Token导航 LogoToken导航TokenDH.com
研究检索操作浏览器github未标认证来源可访问clear审计提醒

gourmet-research美食研究

Agent Skill

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。

总安装

245

周安装

10

GitHub Stars

7

下载量

79
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: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

简介

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。

SKILL.md

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/

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

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 warning/failed 结果,不能写成本站确认安全。

来源信息

继续浏览同类 Skills