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Agent Skill · 研究检索

skill-optimization(技能优化)

skill-optimization 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-optimization 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,权限为调用外部服务;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 70,834。

总安装 198周安装 8GitHub Stars 191下载 62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-optimization(技能优化)
来源仓库:https://github.com/shinpr/ai-coding-project-boilerplate
仓库路径:skills/skill-optimization
安装命令:
npx skills add https://github.com/shinpr/ai-coding-project-boilerplate --skill skill-optimization
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/shinpr/ai-coding-project-boilerplate --skill skill-optimization

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-optimization 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。安装方式:github,使用 npx skills add 命令添加。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/skill-optimization

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/shinpr/ai-coding-project-boilerplate --skill s…;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

    它需要调用外部服务,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-optimization 怎么安装?

在终端执行 npx skills add https://github.com/shinpr/ai-coding-project-boilerplate --skill skill-optim…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

skill-optimization 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-optimization 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

skill-optimization 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-optimization 用的人多吗?

公开数据是累计安装 198 次,近一周安装 8 次,下载 62 次,GitHub 191 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-optimization 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。

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。

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
skill-optimization

技能优化 · 本页

198191调用外部服务未明确通过

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

Skill Content Optimization

Core Philosophy

  1. Evidence-Based: Grounded in prompt engineering research, applied to skill authoring
  2. Concrete: Each pattern provides detection criteria and transform methods
  3. Structure-Focused: Optimizes expression and organization; domain knowledge remains unchanged

Content Optimization Patterns

P1: Critical (Must Fix)

Issues that directly reduce LLM execution accuracy when consuming the skill.

BP-001: Negative Instructions → Positive Form
DetectionTransform
"don't", "do not", "never", "avoid" in skill instructionsReframe as positive directive with equivalent constraint

Skill example:

  • Before: "Don't use generic variable names"
  • After: "Use descriptive variable names that reflect purpose (e.g., userId not x)"

Why critical for skills: LLM attention mechanisms focus on negated content. Skill instructions with "don't" increase probability of the forbidden behavior.

BP-002: Vague Instructions → Specific Criteria
DetectionTransform
"appropriate", "good", "proper", "best", "should be clear"Replace with measurable if-then criteria or concrete thresholds
Missing output format, scope, or success criteriaAdd explicit constraints

Skill example:

  • Before: "Handle errors appropriately"
  • After: "Error handling criteria: 1. try-catch for external API calls, file I/O, JSON.parse 2. Log: error.name, error.stack, timestamp 3. Re-throw with context if caller needs to handle"

Why critical for skills: Accounts for ~40% of execution variance. Every vague instruction forces LLM to guess.

BP-003: Missing Output Format → Structured Output
DetectionTransform
Skill describes what to do but not the expected deliverable formatAdd output section with structure, fields, and example

Skill example:

  • Before: "Analyze the code for issues"
  • After: "Output format: ## Issues Found with table: | Severity | Location | Description | Suggested Fix |"

Why critical for skills: Structured output constraints reduce hallucination and make skill results consistent.

P2: High Impact (Should Fix)

Issues that reduce skill effectiveness when addressed.

BP-004: Unstructured Content → Organized Format
DetectionTransform
Wall of text without headingsApply standard section order (see below)
Multiple topics mixed in one sectionSplit into distinct headed sections
No tables for reference dataConvert lists of criteria/patterns to tables

Standard skill section order:

  1. Context/Prerequisites
  2. Core concepts (definitions, patterns)
  3. Process/Methodology (step-by-step)
  4. Output format/Examples
  5. Quality checklist
  6. References

Conditional: Skip restructuring if skill is under 30 lines and covers a single topic.

BP-005: Missing Context → Explicit Prerequisites
DetectionTransform
Skill assumes knowledge not statedAdd Prerequisites section listing required context
Domain terms used without definitionAdd definitions inline or in a glossary table
No "when to use" guidanceAdd trigger conditions with concrete scenarios

Skill example:

  • Before: "Apply the strangler pattern for migration"
  • After: "Prerequisite: Existing monolith with identifiable module boundaries. When to use: Replacing legacy module while maintaining production traffic."
BP-006: Complex Content → Decomposed Steps
DetectionTransform
3+ objectives in one instructionBreak into numbered steps with checkpoints
Sequential dependencies not explicitAdd dependency markers between steps
No intermediate verificationInsert checkpoint after each step

Conditional: Skip decomposition for simple reference tables or single-criteria rules.

Key insight: Goal is evaluable granularity with quality checkpoints, not decomposition for its own sake.

P3: Enhancement (Could Fix)

Incremental improvements for specific contexts.

BP-007: Biased Examples → Diverse Coverage
DetectionTransform
All examples share same pattern/structureAdd edge cases and exceptions
Only happy-path examplesAdd error cases, boundary conditions
Examples all same complexityInclude simple, moderate, and complex
BP-008: No Uncertainty Permission → Explicit Escalation
DetectionTransform
Skill demands definitive answers alwaysAdd escalation criteria for ambiguous cases
No "when to stop" guidanceAdd explicit stopping conditions

Skill example:

  • Before: "Determine the root cause"
  • After: "Determine the root cause. If root cause is uncertain after 3 investigation cycles, report top 3 hypotheses with confidence levels and evidence for each."

9 Skill Editing Principles

Measurable quality criteria for skill content. Each principle includes a pass/fail test.

#PrinciplePass CriteriaFail Example
1Context efficiencyEvery sentence contributes to LLM decision-making. No filler."This is an important skill that helps with..."
2DeduplicationNo concept explained twice within the skill or across skillsSame error handling rules in both coding-standards and typescript-rules
3GroupingRelated criteria in single section (minimize read operations)Scattered error handling rules across 4 sections
4MeasurabilityAll criteria use if-then format or concrete thresholds"Write clean code" without definition of clean
5Positive formInstructions state what to do (BP-001 applied)"Don't use any" instead of "Use only X"
6Consistent notationUniform heading levels, list styles, table formatsMix of -, *, 1. in same context
7Explicit prerequisitesAll assumed knowledge statedUses "DI" without defining Dependency Injection
8Priority orderingMost important items first, exceptions lastEdge cases before common patterns
9Scope boundariesExplicit coverage: what this skill addresses vs references to other skillsOverlapping guidance with no cross-reference

References

平台分布

Codex

35.24%
按下载量换算22

Claude

28.54%
按下载量换算18

Cursor

18.91%
按下载量换算12

Gemini CLI

9.73%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。

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