concept-clarifier

concept-clarifier is a skill for Claude Code, Codex from yipng05-max/-skills. It costs 170 tokens per session (2,230 once invoked), scanned A, original, MIT.

A concept-analysis method for explaining difficult social-science terms and distinguishing similar concepts. It examines where a concept came from, what it assumes, how it is used, and where its boundaries lie.

In plain words
What is it for?
Use it to define one concept, compare several concepts, or decide whether a concept can support a study. It can help with theoretical framing and turning abstract ideas into research categories.
Why use it?
It helps prevent researchers from treating related terms as interchangeable when they carry different theories or meanings. It also shows whether a concept fits a particular research context.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to define one concept, compare several concepts, or decide whether a concept can support a study. It can help with theoretical framing and turning abstract ideas into research categories.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yipng05-max/-skills/concept-clarifier
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add yipng05-max/-skills --skill concept-clarifier
Clone the repo
git clone --depth 1 https://github.com/yipng05-max/-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for concept-clarifier

README.md
[![agentmods](https://agentmods.dev/badge/skills/yipng05-max/-skills/concept-clarifier/github.svg)](https://agentmods.dev/skills/yipng05-max/-skills/concept-clarifier)
Your own site
<a href="https://agentmods.dev/skills/yipng05-max/-skills/concept-clarifier"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/concept-clarifier/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for concept-clarifier

Your own site · 80×15
<a href="https://agentmods.dev/skills/yipng05-max/-skills/concept-clarifier"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/concept-clarifier.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 170 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,230 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00170 $0.02230
Opus 5 $0.00085 $0.01115
Sonnet 5 $0.00034 $0.00446
Haiku 4.5 $0.00017 $0.00223

Measured 9d ago against content hash 86f2159f7555, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

concept-clarifier scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

concept-clarifier/SKILL.md · 210 lines

How it starts

The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.

概念辨析工具(Concept Clarifier)

本 skill 基于社会学概念分析的经典传统(Sartori 的梯子逻辑、Gerring 的概念评估标准), 协助研究者理解概念的理论谱系、内部张力与使用边界,防止概念混用和标签化使用。

核心立场:概念不是中性的描述工具,而是理论的凝结物。 使用一个概念,就是接受它背后的理论预设。不了解预设,就无法判断概念是否适用。


启动:获取必要信息

触发后,收集以下信息:

必填 1:需要辨析的概念(一个或多个)

单个概念(深度解析)或多个概念(比较辨析),例如:

  • 单个:"请帮我深度解析'制度逻辑'这个概念"
  • 多个:"'话语''叙事''框架'这三个概念有什么实质区别?"
  • 应用场景:"我想在研究中用'监控'这个概念,但不确定是福柯的监控还是Zuboff的监控资本主义"

选填 2:使用场景

用户打算在什么研究情境中使用这些概念?有助于评估适用性。

选填 3:已有认识

用户对这些概念已有的理解(方便识别认知误区)。


执行流程

根据输入类型,选择对应执行路径:

  • 单概念:执行"深度解析模式"
  • 多概念比较:执行"比较辨析模式"
  • 应用场景判断:执行"适用性评估模式"

三种模式可以组合,但优先完成用户最需要的部分。


模式一:深度解析模式(单概念)

对单个概念进行完整的理论解剖,按以下七个维度展开:

维度 1:概念的来源与原初语境

  • 这个概念由谁提出?在什么学术语境中发展起来?
  • 提出者试图用这个概念解决什么理论问题?
  • 原初语境中,这个概念的"对立面"是什么?(概念往往通过与对立物的区分来定义自身)

说明为什么这很重要:同一个词在不同作者笔下可能指称完全不同的机制。 例如"场域"在布迪厄那里是关系性的竞争空间,在某些中国研究中却被用作"领域"的同义词。

维度 2:核心定义与关键要素

  • 最严格的定义是什么?(引用原著表述,而非教科书简化版)
  • 这个定义包含哪些不可缺少的要素?
  • 哪些用法是对原始定义的过度简化或错误延伸?

维度 3:概念的理论预设

这个概念背后预设了什么?包括:

  • 本体论预设:这个概念假设什么样的社会实在?(个体主义 vs 整体主义?实体 vs 关系?)
  • 认识论预设:使用这个概念隐含着什么认识论立场?
  • 机制预设:这个概念隐含了什么因果或解释机制?

接受一个概念,就是接受这些预设。如果研究者的认识论立场与概念预设不兼容,使用该概念会产生内在矛盾。

维度 4:概念的演变与争议

  • 这个概念如何在不同作者、不同传统中被修正、扩展或批判?
  • 目前学界围绕这个概念有哪些主要争议?
  • 哪些修正版本最具代表性?与原始版本的核心差异是什么?

维度 5:边界条件

  • 这个概念在什么情境下成立?在什么情境下失效?
  • 哪些案例或现象构成该概念的边界挑战?
  • 是否存在"貌似符合但实际不符合"的常见误用情境?

维度 6:操作化指引

  • 在实证研究中,这个概念通常通过什么方式观察或测量?
  • 定性研究中,什么样的资料(访谈话语/观察记录/文本/行动)构成该概念的经验证据?
  • 操作化时最容易犯的错误是什么?

维度 7:近似概念对比

  • 有哪些概念经常与之混用?
  • 这些概念与本概念的关键区分在哪里?
  • 用一个判断规则帮助研究者在实际使用中区分:

    "当你的分析关注 [X] 时用 [概念A];当关注 [Y] 时用 [概念B]。"

输出格式(深度解析模式):

【概念深度解析:[概念名称]】

来源与原初语境:
核心定义(原著表述):
关键要素(不可缺少的部分):
理论预设(本体论/认识论/机制):
主要演变与争议:
边界条件:
操作化指引:
与近似概念的区分:

模式二:比较辨析模式(多概念)

对两个或多个概念进行系统比较,重点在于实质差异而非表面差异:

步骤 1:各概念的简要定位

每个概念用 3-5 句话定位其核心内涵和理论来源(简版)。

步骤 2:逐维度比较

比较维度 概念 A 概念 B 概念 C(如有)
理论传统
分析单位
核心机制
本体论预设
典型应用场景
能否替换使用

步骤 3:判断规则

给出清晰的使用判断规则:

"当你的研究问题关注 [X 类型的现象/机制/关系] 时,应使用 [概念A]; 当关注 [Y 类型] 时,应使用 [概念B]; 当两者兼有时,需要说明……"

Read the full file on GitHub · 210 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 210 lines · 170 tokens per session scan A 86f2159f7555

Subscribe to this mod's changes

concept-clarifier is a skill published in the GitHub repository yipng05-max/-skills (285 stars, last pushed 4mo ago), licensed MIT. It adds 170 tokens to every session and 2,230 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens