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Agent Skill · 研究检索

naming-6-gates(命名 6 个门)

naming-6-gates 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。

naming-6-gates 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,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 32,923。

总安装 2,446周安装 98GitHub Stars 85下载 792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:naming-6-gates(命名 6 个门)
来源仓库:https://github.com/guia-matthieu/clawfu-skills
仓库路径:skills/naming-6-gates
安装命令:
npx skills add https://github.com/guia-matthieu/clawfu-skills --skill naming-6-gates
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/guia-matthieu/clawfu-skills --skill naming-6-gates

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。

用于查找、检索和筛选相关信息,支持基于关键词或任务场景定位目标内容。适用于需要快速聚合资料或验证命名规范的智能体工作流场景。通过命令行工具实现信息提取,输出候选结果供人工筛选或自动处理。安装前建议检查仓库活跃度与权限设置,留意是否涉及外部 API 调用。

naming-6-gates 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/naming-6-gates

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/guia-matthieu/clawfu-skills --skill naming-6-g…;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

naming-6-gates 怎么安装?

在终端执行 npx skills add https://github.com/guia-matthieu/clawfu-skills --skill naming-6-gates。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

naming-6-gates 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。

naming-6-gates 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。

naming-6-gates 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。

naming-6-gates 用的人多吗?

公开数据是累计安装 2,446 次,近一周安装 98 次,下载 792 次,GitHub 85 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。

naming-6-gates 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。

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
naming-6-gates

命名 6 个门 · 本页

2,44685调用外部服务未明确有提醒

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

Naming 6-Gates Protocol

Overview

Brand naming in 2026 is architectural engineering, not creative brainstorming. A name must be:

  1. Machine-compatible - tokenized efficiently by LLMs, citeable by AI engines
  2. Legally defensible - clear of trademark conflicts across jurisdictions
  3. Voice-ready - pronounceable by assistants, invocable without friction
  4. Narratively loaded - capable of carrying a brand story

Core principle: Optimize for AEO (Answer Engine Optimization) first, aesthetics second.

When to Use

  • Creating a new brand, product, or company name
  • Replacing a name due to trademark conflict
  • Expanding internationally (multilingual requirements)
  • Launching a voice-activated product or skill
  • Naming for AI-first discovery (ChatGPT, Perplexity, Gemini recommendations)

When NOT to Use

  • Internal project codenames (no legal/AEO requirements)
  • Temporary campaign names
  • Personal projects with no commercial intent

The 6-Gate Naming Process

digraph naming_process {
    rankdir=TB;
    node [shape=box];

    brief [label="1. BRIEF\nDefine constraints"];
    generate [label="2. GENERATE\nAI-assisted creation"];
    filter [label="3. LINGUISTIC FILTER\nMultilingual stress test"];
    legal [label="4. LEGAL FILTER\nTrademark screening"];
    aeo [label="5. AEO FILTER\nEntity strength test"];
    story [label="6. STORY FILTER\nNarrative potential"];

    brief -> generate -> filter -> legal -> aeo -> story;

    filter -> generate [label="fails" style=dashed];
    legal -> generate [label="conflicts" style=dashed];
    aeo -> generate [label="weak entity" style=dashed];
    story -> generate [label="no narrative" style=dashed];
}

Gate 1: BRIEF (Constraints Definition)

Define hard constraints BEFORE generating:

ConstraintTargetWhy
Length7-8 letters maxMobile icons, cognitive load, tokenization
Syllables1-3 syllablesVoice invocation, memorability
Character setA-Z onlyDomain availability, global keyboards
Forbidden sequencesght, ough, silent lettersPronunciation barriers

Output format:

brief:
  max_letters: 8
  max_syllables: 3
  target_markets: [FR, ES, US, UK]
  nice_classes: [9, 35, 42]
  avoid_phonemes: [ʒ, θ, ð]  # Hard for non-native speakers
  competitor_names: [Notion, Figma, Linear]  # Style reference
  forbidden_associations: [medical, financial, religious]

Gate 2: GENERATE (AI-Assisted Creation)

Use structured prompting with 5 typologies:

Typology 1: Analogical (Real-Word Transfer)

Words from unrelated domains applied to new context.

  • Examples: Apple (tech), Amazon (retail), Jaguar (auto)
  • Prompt: "Find words from [nature/mythology/geography] that evoke [core attribute]"

Typology 2: Metaphorical (Abstract Concept)

Abstract concepts made concrete.

