Token导航 LogoToken导航

Agent Skill · 研究检索

authoring-skills(创作技巧)

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

authoring-skills 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,权限为只读;许可证信息还不明确,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 60,316。

总安装 371周安装 15GitHub Stars 3下载 116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
unknown

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:authoring-skills(创作技巧)
来源仓库:https://github.com/lidessen/moniro
仓库路径:skills/authoring-skills
安装命令:
npx skills add https://github.com/lidessen/moniro --skill authoring-skills
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/lidessen/moniro --skill authoring-skills

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。

用于辅助安全审计、权限检查和常见漏洞排查。适用于 Codex、Claude、Cursor、Gemini CLI,通过 GitHub 安装。适合梳理敏感配置和生成安全复核清单。authoring-skills 属于研究检索类 Skill,可作为该场景下的辅助能力补充。使用时不能将工具输出直接作为最终结论。

涉及密钥或生产系统时应先确认最小权限和操作边界。

来源仓库内路径:skills/authoring-skills

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/lidessen/moniro --skill authoring-skills;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

authoring-skills 怎么安装?

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

authoring-skills 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。

authoring-skills 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

authoring-skills 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

authoring-skills 用的人多吗?

公开数据是累计安装 371 次,近一周安装 15 次,下载 116 次,GitHub 3 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。

authoring-skills 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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 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
authoring-skills

创作技巧 · 本页

3713只读未明确通过

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

Authoring Agent Skills

The Goal: Efficient Context Usage

Every skill you create will be loaded into Claude's limited context window. The fundamental challenge is:

Every unnecessary token wastes limited context space, slows processing, costs money, and reduces room for actual work.

Everything else—structure, naming, content—follows from this goal. Understanding *why* these patterns matter allows you to make good decisions in novel situations, rather than mechanically following rules.

Three Core Principles

1. Progressive Disclosure: Load Only What's Needed

The Intent: Don't force Claude to read 5000 lines when it needs 100.

Why It Matters: When a skill is triggered, SKILL.md loads entirely into context. If you put everything in one file, every query wastes tokens on irrelevant content. A finance query shouldn't load sales data. A basic usage shouldn't load advanced edge cases.

How to Apply:

  • Keep SKILL.md as a navigation hub (~500 lines)
  • Defer detailed content to reference files
  • Link directly from SKILL.md (avoid nested references that trigger partial reads)

The Trade-off: More files vs. larger context. Always choose more files.

See best-practices/progressive-disclosure.md for patterns.

2. Respect Intelligence: Assume Claude Is Smart

The Intent: Don't explain what Claude already knows.

Why It Matters: Explaining "PDF stands for Portable Document Format" wastes tokens and provides zero value. Claude understands file formats, programming concepts, standard tools, and industry patterns. Token space is precious—use it for your domain-specific knowledge, not computer science 101.

How to Apply:

  • Skip explanations of common concepts (file formats, imports, package managers)
  • Provide code and commands, not tutorials
  • Challenge each sentence: "Does Claude really need this?"

The Trade-off: Brevity vs. completeness. Trust Claude's knowledge.

See best-practices/conciseness.md for examples.

3. Enable Discovery: Make Skills Findable

The Intent: Help Claude choose the right skill from potentially 100+ options.

Why It Matters: A vague description means your skill won't trigger when needed. "Helps with documents" doesn't tell Claude when to use it. "Extract text from PDF and Word documents" with triggers like "PDF,.docx, extraction" ensures discovery.

How to Apply:

  • Write descriptions in third person (they're injected into system prompts)
  • Include specific capabilities (WHAT) and trigger scenarios (WHEN)
  • Use concrete terms: file types, technology names, activity phrases

The Trade-off: Specificity vs. flexibility. Be specific about triggers.

See best-practices/description.md for examples.

Quick Start Workflow

When creating a skill, apply these principles:

  1. Understand the goal - What problem does this solve? Who will use it?
  2. Draft metadata - Name (lowercase-with-hyphens) and description (WHAT + WHEN)
  3. Design for disclosure - What goes in SKILL.md vs. reference files?
  4. Write concisely - Assume Claude is smart. Skip the obvious.
  5. Test with sub-agent - Dry-run discovery, execution, and boundary tests (see Testing)

Essential Constraints

Some requirements exist for technical reasons:

Name Format

  • Lowercase letters, numbers, hyphens only (parsing requirement)
  • Max 64 characters (system limit)
  • No XML tags, no "anthropic" or "claude" (reserved terms)
  • Prefer gerund form: processing-pdfs, analyzing-data (consistency)

Description Format

  • Max 1024 characters (system limit)
  • Third person: "Processes files..." not "I process..." (system prompt injection)
  • Include capabilities + triggers (discovery mechanism)

Good example:

description: Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.

