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Agent Skill · 开发

skill-creator(技能创建器)

skill-creator 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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-creator 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 9,319。

总安装 2,766周安装 113GitHub Stars 1,382下载 895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:skill-creator(技能创建器)
来源仓库:https://github.com/himself65/finance-skills
仓库路径:skills/skill-creator
安装命令:
npx skills add https://github.com/himself65/finance-skills --skill skill-creator
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/himself65/finance-skills --skill skill-creator

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。适合在需要围绕仓库状态或代码变更进行整理时使用。可结合来源仓库和原始 README 核验具体用法。安装前建议确认权限范围和维护状态。skill-creator 属于开发类 Skill,可作为该场景下的辅助能力补充。

注意是否会触发联网、命令执行或文件读写。

来源仓库内路径:skills/skill-creator

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/himself65/finance-skills --skill skill-creator;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

    它需要执行命令,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-creator 怎么安装?

在终端执行 npx skills add https://github.com/himself65/finance-skills --skill skill-creator。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

skill-creator 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-creator 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/himself65/finance-skills --skill skill-creator 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

skill-creator 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。

skill-creator 用的人多吗?

公开数据是累计安装 2,766 次,近一周安装 113 次,下载 895 次,GitHub 1,382 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-creator 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。
  • 来源仓库有 1,382 星,维护活跃度相对可查。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
skill-creator

技能创建器 · 本页

2,7661,382执行命令未明确有提醒

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 Creator

Create, evaluate, and iterate on high-quality agent skills. This skill guides the entire lifecycle: planning what the skill should do, writing SKILL.md and reference files, scoring quality against a rubric, and iterating until the skill meets production standards.

Philosophy: A great skill is not a long skill. It is a *precise* skill: exhaustive triggers, explicit defaults, clear steps with exit gates, deferred complexity via reference files, and a structured output template.

Core rule — always dynamic, never static: Skills MUST detect what tools, libraries, and auth are available at runtime and adapt their behavior accordingly. Never hardcode a single method. Always provide a detection flow with a decision tree and fallback paths. See references/dynamic-calling.md for the complete pattern catalog.


Step 1: Understand What the User Wants

Classify the request into one of these modes:

User IntentModeJump To
Create a brand-new skillCreateStep 2
Improve / fix an existing skillImproveStep 6
Evaluate / score a skill's qualityEvaluateStep 7

If ambiguous, ask: "Do you want to create a new skill, improve an existing one, or evaluate one?"

Gather Requirements (for Create mode)

Before writing anything, answer these questions (ask the user if unclear):

QuestionWhy it matters
What task does the skill automate?Defines the core workflow
Who is the target user?Determines complexity and terminology level
What tools/APIs/CLIs does it use?Determines dependencies and platform restrictions
What does the user provide as input?Defines parameters and defaults
What should the output look like?Defines the response template
Does it need API keys or credentials?Determines required_environment_variables
Should it work on Claude.ai or only CLI?Determines platform field and dynamic commands

Step 2: Plan the Skill Architecture

Before writing SKILL.md, plan the structure. Read references/architecture-patterns.md for detailed guidance on each pattern.

Choose a Structural Pattern

PatternWhen to useStepsExample
LinearSingle workflow, no branching5-7earnings-preview, etf-premium
RouterMultiple sub-tasks under one umbrella3 + sub-skillsstock-correlation (4 sub-skills)
MethodologyComplex domain framework with sequential gates7-9sepa-strategy (9-step trading methodology)
WidgetGenerates interactive UI output4-5options-payoff (extract + compute + render)
API WrapperWraps an external API with many endpoints3-5 + heavy referencesfunda-data (5 steps, 8 reference files)

Plan the Step Outline

Write out the step names before writing content. Every skill should have:

  1. Detection flow (Step 1) -- dynamically detect available tools, auth state, and runtime environment; build a decision tree for which method to use
  2. Core methodology (Steps 2-N) -- the actual work, with pass/fail gates; each step that calls an external tool should have method alternatives based on what Step 1 detected
  3. Respond to user (Final step) -- structured output template

Target 5-9 steps total. More than 9 means the skill should be split or use a router pattern.