  • Examples: Slack (workspace tension release), Notion (idea capture)
  • Prompt: "What single word captures the transformation from [pain] to [outcome]?"

Typology 3: Neologism (Invented Word)

New words with phonetic engineering.

  • Examples: Kodak, Xerox, Häagen-Dazs
  • Prompt: "Create a 2-syllable word with [plosive start] + [open vowel] + [soft ending]"

Typology 4: Portmanteau (Fusion)

Blend of two meaningful words.

  • Examples: Instagram (instant + telegram), Pinterest (pin + interest)
  • Prompt: "Blend [word1] + [word2] keeping recognition of both roots"

Typology 5: Empty Vessel

Meaningless but phonetically pleasing.

  • Examples: Rolex, Vimeo, Skype
  • Prompt: "Generate 5-letter combinations: CVCVC pattern, no existing meaning"

Generation Contract:

CONSTRAINTS:
- Max 8 letters
- Pronounceable in French, Spanish, English
- No existing trademark in Class 42
- Distinct from: [competitor list]

OUTPUT FORMAT per candidate:
- Name: [word]
- Typology: [1-5]
- Phonetic: /IPA transcription/
- Syllables: [count]
- Rationale: [1 sentence]

Gate 3: LINGUISTIC FILTER (Multilingual Stress Test)

Test each candidate across target markets:

Pronunciation Test

Run through TTS engines in 15+ languages:

  • Google TTS, Amazon Polly, Azure Speech
  • Listen for: mispronunciation, confusion, awkwardness

Cultural Connotation Check

Verify no negative meanings in:

  • Target market slang (TikTok, Douyin, RedNote trends)
  • Historical associations
  • Religious/political sensitivities
  • Homophones in local languages

Phonetic Hazards to Avoid

HazardExampleImpact
Silent letters"Knight"Voice assistant failure
Consonant clusters"Strengths"Non-native difficulty
Ambiguous vowels"Read" (reed/red)Confusion
Cross-language homophones"Gift" (poison in German)Negative association

Kill criterion: If TTS mispronounces in >2 target markets, reject.


Gate 4: LEGAL FILTER (Trademark Screening)

Phase 1: Quick Disqualification

  • Google: "[name] company" / "[name] brand" / "[name] trademark"
  • Domain check:.com,.co,.io,.fr,.eu
  • Social handles: Namechk scan all platforms

Phase 2: Formal Trademark Search

RegistryToolCheck For
FranceINPI data.inpi.frExact + phonetic similarity
EUEUIPO eSearch / TMviewSame Nice classes
USUSPTO TESSRegistered + pending
InternationalWIPO Global Brand DatabaseMadrid Protocol

Phase 3: AI-Assisted Similarity Detection

EUIPO "Early TM Screening" detects:

  • Phonetic similarity (sounds-like)
  • Visual similarity (looks-like)
  • Conceptual similarity (means-like)

Kill criterion: Any conflict in target Nice classes → reject.


Gate 5: AEO FILTER (Entity Strength Test)

A name must be recognizable as a distinct entity by AI systems.

Entity Strength Checklist

  • Unique token: Does the name tokenize as a single unit or get split?
  • No collision: Search "[name]" - does it return your intended context or noise?
  • Semantic boundaries: Can AI distinguish "[name] the company" from generic usage?

Testing AEO Strength

Prompt to test:
"What is [NAME]?"

GOOD response: "I don't have information about [NAME]" (clean slate)
BAD response: "[NAME] is a common word meaning..." (collision)
BAD response: "[NAME] could refer to several things..." (ambiguity)

Keyword Difficulty Check

Use Semrush/Ahrefs:

  • Keyword difficulty < 30 = good
  • Keyword difficulty > 60 = buried by generics

Kill criterion: If name collides with high-volume generic term, reject.


Gate 6: STORY FILTER (Narrative Potential)

A name must be "tell-able" - capable of carrying a brand story.

The 5 Narrative Pillars Test

For each candidate, verify you can build:

PillarQuestionExample Answer Required
Origin story"Why this name?"Must have a genuine, shareable reason
Belief storyWhat does the brand stand for?Values must connect to name
Product truthWhat does it actually do?Name should hint at function
TransformationWhat changes for the customer?Before/after must be nameable
CultureWhat kind of community forms?Name should enable identity

Empty Vessel vs. Loaded Name

  • Empty vessel (Kodak, Google): Brand fills meaning over time
  • Loaded name (Instagram, Airbnb): Meaning is built-in

Both work. But you must choose consciously and build accordingly.

