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terminology-normalizer术语规范化器

Agent Skill

terminology-normalizer 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

906

周安装

37

GitHub Stars

422

下载量

293
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:terminology-normalizer(术语规范化器)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/terminology-normalizer
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill terminology-normalizer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill terminology-normalizer

简介

用于查找、检索和筛选相关信息,支持术语标准化相关任务。

  • 适合在需要根据关键词或领域知识快速定位候选术语时使用。
  • 使用时应结合具体上下文验证准确性,避免直接采用未经验证的输出。
  • 安装方式:通过 npx 从研究单元管道仓库添加,注意权限与维护状态。
  • terminology-normalizer 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Terminology Normalizer

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)

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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

26.11%
按下载量换算77

Gemini CLI

24.84%
按下载量换算73

OpenCode

16.49%
按下载量换算48

Cursor

13.44%
按下载量换算39

Antigravity

8.13%
按下载量换算24

Codex

3.12%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

来源信息

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