Plan the Detection Flow

Every skill that touches external tools MUST start with a runtime detection flow. Read references/dynamic-calling.md for all patterns. The detection flow answers:

QuestionHow to detectDecision
Is the CLI tool installed?command -v toolCLI path vs Python fallback
Is the user authenticated?tool auth status / echo $API_KEYSkip auth setup vs guide through it
Which runtime has the library?import lib in terminal vs execute_codeRoute to correct runtime
Is a richer tool available?gh --version vs git --versionRich path vs minimal path
Is live data reachable?curl -s endpointLive data vs cached/default

The detection output feeds into a decision tree that the rest of the skill follows. Never assume — always check.

Plan Reference Files

Decide what goes in SKILL.md vs references/:

In SKILL.md (under ~250 lines)In references/
Step-by-step workflowDetailed API documentation
Routing/decision tablesCode templates (>20 lines)
Parameter defaults tableFormulas and edge cases
Output format templateTroubleshooting database
Quick examples (1-3)Comprehensive examples (4+)

Step 3: Write the SKILL.md

Read references/writing-guide.md for detailed instructions on writing each section. Read references/frontmatter-guide.md for the complete YAML field reference.

Key Rules

  1. Frontmatter first: name (lowercase-hyphenated, max 64 chars) and description (exhaustive trigger list, max 1024 chars) are required. Description needs 5+ triggers including sideways entry points.
  2. Step 1 = detection flow: Use !command` with fallbacks to detect available tools, auth state, and runtime. Build a decision tree with multiple method paths (e.g., CLI preferred, Python fallback, built-in tools last resort). Never hardcode a single tool — always detect and adapt. See references/dynamic-calling.md`.
  3. Core steps with method alternatives: Each step that calls an external tool should offer at least 2 paths based on what Step 1 detected. Use pattern: "If TOOL_A detected → Method 1, otherwise → Method 2." Each step gets ## Step N: [Verb] [Object], a decision table if routing, a pass/fail gate if evaluative, and a reference pointer for deep content.
  4. Defaults table: Every parameter MUST have an explicit default. No skill should ever stall waiting for input.
  5. Final step = output template: Number every output section. Specify exactly what data goes in each. Include a verdict/grade system if evaluative.

See references/skill-examples.md for annotated examples of each pattern.


Step 4: Write Reference Files

Read references/writing-guide.md for the full reference file authoring guide.

Key Rules

  1. Naming: lowercase-hyphenated.md, one file per concept-cluster
  2. Size: Quick lookup 50-150 lines, deep guide 150-400 lines, catalog 400-900 lines
  3. Structure: H1 title, H2 sections, code blocks, tables, edge cases section at end
  4. Linking: Use backtick paths in SKILL.md steps and a ## Reference Files section at the end

Step 5: Quality Check Before Delivery

Run the skill through the quality rubric in references/quality-rubric.md. Score each dimension.

Quick Checklist

  • Frontmatter has name and description (both required)
  • Description has 5+ distinct trigger phrases
  • Description includes sideways entry points
  • SKILL.md is under 300 lines (ideally under 250)
  • Every parameter has an explicit default
  • Steps are numbered (## Step N:...)
  • Each step has a clear exit condition or deliverable
  • Final step specifies exact output structure with numbered sections
  • Complex content is in reference files, not inline
  • Reference file pointers use backtick paths
  • Step 1 has a detection flow with !command` checks and fallbacks (|| echo "..."`)
  • Detection flow produces a decision tree with 2+ method paths
  • Core steps adapt behavior based on detection results (not hardcoded to one tool)
  • Separate runtimes treated as separate environments (terminal vs execute_code)
  • Legal/ethical disclaimers included where appropriate
  • No hardcoded ticker lists, tool paths, or static data that will go stale

If any item fails, fix it before delivering to the user.


Step 6: Improve an Existing Skill

When the user asks to improve a skill:

6a: Read the Current Skill

Load the skill with skill_view(name) or read the SKILL.md directly. Also read all reference files.