Vocal Signature Test

Say the name aloud 10 times. Ask:

  • Does it feel good in the mouth?
  • Would customers enjoy saying it?
  • Does it have a "sticky" quality?

Kill criterion: If you can't tell a compelling origin story, reject.


Voice Assistant Constraints (2026)

AssistantInvocation RulesRejection Risks
Alexa2+ words required (unless trademarked)Single generic words
SiriMust be phonetically distinctConfusion with system commands
GoogleStrong Knowledge Graph presence neededNames too similar to famous brands
BixbyAvoid wake-word conflicts"Open", "cancel", common verbs

Test procedure:

  1. Say "[Name], do [action]" to each assistant
  2. Note recognition rate
  3. Reject if < 80% accurate invocation

Output: Naming Dossier

For the final candidate, produce:

## [NAME] - Naming Dossier

### Basic Info
- **Name:** [word]
- **Typology:** [1-5]
- **Phonetic:** /IPA/
- **Length:** X letters, Y syllables

### Linguistic Clearance
- FR pronunciation: [pass/fail + notes]
- ES pronunciation: [pass/fail + notes]
- EN pronunciation: [pass/fail + notes]
- Cultural flags: [none / list]

### Legal Clearance
- INPI (FR): [clear / conflict]
- EUIPO (EU): [clear / conflict]
- USPTO (US): [clear / conflict]
- Domains available: [list]
- Social handles: [list]

### AEO Score
- Entity uniqueness: [1-10]
- Keyword difficulty: [score]
- Tokenization: [single/split]

### Narrative Potential
- Origin story hook: [1 sentence]
- Brand values connection: [1 sentence]
- Community identity: [suggested demonym]

### Voice Readiness
- Alexa: [pass/fail]
- Siri: [pass/fail]
- Google: [pass/fail]

### Recommendation
[APPROVED / NEEDS WORK / REJECTED]
[Final reasoning]

Common Mistakes

MistakeWhy It FailsFix
Starting with brainstormingGenerates unusable namesStart with constraints (Gate 1)
Skipping TTS testVoice assistant failuresAlways test pronunciation
Ignoring Nice class overlapTrademark conflictsCheck specific classes, not just exact matches
Choosing descriptive namesWeak AEO, hard to trademarkPrefer distinctive/invented
No narrative testBrand can't tell its storyAlways verify storytelling potential

Quick Reference

The 7-8 Letter Rule: Shorter = better for mobile, voice, memory, tokenization.

Phonetic Gold Standard: CVCV pattern (consonant-vowel-consonant-vowel) works globally.

AEO Priority: If AI can't cite you as a distinct entity, you don't exist in 2026.

Empty Vessel Strategy: Meaningless names (Kodak) require more brand-building but face fewer conflicts.

Voice-First: If Alexa can't invoke it, 8.4 billion assistants can't recommend it.


What Claude Does vs What You Decide

Claude handlesYou provide
Generating candidates by typologyBrief constraints (markets, classes)
Running linguistic stress testsFinal pronunciation judgment
Checking trademark databasesLegal professional validation
Evaluating AEO/entity strengthStrategic brand direction
Producing naming dossierFinal selection and approval

Skill Boundaries

This skill excels for:

  • Brand, product, or company naming
  • Names requiring international deployment
  • Voice-assistant compatible names
  • AI-discoverable entity creation

This skill is NOT ideal for:

  • Internal codenames → No legal/AEO requirements
  • Temporary campaign names → Overhead unnecessary
  • Personal projects → Simplified process sufficient

Skill Metadata

name: naming-6-gates
category: branding
version: 2.0
author: GUIA
source_expert: 6-Gate Protocol (AEO-first naming)
difficulty: advanced
mode: centaur
tags: [naming, branding, trademark, voice-assistant, aeo, entity]
created: 2026-02-03
updated: 2026-02-03

平台分布

Codex

38.3%
按下载量换算303

Claude

29.84%
按下载量换算236

Cursor

19.44%
按下载量换算154

Gemini CLI

9.33%
按下载量换算74

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。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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