven-writing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evidence Driven Writing 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 Evidence Driven Writing 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 Evidence Driven Writing 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.

Evidence-Driven Writing

This skill turns a literature pool into manuscript argument. It is required for Introduction, Related Work, and any background section with citations.

Hard Gate

Do not write the section until an evidence map and paragraph blueprint exist.

Required artifacts:

  • refs/evidence-map.md or plan/evidence-map.md
  • plan/chapter-blueprints/<section>-blueprint.md
  • plan/review/evidence-coverage.md

Evidence Map

Create one row per usable source:

Source IDCitationSource typeAbstract-level findingUsable factSupported claimCitation slotRisk

Rules:

  • Use the user's literature pool first.
  • Use only information present in title, abstract, DOI metadata, or user-provided notes unless full text is available.
  • One citation must support one concrete claim, not a vague field statement.
  • Mark weak or indirect support as Risk: indirect.

Paragraph Blueprint

For each paragraph, define:

markdown
### Paragraph N
- Role: context / method landscape / limitation / gap / contribution
- Main claim:
- Evidence IDs:
- Contrast or transition:
- Forbidden content:

Only after the blueprint is complete may manuscript prose be written.

Introduction Pattern

A publishable technical Introduction usually follows this chain:

  1. Application context and why the problem matters, supported by foundational or survey citations.
  2. Existing technical routes, grouped by method family rather than by paper list.
  3. Bottleneck cascade: what each route solves and what remains unsolved.
  4. Specific research gap that the proposed method can realistically address.
  5. Contributions written as prose or a short list only if the target style allows it.

For computer science / engineering SCI drafts, integrate the most relevant related-work synthesis into the Introduction unless the locked outline explicitly requires a standalone Related Work chapter. The section must read as an argument: field pressure, existing routes, unresolved bottleneck, proposed position, and contribution boundary.

Do not end the Introduction with a long mechanical chapter map. One concise manuscript-organization paragraph is enough.

Related Work Pattern

Organize by theme, not by chronological paper dump:

  • Theme opening: define the method family or research stream.
  • Evidence synthesis: compare 2-4 sources in the same paragraph.
  • Critical boundary: state what the theme does not solve.
  • Bridge: explain why the next theme or the proposed method is needed.

Body Contamination Firewall

User requirements and process notes must not enter manuscript text. Phrases such as "write naturally", "avoid generic wording", "discussion prompt", "fill later", "user should replace", and "this section is a template" belong in plan files, never in chapters.

Compression is not deletion. When shortening, preserve the claim, evidence, method condition, and limitation.

Anti-Enumeration Gate

Literature-driven sections fail review if they read like a sequence of source notes. Each paragraph must synthesize at least two of the following:

  • a problem condition;
  • a method family;
  • a limitation shared by multiple studies;
  • a direct bridge to the proposed method;
  • a citation-backed boundary.

If a paragraph can be converted into a table row without losing meaning, it is not yet manuscript prose.

GPR Sample

For a reusable example of literature-driven argument structure extracted from the user's GPR passage, read references/gpr-introduction-example.md. Use it as a pattern, not as content for unrelated papers.

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 Evidence Driven Writing AI skill do?

Use when writing or revising Introduction, Related Work, background, literature synthesis, or any section where references must drive claims

Why use Evidence Driven Writing on TypingMind?

Because you install it once and use it with any model. Evidence Driven Writing 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 Evidence Driven Writing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Norman-bury/research-writing-skill/tree/main/skills/evidence-driven-writing. 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 Evidence Driven Writing?

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 Evidence Driven Writing?

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

Is the Evidence Driven Writing AI skill free?

Yes. It is published on GitHub by Norman-bury 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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