6b: Score It Against the Rubric

Use the quality rubric from references/quality-rubric.md. Present the score breakdown to the user:

DimensionScoreIssue
Trigger quality6/10Missing beginner phrasing
Defaults coverage3/10No defaults table
Step structure8/10Good, but Step 3 lacks exit gate
Output template4/10Vague "summarize results"
Reference usage7/10Good split, but missing troubleshooting

6c: Propose Specific Improvements

List concrete changes ranked by impact:

  1. [Highest impact] Add defaults table with 8+ parameters
  2. [High impact] Rewrite description with 10+ trigger phrases
  3. [Medium impact] Add structured output template to final step
  4. ...

6d: Apply Changes

After user approval, edit the skill. Use skill_manage(action='patch',...) for targeted changes or skill_manage(action='edit',...) for full rewrites.


Step 7: Evaluate a Skill

When the user asks to evaluate or score a skill:

7a: Load and Analyze

Read the full SKILL.md and all reference files. Count lines, steps, triggers, defaults, reference files.

7b: Score Against Rubric

Use the comprehensive rubric from references/quality-rubric.md. Score each of the 10 dimensions on a 1-10 scale.

7c: Present the Scorecard

## Skill Quality Scorecard: [skill-name]

| # | Dimension | Score | Notes |
|---|---|---|---|
| 1 | Trigger quality | 8/10 | 12 triggers, includes sideways entries |
| 2 | Defaults coverage | 9/10 | All 11 parameters have defaults |
| 3 | Step architecture | 8/10 | 5 clear steps with gates |
| 4 | Reference file strategy | 7/10 | 2 files, could use troubleshooting |
| 5 | Dynamic content | 10/10 | Dep check + live data injection |
| 6 | Output template | 9/10 | 5 numbered sections + verdict |
| 7 | Error handling | 6/10 | Missing data handling unclear |
| 8 | Code/formula quality | 8/10 | Working JS, copy-paste ready |
| 9 | SKILL.md conciseness | 7/10 | 196 lines, well within target |
| 10 | Domain accuracy | 9/10 | BS formulas correct, edge cases covered |

**Overall: 81/100** -- Production quality

### Top 3 Improvements
1. ...
2. ...
3. ...

Benchmark Reference

For context, here are scores for known high-quality skills in this repo:

SkillScoreWhy
sepa-strategy~90/1009 steps, 7 refs, exhaustive triggers, structured verdict
options-payoff~85/100Strong defaults, working code, live data, clean output
stock-correlation~80/100Router pattern, 4 sub-skills, good defaults

Step 8: Respond to the User

For Create mode

Deliver:

  1. The complete SKILL.md content
  2. All reference files
  3. A README.md for the skill directory
  4. The quality scorecard (from Step 5)
  5. Suggested next steps (test it, iterate, publish)

For Improve mode

Deliver:

  1. Before/after quality scores
  2. Summary of changes made
  3. Remaining improvement opportunities

For Evaluate mode

Deliver:

  1. The full quality scorecard
  2. Comparison to benchmark skills
  3. Prioritized improvement list

Reference Files

  • references/dynamic-calling.md -- Core reference: Detection flows, decision trees, method fallbacks, runtime awareness, and multi-tool adaptation patterns with annotated examples from production skills
  • references/writing-guide.md -- Detailed instructions for writing SKILL.md sections, environment checks, defaults tables, output templates, and reference files
  • references/architecture-patterns.md -- Linear, Router, Methodology, Widget, and API Wrapper patterns with examples and anti-patterns
  • references/frontmatter-guide.md -- Complete YAML frontmatter field reference (name, description, platform, env vars, config, credentials)
  • references/quality-rubric.md -- 10-dimension scoring rubric with 1-10 scales, benchmark scores, and score interpretation
  • references/skill-examples.md -- Annotated excerpts from top skills showing why specific patterns work

平台分布

Codex

37.44%
按下载量换算335

Claude

31.33%
按下载量换算280

Cursor

18.93%
按下载量换算169

Gemini CLI

9.32%
按下载量换算83

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/himself65/finance-skills --skill skill-creator 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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