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skills-eval技能评估

Agent Skill

skills-eval 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

1,067

周安装

44

GitHub Stars

264

下载量

348
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:skills-eval(技能评估)
来源仓库:https://github.com/athola/claude-night-market
仓库路径:skills/skills-eval
安装命令:
npx skills add https://github.com/athola/claude-night-market --skill skills-eval
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/athola/claude-night-market --skill skills-eval

简介

用于查找、检索和筛选相关信息。

  • 适合在技能评估或性能分析场景中使用。
  • 可通过来源仓库和 README 核验具体用法。
  • 安装命令:npx skills add https://github.com/athola/claude-night-market --skill skills-eval。
  • 建议确认权限范围及是否会触发联网或文件读写。

SKILL.md

Skills Evaluation and Improvement

Table of Contents

  1. Overview
  2. Quick Start
  3. Evaluation Workflow
  4. Evaluation and Optimization
  5. Resources

Overview

This framework audits Claude skills against quality standards to improve performance and reduce token consumption. Automated tools analyze skill structure, measure context usage, and identify specific technical improvements. Run verification commands after each audit to confirm fixes work correctly.

The skills-auditor provides structural analysis, while the improvement-suggester ranks fixes by impact. Compliance is verified through the compliance-checker. Runtime efficiency is monitored by tool-performance-analyzer and token-usage-tracker.

Quick Start

Basic Audit

Run a full audit of all skills or target a specific file to identify structural issues.

# Audit all skills
make audit-all

# Audit specific skill
make audit-skill TARGET=path/to/skill/SKILL.md

Analysis and Optimization

Use skill_analyzer.py for complexity checks and token_estimator.py to verify the context budget.

make analyze-skill TARGET=path/to/skill/SKILL.md
make estimate-tokens TARGET=path/to/skill/SKILL.md

Improvements

Generate a prioritized plan and verify standards compliance using improvement_suggester.py and compliance_checker.py.

make improve-skill TARGET=path/to/skill/SKILL.md
make check-compliance TARGET=path/to/skill/SKILL.md

Evaluation Workflow

Start with make audit-all to inventory skills and identify high-priority targets. For each skill requiring attention, run analysis with analyze-skill to map complexity. Generate an improvement plan, apply fixes, and run check-compliance to verify the skill meets project standards. Finalize by checking the token budget for efficiency.

Evaluation and Optimization

Quality assessments use the skills-auditor and improvement-suggester to generate detailed reports. Performance analysis focuses on token efficiency through the token-usage-tracker and tool performance via tool-performance-analyzer. For standards compliance, the compliance-checker automates common fixes for structural issues.

Scoring and Prioritization

We evaluate skills across five dimensions: structure compliance, content quality, token efficiency, activation reliability, and tool integration. Scores above 90 represent production-ready skills, while scores below 50 indicate critical issues requiring immediate attention.

Improvements are prioritized by impact. Critical issues include security vulnerabilities or broken functionality. High-priority items cover structural flaws that hinder discoverability. Medium and low priorities focus on best practices and minor optimizations.

Structural Patterns

Deprecated: skills/shared/modules/ directories. Shared modules must be relocated into the consuming skill's own modules/ directory. The evaluator flags any remaining skills/shared/ as a structural warning.

Current: Each skill owns its modules at skills/<skill-name>/modules/. Cross-skill references use relative paths (e.g., ../skill-authoring/modules/anti-rationalization.md).

Resources

Shared Modules: Cross-Skill Patterns

Skill-Specific Modules

  • Trigger Isolation Analysis: See modules/trigger-isolation-analysis.md
  • Authoring Checklist: See modules/authoring-checklist.md
  • Evaluation Workflows: See modules/evaluation-workflows.md
  • Advanced Tool Use Analysis: See modules/advanced-tool-use-analysis.md
  • Evaluation Framework: See modules/evaluation-framework.md
  • Integration Patterns: See modules/integration.md
  • Troubleshooting: See modules/troubleshooting.md
  • Pressure Testing: See modules/pressure-testing.md
  • Integration Testing: See modules/integration-testing.md
  • Performance Benchmarking: See modules/performance-benchmarking.md

Tools and Automation

  • Tools: Executable analysis utilities in scripts/ directory.
  • Automation: Setup and validation scripts in scripts/automation/.

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

25.86%
按下载量换算90

OpenCode

21.71%
按下载量换算76

Cursor

17.68%
按下载量换算62

Codex

13.88%
按下载量换算48

Antigravity

7.78%
按下载量换算27

Gemini CLI

3.17%
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。

来源信息

继续浏览同类 Skills