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Agent Skill · 研究检索

skill-maker(技能制造者)

skill-maker 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。

skill-maker 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 59,803。

总安装 376周安装 16GitHub Stars 2下载 132核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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:skill-maker(技能制造者)
来源仓库:https://github.com/xbklairith/kisune
仓库路径:skills/skill-maker
安装命令:
npx skills add https://github.com/xbklairith/kisune --skill skill-maker
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/xbklairith/kisune --skill skill-maker

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

skill-maker 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 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。当前功能描述基于仓库公开信息,实际能力以源码和文档为准。

来源仓库内路径:skills/skill-maker

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/xbklairith/kisune --skill skill-maker;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

skill-maker 怎么安装?

在终端执行 npx skills add https://github.com/xbklairith/kisune --skill skill-maker。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

skill-maker 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

skill-maker 需要什么权限?

权限级别是需要联网。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。

skill-maker 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

skill-maker 用的人多吗?

公开数据是累计安装 376 次,近一周安装 16 次,下载 132 次,GitHub 2 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

skill-maker 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
skill-maker

技能制造者 · 本页

3762需要联网MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Skill Maker

Overview

Creating skills IS Test-Driven Development applied to process documentation.

Write test cases (pressure scenarios with subagents), watch them fail (baseline behavior), write the skill (documentation), watch tests pass (agents comply), and refactor (close loopholes).

Core principle: If you didn't watch an agent fail without the skill, you don't know if the skill teaches the right thing.

What is a Skill?

Skills are modular packages that extend Claude's capabilities with specialized knowledge, workflows, and tools — like "onboarding guides" for specific domains.

Skills provide: Specialized workflows, tool integrations, domain expertise, bundled resources (scripts, references, assets)

Skills are: Reusable techniques, patterns, tools, reference guides

Skills are NOT: Narratives, one-off solutions, or project-specific conventions (use CLAUDE.md for those)

Skill Types: Technique (concrete steps), Pattern (mental models), Reference (API docs/guides), Discipline-Enforcing (rules/requirements)

Create when: Technique wasn't obvious, applies broadly, would be referenced again, ensures consistent behavior across instances

Don't create for: One-off solutions, standard well-documented practices, project-specific conventions


Skill Anatomy

skill-name/
├── SKILL.md (required)
│   ├── YAML frontmatter: name (required), description (required), allowed-tools (optional)
│   └── Markdown instructions
└── Bundled Resources (optional)
    ├── scripts/     - Executable code (deterministic, token-efficient)
    ├── references/  - Documentation loaded as needed (schemas, APIs)
    └── assets/      - Files used in output (templates, boilerplate)

Frontmatter rules:

  • name: Letters, numbers, hyphens only (max 64 chars)
  • description: Third-person, starts with "Use when...", includes triggers AND purpose (max 1024 chars)
  • allowed-tools: Optional list restricting tool access

Writing style: Use imperative/infinitive form ("To accomplish X, do Y"), not second person

Progressive Disclosure: Metadata always loaded (~100 words) → SKILL.md on trigger (<5k words) → Resources as needed

Reference best practices: Keep SKILL.md lean; if reference files >10k words, include grep patterns in SKILL.md; avoid duplicating content between SKILL.md and references


The Iron Law

NO SKILL WITHOUT A FAILING TEST FIRST

Applies to NEW skills AND EDITS. Write/edit skill before testing? Delete it. Start over. No exceptions — not for "simple additions", "just a section", or "documentation updates". Delete means delete.


RED-GREEN-REFACTOR for Skills

RED: Baseline Testing

Run pressure scenarios WITHOUT the skill. Document exact behavior:

  • What choices did the agent make?
  • What rationalizations did they use (verbatim)?
  • Which pressures triggered violations?

Method: Create new conversation/subagent → present scenario → apply pressure (time constraints, sunk cost, authority, exhaustion) → document failures

GREEN: Write Minimal Skill

Write skill addressing those specific rationalizations. Don't add content for hypothetical cases.

Run same scenarios WITH skill present. Agent should now comply. Document any new violations.

REFACTOR: Close Loopholes

For each new violation: identify rationalization → add explicit counter → re-test until bulletproof.

For discipline-enforcing skills: Build rationalization table, create "Red Flags" list, add "No exceptions" counters, address "spirit vs letter" arguments.


Skill Creation Process

Step 1: Understanding

Clarify concrete examples of usage. Ask: "What should this skill support?", "What triggers it?", "How would it be used?"

