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Agent Skill · 研究检索

wine-archive(葡萄酒档案馆)

wine-archive 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

wine-archive 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,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 23,964。

总安装 4,134周安装 174GitHub Stars 1下载 1,448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-0
版本
0.1.8

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:wine-archive(葡萄酒档案馆)
来源仓库:https://github.com/sidvorak/wine-archive
安装命令:
openclaw skills install wine-archive
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install wine-archive

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。

使用自然语言查询和本地 SQLite 数据库中的持久图像存储来存储、调用和管理个人品酒和标签。

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 OpenClaw;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 openclaw skills install wine-archive;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

    它需要接触敏感数据,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

wine-archive 怎么安装?

在终端执行 openclaw skills install wine-archive。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

wine-archive 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

wine-archive 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

wine-archive 是免费的吗?

来源标注的许可证是 MIT-0。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。

wine-archive 用的人多吗?

公开数据是累计安装 4,134 次,近一周安装 174 次,下载 1,448 次,GitHub 1 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。

wine-archive 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 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

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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 OpenClaw,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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
wine-archive

葡萄酒档案馆 · 本页

4,1341接触敏感数据MIT-0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

Wine Archive

Personal wine archive for tasting history, label storage, and natural-language recall. Stores wines in a local SQLite database with a normalized schema: stable wine identities plus individual tasting/purchase instances. Integrates with Telegram via the OpenClaw bridge.

Trigger phrases

Use this skill when the user says things like:

  • "remember this wine", "log this bottle", "add a wine", "save this tasting"
  • "what wine did I have last week?", "show me my pinot noirs", "recall wines from Nugget"
  • "show the [wine name] label", "send me the Vinho Verde photo"
  • "show me the [wine name]", "tell me about the Broadbent"
  • "export my wine archive", "import wine data"

Setup (first time)

cd skills/wine-archive
zsh setup.sh

Or manually:

cd skills/wine-archive
npm install
npm run wine:init
cp .env.example .env   # add ANTHROPIC_API_KEY for LLM features

Data is stored in data/wine/wine.sqlite3 and label images in data/wine/labels/. Override the DB location with WINE_DB_PATH env var.

Core rules

  • Personal ratings use a 5-star scale.
  • Prefer new tasting instances over overwriting when the user re-tries a wine.
  • If it is ambiguous whether to update an existing instance or create a new one, ask.
  • Keep deterministic regex parsing as the source of truth; LLM parsing is assistive only.
  • Archived label images are durable — never rely on temporary inbound media paths.
  • On Telegram, use the bridge path for label recall rather than inline MEDIA: text.

Data model

Wine = stable identity

  • producer, wine_name, region, country, style, color, varietal
  • official_rating / official_rating_source
  • label image (default_source_image_path)

Wine instance = specific bottle / tasting event

  • vintage, price, currency, place_of_purchase
  • purchased_on, consumed_on
  • subjective_rating (5-star), notes
  • source_type (chat | image | manual)

Commands

Initialize DB:

npm run wine:init

Add from free text:

npm run wine:add -- --text "Had a Broadbent Vinho Verde from Minho. Bought at Nugget for $14. Rated 4/5."

Add structured:

npm run wine:add -- --wine_name "Broadbent Vinho Verde" --varietal "Loureiro" --region "Minho" \
  --style "vino verde" --color "white" --price 14 --place_of_purchase "Nugget" --consumed_on 2026-03-29

Add from label text + image:

npm run wine:add -- --label-text $'Broadbent\
Vinho Verde\
2024\
Minho Portugal\
Loureiro' \
  --image data/wine/labels/broadbent.jpg

Parse label without inserting:

npm run wine:parse-label -- --label-text $'Broadbent\
Vinho Verde\
2024\
Minho Portugal'

Query:

npm run wine:query -- --text "vinho verde"
npm run wine:query -- --varietal "Pinot Noir"
npm run wine:query -- --consumed_after 2026-03-24 --consumed_before 2026-03-31

