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Agent Skill · 研究检索

gourmet-research(美食研究)

gourmet-research 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。

gourmet-research 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,权限为操作浏览器;许可证为 MIT,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 67,439。

总安装 245周安装 10GitHub Stars 7下载 79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
MIT

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:gourmet-research(美食研究)
来源仓库:https://github.com/narumiruna/agent-skills
仓库路径:skills/gourmet-research
安装命令:
npx skills add https://github.com/narumiruna/agent-skills --skill gourmet-research
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/narumiruna/agent-skills --skill gourmet-research

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。

gourmet-research 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于需要根据关键词或任务场景进行信息检索的场景,如美食研究或内容调研。通过 npx skills add 命令从 GitHub 仓库安装,支持主流 AI 宿主环境。

安装前需确认权限范围、维护状态,注意可能触发联网或文件读写操作。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/gourmet-research

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/narumiruna/agent-skills --skill gourmet-resear…;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

    它需要操作浏览器,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

gourmet-research 怎么安装?

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

gourmet-research 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。

gourmet-research 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

gourmet-research 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

gourmet-research 用的人多吗?

公开数据是累计安装 245 次,近一周安装 10 次,下载 79 次,GitHub 7 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。

gourmet-research 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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 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
gourmet-research

美食研究 · 本页

2457操作浏览器MIT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

Gourmet Research

Overview

Template-first workflow for traceable, comparable, auditable food recommendations across cities. Keep evidence, scores, and decisions synchronized.

Core Rules (Non-Negotiable)

  • If the user has not specified an output language, ask once at project start and record it in overview.md.
  • Use gourmet// with 6 files: overview.md, inbox.md, candidates.md, notes.md, top-places.md, excluded.md.
  • Never fabricate sources, ratings, or claims. Use unknown when missing.
  • Prefer original-language place names (not translated) unless the user requests otherwise.
  • Preserve audit trail: never delete candidates; mark rejected and record why in excluded.md.
  • Default minimum sources = 4. If the locale is information-sparse, allow 3 only when you record evidence: limited with the reason and attempted sources.

Template-First Workflow (Summary)

  1. Initialize: Ask for output language + city, then copy templates from assets/templates/ into the city folder.
  2. Normalize: Update language/city placeholders in the copied files before research begins.
  3. Discovery: Capture raw ideas in inbox.md, then move top candidates into candidates.md with status: inbox.
  4. Evidence: For each candidate, write a full evidence block in notes.md with sources + practical constraints.
  5. Score: Apply the 50-point rubric and justify each component in notes.md.
  6. Decide: Promote to Top Picks (>=35), Backups (30-34), or reject (<30).
  7. Publish: Update top-places.md and excluded.md to match decisions.
  8. Verify: Ensure no inbox statuses remain and required sections exist.

Ranking Retrieval (When user asks for “highest score”)

Before extracting any “top N” list, confirm the scope:

  • Geography: Okinawa *prefecture* vs *main island only* vs *specific subarea*.
  • Category: overall vs cuisine category.
  • Source URL: must match the user’s intent exactly.

Checklist (must pass):

  1. URL matches the requested scope (prefecture vs category).
  2. If “main island only” is required, exclude island subareas (A4705/A4706).
  3. Page title confirms the intended ranking.
  4. Language modal handled so list items actually render.

If static scraping fails or content is blocked, use Playwright to load the page, close the language modal (日本語), and then extract items.

Evidence & Negative Review Rules

  • Sources must include: Maps + local reviews + guide/editorial + official channel (where available).
  • Negative review analysis is conditional: perform a focused negative review pass when risk signals appear in any source.

- Risk signals: repeated service complaints, hygiene/safety concerns, tourist-trap claims, extreme queue issues, inconsistent ratings, unclear hours/reservations. - If triggered: add a Negative reviews subsection in notes.md, adjust Risk/Consistency/Value as needed, and sync scores/status across files.

Locale-Specific Source Suggestions (Optional)

LocaleLocal reviewsAggregatorGuides/editorial
JapanTabelog, RettyGoogle MapsMichelin, local food media
KoreaNaver Map, Kakao MapGoogle MapsMichelin, local food media
TaiwanGoogle Maps, iPeenOpenRiceLocal food media
Hong KongOpenRiceGoogle MapsMichelin, local food media
SingaporeOpenRiceGoogle MapsMichelin, local food media
EuropeGoogle MapsTripadvisorMichelin, local city guides
North AmericaGoogle Maps, YelpTripadvisorEater, local food media
Latin AmericaGoogle MapsTripadvisorLocal city guides
SEA (general)Google MapsTripadvisorLocal food media

Scoring (50-Point Rubric)

  • Taste/Quality (0-10)
  • Value (0-10)
  • Convenience (0-10)
  • Consistency (0-10)
  • Risk (0-10, higher = lower risk)

Thresholds:

  • Top Picks: >=35
  • Backups: 30-34
  • Reject: <30 (or hard exclusion: hygiene/safety/tourist-trap evidence)

Roles (Optional, Compact)

  • Research: find sources + capture evidence.
  • Verify: resolve conflicts, confirm practical constraints.
  • Score: apply rubric + justify.
  • Synthesize: finalize top-places + dining strategy.

Quick Reference

ItemRule
City pathgourmet/<city-slug>/
Filesoverview/inbox/candidates/notes/top-places/excluded
Min sources4 (3 only with evidence: limited)
Output languageAsk if not specified
Place namesPrefer original language
Score tiers>=35 Top, 30-34 Backup, <30 Reject

Example (Evidence Block)

### Sakura Teahouse
**Official**: https://example.com
**Maps**: 4.4/5 (820 reviews) - https://maps.app.goo.gl/...
**Local reviews**: 3.7/5 (420 reviews) - https://tabelog.com/...
**Guide/editorial**: https://guide.example.com/...
**Notes**: quiet seating, popular seasonal desserts
**Practical**: reservations recommended, closed Tue
**Score**: Taste 8 / Value 7 / Convenience 6 / Consistency 7 / Risk 7 = **35/50**

Common Mistakes

  • Skipping templates and mixing content across files.
  • Skipping inbox.md and dumping raw ideas into candidates.
  • Translating place names instead of using the original language.
  • Using only one review platform.
  • Pulling the wrong ranking scope (category vs overall, islands included).
  • Changing scores without updating candidates/top-places/excluded.
  • Ignoring unclear hours or reservation policies.

Rationalization Table

ExcuseReality
"It’s just one city; I can skip templates."Templates prevent drift and keep outputs comparable.
"Inbox is optional; I can put everything in candidates."inbox.md keeps raw capture separate and reduces noise.
"There aren’t 4 sources; I’ll guess."Use unknown and mark evidence: limited. Never guess.
"I’ll translate names for clarity."Keep original-language names unless the user asks.
"This ranking page is close enough."Scope mismatch invalidates the answer. Confirm URL and geography.
"Negative reviews are optional."Required when risk signals appear.

Red Flags — Stop and Fix

  • Candidates deleted instead of rejected.
  • Scores updated in notes but not in candidates/top-places.
  • Missing output language decision.
  • Uncited claims or ratings.

References

  • references/repo-spec.md
  • assets/templates/

平台分布

Codex

61.45%
按下载量换算49

Claude Code

30.99%
按下载量换算24

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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。

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