UltraThink before creating structure:

"Let me ultrathink what this skill should really accomplish and how it fits the ecosystem."

Question fundamentals: Is this a skill or project docs? What's reusable vs one-off? How will Claude discover it? What rationalizations will agents use? What are we NOT including?

Step 2: Plan Resources

For each use case: How would you execute from scratch? What scripts, references, or assets would help when executing repeatedly?

Step 3: Initialize Structure

mkdir -p dev-workflow/skills/skill-name/{scripts,references,assets}

Create SKILL.md with frontmatter template:

---
name: skill-name
description: Use when [triggers] - [what it does]
allowed-tools: Read, Write, Edit, Glob, Grep
---

# Skill Name

## Overview
[Core principle in 1-2 sentences]

## When to Use
[Activation triggers; when NOT to use]

## [Main content sections]

Step 4: Execute RED-GREEN-REFACTOR

Follow the cycle above: baseline test (RED) → write content addressing failures (GREEN) → close loopholes (REFACTOR).

Skill content must answer: What is the purpose? When should it be used? How should Claude use it?

Step 5: Iterate After Deployment

Use skill on real tasks → notice struggles → baseline test again → update → verify.


Claude Search Optimization (CSO)

Description field: Start with "Use when...", use concrete triggers/symptoms, describe the *problem* not language-specific symptoms, write in third person.

Keywords: Include error messages, symptoms, synonyms, and tool names Claude would search for.

Naming: Use active voice, verb-first, gerunds work well (e.g., creating-skills, testing-async-code).

Token efficiency targets: Frequently-loaded skills <500 words, others <1000 words, getting-started <200 words. Move large content to references, cross-reference other skills, use one excellent example over many mediocre ones.


Testing by Skill Type

TypeTest WithSuccess Criteria
DisciplineAcademic questions + pressure scenarios (3+ combined: time, sunk cost, exhaustion)Agent follows rule under maximum pressure
TechniqueApplication scenarios, variations, missing info testsAgent applies technique to new scenario
PatternRecognition, application, counter-examplesAgent knows when/how to apply (and when NOT to)
ReferenceRetrieval, application, gap testingAgent finds and correctly applies information

Skill Creation Checklist

Use TodoWrite to create todos for EACH item.

RED Phase:

  • Create pressure scenarios (3+ combined pressures for discipline skills)
  • Run scenarios WITHOUT skill - document baseline verbatim
  • Identify patterns in rationalizations/failures

GREEN Phase:

  • Valid frontmatter: name (letters/numbers/hyphens), description ("Use when...", third person, <1024 chars)
  • Keywords throughout for search (errors, symptoms, tools)
  • Clear overview with core principle
  • Address specific baseline failures from RED
  • One excellent example (not multi-language)
  • Run scenarios WITH skill - verify compliance

REFACTOR Phase:

  • Add counters for new rationalizations
  • Build rationalization table and red flags list
  • Re-test until bulletproof

Quality Checks:

  • Overview answers: What? When? How?
  • Quick reference table or bullets
  • Common mistakes section
  • No narrative storytelling
  • Token count within targets

Deployment:

  • Commit skill to git
  • Update documentation if needed

Anti-Patterns

  • Narrative examples ("In session 2025-10-03...") — too specific, not reusable
  • Multi-language dilution (example-js, example-py, example-go) — mediocre quality, maintenance burden
  • Generic labels (helper1, step3) — use semantic names
  • Untested skills — guarantees issues in production
  • Vague descriptions ("Helps with coding") — Claude won't know when to activate

Integration with Dev-Workflow

Leverage existing skills: test-driven-development for testing, review for code review, documentation for docs, brainstorming for design exploration.

Activation context: Consider when skill activates in the workflow (planning, implementation, quality) and whether it auto-activates or requires explicit invocation.

Tool restrictions by phase:

  • Planning: Read, Write, Glob, Grep
  • Implementation: Read, Write, Edit, Glob, Grep, Bash
  • Quality: Read, Grep, Glob

The Bottom Line

Creating skills IS TDD for process documentation. Same Iron Law, same cycle (RED → GREEN → REFACTOR), same benefits. If you follow TDD for code, follow it for skills.

平台分布

Claude Code

28.16%
按下载量换算37

windsurf

23.85%
按下载量换算31

trae

21.02%
按下载量换算28

OpenCode

13.88%
按下载量换算18

Codex

8.42%
按下载量换算11

Antigravity

3.59%
按下载量换算5

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。

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