Natural-language recall:

npm run wine:recall -- --text "show me wines from last week at Nugget"
npm run wine:recall -- --text "what pinot noir did I drink last month"
npm run wine:recall -- --text "find red wines rated at least 3"

Chat-facing flow (intent detection + dispatch):

npm run wine:chat -- --text "Remember this wine: Had a vinho verde last week. Rated 4/5."
npm run wine:chat -- --text "What was that vinho verde I had last week?"
npm run wine:chat -- --label-text $'Broadbent\
Vinho Verde\
2024\
Minho' --image data/wine/labels/broadbent.jpg

List recent entries:

npm run wine:list -- --limit 10

Remove an entry:

npm run wine:remove -- --id 42

Export archive:

npm run wine:export -- --out my-wines.json              # paths only
npm run wine:export -- --out my-wines.json --include-images  # embed label images as base64

Import archive:

npm run wine:import -- --in my-wines.json --dry-run    # preview only
npm run wine:import -- --in my-wines.json               # import

Telegram bridge (requires OpenClaw)

The bridge script resolves the wine chat request and outputs a structured JSON result. When action === "send-media", the agent sends the media using the openclaw CLI.

npm run wine:telegram-bridge -- --text "Show me the Vinho Verde label"

Example output when a label is found:

{
  "status": "ok",
  "action": "send-media",
  "reply": "Broadbent Vinho Verde label",
  "mediaPath": "./data/wine/labels/broadbent-abc123.jpg",
  "caption": "Vinho Verde label"
}

The agent then sends:

openclaw message send --channel telegram \
  --target <chat_id> --thread-id <thread_id> --reply-to <reply_to> \
  --media <mediaPath> --message <caption>

Shell helper (outputs JSON for the agent):

zsh scripts/wine-send-label-telegram.sh <chat_id> <thread_id> <reply_to> Vinho Verde

Response templates

Add / save confirmation

Added as a new wine instance:
- Broadbent Vinho Verde · 2024
- Nugget, $14.00 · consumed 2026-03-29
- rated 4/5

Update confirmation

Updated — rating: 3.5/5, consumed_on: 2026-04-02

Show a wine (canonical entry)

Broadbent — Vinho Verde
- region: Minho
- country: Portugal
- style: vino verde
- color: white
- varietal: Loureiro
[label image]

Show wine instances

Broadbent — Vinho Verde (2024)
- consumed: 2026-03-29
- rated: 4/5
- notes: crisp, slightly frizzante

Show a list of wines

- Vinho Verde
  producer: Broadbent
  year: 2024
  varietal: Loureiro
  style: vino verde

Recall by time window

Broadbent — Vinho Verde (2024)
- consumed: 2026-03-29
- rated: 4/5

Label response

Send the archived image with caption: Vinho Verde label

Clarification (ambiguous intent)

Do you want me to update the existing instance or create a new tasting instance?

Environment variables

VariableDefaultDescription
ANTHROPIC_API_KEY—Required when using an Anthropic model for LLM intent classification
OPENAI_API_KEY—Required when using an OpenAI model for LLM intent classification
WINE_DB_PATHdata/wine/wine.sqlite3Override DB location
WINE_LLM_INTENT_CLASSIFIER0Set to 1 to enable LLM intent classification (off by default; skill works fully offline without it)
WINE_LLM_INTENT_MODELopenai/gpt-4.1-miniModel for intent classification — use claude-haiku-4 for Anthropic, openai/gpt-4.1-mini for OpenAI
WINE_LLM_INTENT_MIN_CONFIDENCE0.65Minimum confidence to use LLM over regex

Platform notes

  • macOS: Full support including image normalization via sips.
  • Linux/Windows: Core features work. Image resizing is skipped (install ImageMagick

and adapt lib/wine-store.js normalizeImageInPlace for cross-platform resizing).

  • Node.js >= 22 required (node:sqlite built-in used in shared/interaction-store.js).

平台分布

OpenClaw

82.68%
按下载量换算1,197

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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