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Terminology Normalizer

Community
WILLOSCAR
terminology-normalizer

Normalize terminology across a draft (canonical terms + synonym policy) without changing citations or meaning.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill nameterminology-normalizer
Stars
513
Forks
39
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 WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Terminology Normalizer 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/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/terminology-normalizer
  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/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/terminology-normalizer .claude/skills/terminology-normalizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Terminology Normalizer 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 Terminology Normalizer 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 Terminology Normalizer 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.

Terminology Normalizer

Triggers & routing

  • Trigger: terminology, glossary, consistent terms, 术语统一, 统一叫法, 术语表.
  • Use when: the draft has concept drift (same thing called 2–3 names) or global-review flags terminology inconsistency.

Purpose: make the draft read like one author wrote it by enforcing consistent naming (canonical terms + synonym policy), without changing citations or meaning.

Role cards (use explicitly)

Taxonomist (canonicalizer)

Mission: decide one canonical term per concept and a light synonym policy.

Do:

  • Prefer taxonomy node names (outline/taxonomy.yml) as canonical labels when available.
  • Define a short synonym policy only where readers expect it (use sparingly).
  • Keep headings and tables aligned with canonical terms.

Avoid:

  • Renaming proper nouns (paper titles, benchmark names, model names).
  • Over-normalizing away meaningful distinctions (e.g., collapsing two different mechanisms into one word).

Integrator (apply without drift)

Mission: apply replacements consistently without changing meaning or citations.

Do:

  • Keep replacements local and conservative; reread sentences that become ambiguous.
  • Preserve citation placement and subsection boundaries.

Avoid:

  • Introducing new claims while rewriting for terminology.
  • Moving citations across subsections.

Role prompt: Terminology Editor (one voice)

text
You are normalizing terminology in a technical survey draft.

Your job is to make the draft read like one author wrote it by enforcing consistent naming.

Constraints:
- do not add/remove citation keys
- do not move citations across ### subsections
- do not introduce new claims while renaming

Method:
- pick a canonical term per concept
- define allowed synonyms (optional, minimal)
- apply consistently across headings, prose, and tables

Inputs

  • output/DRAFT.md
  • Optional (read-only context):
    • outline/outline.yml (heading consistency)
    • outline/taxonomy.yml (canonical labels)

Outputs

  • output/DRAFT.md (in place)
  • Optional: output/GLOSSARY.md (short appendix/glossary table, if useful)

Workflow

Use the role cards above.

Steps:

  1. Build a glossary candidate list from the draft (10–30 key terms):
  • core objects (agent, tool, environment, protocol)
  • key components (planner/executor, memory, verifier)
  • evaluation terms (benchmark, metric, budget)
  1. Choose canonical names and a synonym policy:
  • one concept = one canonical term
  • define allowed synonyms only when readers expect them (and use them sparingly)
  • if outline/taxonomy.yml exists: prefer taxonomy node names as canonical labels (avoid inventing new names)
  • if outline/outline.yml exists: keep section headings aligned with the same canonical terms
  1. Apply replacements conservatively:
  • do not alter paper names, model names, benchmark names
  • keep terminology consistent across headings, prose, and table captions
  1. Optional: write a small output/GLOSSARY.md:
  • term | canonical | allowed synonyms | notes

Mini examples (what to do / what to avoid)

  • Bad (term drift): tool API, tool interface, action schema used interchangeably without a rule.

  • Better (canonical + light synonym policy): pick one canonical term (e.g., tool interface) and allow one synonym only when first introduced (e.g., tool interface (API contract)), then stick to canonical thereafter.

  • Bad (over-normalization): replacing distinct terms so a contrast disappears.

  • Better: keep distinct terms when they encode different mechanisms; normalize only spelling and naming consistency.

Guardrails (do not violate)

  • Do not add/remove citation keys.
  • Do not move citations across ### subsections.
  • Do not introduce new claims while renaming.

Troubleshooting

Issue: normalization changes citation keys or moves citations

Fix:

  • Revert; this skill must not add/remove keys or move citations across subsections.

Issue: synonyms policy is unclear

Fix:

  • Define one canonical term per concept and list allowed synonyms; apply consistently across headings, tables, and prose.

Frequently asked questions

What does the Terminology Normalizer AI skill do?

Normalize terminology across a draft (canonical terms + synonym policy) without changing citations or meaning.

Why use Terminology Normalizer on TypingMind?

Because you install it once and use it with any model. Terminology Normalizer 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 Terminology Normalizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/terminology-normalizer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Terminology Normalizer?

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 Terminology Normalizer?

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

Is the Terminology Normalizer AI skill free?

It is published on GitHub by WILLOSCAR. 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.

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