File Organization

  • Forward slashes in paths (cross-platform compatibility)
  • One-level references from SKILL.md (avoid partial reads)
  • Reference files >100 lines need TOC (enables preview scanning)

Common Patterns

These patterns emerge from the core principles:

  1. Template Pattern: Provide output format templates when consistency matters
  2. Workflow Pattern: Multi-step checklists for complex processes
  3. Feedback Loop: Validate → fix → repeat for quality-critical tasks
  4. Conditional Pattern: Decision branches for context-dependent logic

See patterns/ for detailed examples.

Degrees of Freedom

Match specificity to task fragility:

  • High freedom (instructions): Multiple valid approaches, Claude chooses based on context
  • Medium freedom (templates): Preferred pattern, acceptable variation
  • Low freedom (scripts): Fragile operations requiring exact execution

*Why this matters*: Over-specifying simple tasks wastes tokens. Under-specifying fragile tasks causes errors.

See best-practices/degrees-of-freedom.md.

Understanding Anti-Patterns

Rather than memorizing rules, understand *why* certain patterns fail:

  • Windows paths (\) → Parser must handle both styles, adds cognitive load
  • Nested references → Triggers partial reads, loses information
  • Time-based conditions → Becomes wrong after dates pass
  • Vague names → Poor discoverability, unclear purpose

The pattern: They all violate efficient context usage or discoverability.

See anti-patterns.md for detailed explanations.

Quality Self-Check

Before finalizing, ask yourself:

Core Principles:

  • Does this minimize context usage? (Progressive disclosure)
  • Am I explaining things Claude knows? (Respect intelligence)
  • Will Claude discover this skill correctly? (Enable discovery)

Technical Constraints:

  • Name/description follow format requirements?
  • SKILL.md under 500 lines?
  • References one level deep?
  • All paths use forward slashes?

Content Quality:

  • Consistent terminology?
  • Concrete examples over abstract descriptions?
  • No time-sensitive information?

Testing with Sub-Agent

Use Task tool to dry-run test your skill before finalizing.

1. Discovery Test

Verify the description triggers correctly:

Task tool → Explore agent:
"Given this user request: '[typical trigger phrase]',
which skill would you recommend and why?"

Pass criteria: Agent identifies your skill for the right scenarios.

2. Execution Test

Verify the skill content is actionable:

Task tool → General-purpose agent:
"Using the [skill-name] skill located at [path],
perform this task: [realistic task]"

Pass criteria: Agent follows the workflow without confusion or asking for clarification on skill instructions.

3. Boundary Test

Verify the skill doesn't over-trigger:

Task tool → Explore agent:
"Given this user request: '[edge case that should NOT trigger]',
which skill would you recommend?"

Pass criteria: Agent recommends a different skill or no skill.

Quick Test Checklist

TestPrompt ExampleExpected
Discovery"How do I [trigger phrase]?"Recommends this skill
Execution"[Realistic task]"Completes using skill workflow
Boundary"[Similar but different task]"Recommends other skill

Storage Locations

Determine location based on your environment:

  1. Check existing setup - Look for existing skill directories:

- Cursor: ~/.cursor/skills/ (personal) or .cursor/skills/ (project) - Codex/Claude: $CODEX_HOME/skills/ (personal) or project-specific location - Other tools: Check tool-specific documentation

  1. If no existing setup, use standard locations:

- Project-level: .agents/skills/ (standard Agent Skills format) - Personal: ~/.agents/skills/ or tool-specific home directory

  1. For project instructions (not skills), use AGENTS.md in project root

Important:

  • Never use ~/.cursor/skills-cursor/ (reserved for Cursor built-ins)
  • Prefer project-level storage for team collaboration

Learning Resources

Start Here:

Core Philosophy:

Practical Patterns:

Examples:

Reference:

平台分布

Codex

36.06%
按下载量换算42

Claude

28.31%
按下载量换算33

Cursor

21.59%
按下载量换算25

Gemini CLI

9.91%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills