Token导航 LogoToken导航

Agent Skill · 研究检索

normalize-skill-for-claude(normalize 技能 FOR Claude)

normalize-skill-for-claude 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。

normalize-skill-for-claude 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,权限为执行命令;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 52,672。

总安装 549周安装 22GitHub Stars 公开资料未说明下载 178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:normalize-skill-for-claude(normalize 技能 FOR Claude)
来源仓库:https://github.com/ulpi-io/skills
仓库路径:skills/normalize-skill-for-claude
安装命令:
npx skills add https://github.com/ulpi-io/skills --skill normalize-skill-for-claude
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/ulpi-io/skills --skill normalize-skill-for-claude

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。

normalize-skill-for-claude 用于查找、检索和筛选相关信息,适合技能标准化支持场景。适用于 Codex、Claude、Cursor 和 Gemini CLI,支持基于线索快速定位相关内容。可通过 GitHub 仓库和原始文档继续核验具体用法。安装前应确认是否会触发外部操作,评估安全风险。

建议在受控环境中测试后再集成到技能管理中。

来源仓库内路径:skills/normalize-skill-for-claude

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/ulpi-io/skills --skill normalize-skill-for-cla…;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

normalize-skill-for-claude 怎么安装?

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

normalize-skill-for-claude 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。

normalize-skill-for-claude 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/ulpi-io/skills --skill normalize-skill-for-claude 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。

normalize-skill-for-claude 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。

normalize-skill-for-claude 用的人多吗?

公开数据是累计安装 549 次,近一周安装 22 次,下载 178 次。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。

normalize-skill-for-claude 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

  • 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

  • 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。

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
normalize-skill-for-claude

normalize 技能 FOR Claude · 本页

549—执行命令未明确通过

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

Non-negotiable rules:

  1. Read references/claude-skill-runtime.md before planning or rewriting.
  2. Default to --mode plan unless the user explicitly asks to rewrite now.
  3. Treat frontmatter as the highest-leverage surface. Claude routes and budgets skills from metadata first.
  4. Keep SKILL.md focused on workflow. Move bulky examples, framework variants, and edge-case catalogs into references/ or scripts/.
  5. Do not add context: fork, paths:, or user-invocable: false without a concrete runtime reason.
  6. Never add agent-only frontmatter to a skill. tools, disallowedTools, skills, initialPrompt, permissionMode, maxTurns, background, memory, isolation, color, and mcpServers belong to agents, not skills.

Normalize Skill For Claude

Inputs

  • $target: Skill directory or direct path to SKILL.md
  • $mode: Optional. plan or rewrite. Default: plan

Goal

Produce a Claude Code optimized skill that is easier to auto-invoke, cheaper to keep in prompt budget, and safer to execute. Always start by creating a per-skill DAG plan unless the user explicitly asks for direct rewrite.

Step 1: Resolve the target

  1. Accept either a skill directory or a direct SKILL.md path.
  2. Normalize to the skill root and confirm SKILL.md exists.
  3. Determine mode:

- --mode rewrite means plan first, then rewrite in the same run - missing mode means plan

  1. Inventory the current skill:

- line count of SKILL.md - existing frontmatter fields - whether references/, scripts/, or assets/ already exist - whether the skill appears internal-only or user-invocable

Success criteria: You know the exact skill root, target mode, current file shape, and likely scope of the rewrite.

Step 2: Load the Claude Code skill runtime anchors

Read references/claude-skill-runtime.md fully before making any recommendation.

Extract these constraints from the reference:

  • Claude budgets skill discovery from frontmatter and short descriptions first.
  • Full skill bodies are loaded on invocation, not for listing.
  • paths: activates conditional skills when matching files are touched.
  • context: fork runs the skill in a forked subagent and should be rare.
  • allowed-tools should be the minimum needed permission surface.
  • Skill rewrites must use skill-native frontmatter only, not agent-native headers.

If this repository contains claude-code-source/, use the exact source files named in the reference to verify edge cases before rewriting unusual frontmatter.

Success criteria: Every planned change can be tied to a specific Claude runtime behavior.

Step 3: Audit the current skill against Claude's runtime

Evaluate the target skill using this checklist:

  1. Description and trigger quality

- Does description say what the skill does, when to use it, and example trigger phrases? - Is when_to_use missing or weak?

  1. Frontmatter completeness

- Would allowed-tools, arguments, argument-hint, user-invocable, paths, context, or agent improve runtime behavior? - Are there any agent-only headers that must be removed or explicitly avoided?

  1. Body size and shape

- Is the body carrying reference material instead of core workflow? - Are there giant examples, duplicated checklists, or framework-specific sections that belong in references/? - Structural content audit: Does the skill contain routing tables (task→reference mappings), personality/expertise definitions, numbered rule sets, or gate classifications? These are high-signal inline content, NOT bulk examples. Tag each as PRESERVE-INLINE or EXTRACT-WITH-MANDATORY-LOAD.

  1. Invocation type

- Is this a public slash command, an internal router, or a conditional path-scoped helper?

  1. Execution model

- Would context: fork help, or would it fragment work and require mid-flow user interaction?

  1. Determinism opportunities

- Are there repeated shell snippets or fragile transformations that belong in scripts/?

Classify each issue:

  • ROUTING: metadata is too weak for auto-invocation
  • PROMPT: body is too large or redundant
  • SAFETY: tool scope or execution mode is too broad
  • STRUCTURE: references or scripts should be extracted

Success criteria: You have a concrete, source-backed audit of what must change and why.

Step 4: Write the per-skill DAG plan

Create both of these artifacts:

  • .ulpi/plans/skills/<skill-name>-normalize-for-claude.md
  • .ulpi/plans/skills/<skill-name>-normalize-for-claude.json

The plan must include:

  1. Current state

- line count - existing frontmatter - current folders - largest bloat areas

  1. Claude runtime findings

- each finding mapped to source references from references/claude-skill-runtime.md

  1. Target state

- final frontmatter shape - files to keep, rewrite, create, or split

  1. DAG tasks

- frontmatter rewrite - body trim - reference extraction - optional script extraction - validation

  1. Guardrails

- what must not be changed - what must not be over-automated

  1. Validation

- concrete commands or checks to confirm the skill is structurally sound

Each DAG task should include:

  • id
  • title
  • rationale
  • filesToModify
  • filesToCreate
  • dependencies
  • validation

Success criteria: The markdown and JSON plans describe the same work and can be executed safely.

Step 5: Rewrite only if requested

If mode is plan, stop after writing the DAG artifacts.

If mode is rewrite:

  1. Rewrite the frontmatter first.
  2. Keep the body focused on:

- inputs - goal - workflow steps - guardrails - output contract

  1. Move bulky material into references/, but respect content classification:

- Bulk (move to references): giant examples, framework variants, edge-case catalogs, verbose checklists, before/after code samples - Structural (keep inline OR extract with mandatory load directive): routing tables (task→reference mappings), numbered guardrail rules in EXTREMELY-IMPORTANT, gate classifications (BLOCK/CONCERN/OBSERVATION), personality/expertise sections - Routing tables that map task types to reference files MUST stay inline — they are the skill's primary navigation surface. - Concrete guardrail lists (numbered rules, always/never) MUST either stay in the EXTREMELY-IMPORTANT block or get a mandatory load directive in the step that needs them — not a soft "see references" suggestion. - When extracting personality/expertise to a reference file, add a mandatory load directive at session start, not just a "When To Load References" entry.

  1. Create scripts/ only when deterministic repeated logic is clearly justified.
  2. Keep the resulting SKILL.md lean enough that future edits remain easy.

Preferred rewrite outcomes:

  • description and when_to_use become trigger-oriented
  • internal router skills become user-invocable: false
  • domain skills gain paths: only when file-touch activation clearly helps
  • context: fork appears only for self-contained analysis/research workflows
  • no agent-only headers are introduced into the rewritten skill

Success criteria: The rewritten skill is structurally smaller, clearer to route, and justified by Claude runtime behavior.

Step 6: Validate the result

After planning or rewriting:

  1. Re-read the final SKILL.md.
  2. Confirm the body does not duplicate reference files.
  3. Confirm frontmatter fields are intentionally chosen, not cargo-culted.
  4. Confirm no agent-only headers were introduced.
  5. Confirm every references/ file is directly linked from SKILL.md.
  6. Confirm no routing tables, numbered guardrail rules, or gate classifications were stripped without replacement.
  7. Confirm the plan or rewrite summary names the Claude source anchors that drove the major decisions.

Success criteria: The output is usable immediately by a human or follow-on agent without guessing.

Output Contract

Always report:

  1. Skill: target path and normalized root
  2. Mode: plan or rewrite
  3. Top runtime issues: the highest-value Claude mismatches
  4. Artifacts: exact plan or rewritten file paths
  5. Guardrails applied: 3 to 5 bullets tied to Claude runtime behavior

If rewrite mode was used, also report:

  • which sections moved to references/
  • which frontmatter fields were added, removed, or intentionally omitted

平台分布

Codex

38.34%
按下载量换算68

Claude

29.83%
按下载量换算53

Cursor

19.21%
按下载量换算34

Gemini CLI

9.25%
按下载量换算16

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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/ulpi-io/skills --skill normalize-skill-for-claude 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。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