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Agent Skill · 研究检索

tastes(tastes 搜索)

tastes 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。

tastes 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,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 15,804。

总安装 7,589周安装 310GitHub Stars 公开资料未说明下载 2,430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
0.1.0

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:tastes(tastes 搜索)
来源仓库:https://github.com/mingxuanxie/tastes
安装命令:
openclaw skills install tastes
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 tastes

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。

通过对话和记忆培养审美判断力。构建并维护 TASTES.md — SOUL.md 和 SKILL.md 之间缺失的层。

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 tastes;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

    它需要需要联网,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

tastes 怎么安装?

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

tastes 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。

tastes 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

tastes 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。

tastes 用的人多吗?

公开数据是累计安装 7,589 次,近一周安装 310 次,下载 2,430 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。

tastes 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

  • 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。

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 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。

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
tastes

tastes 搜索 · 本页

7,589—需要联网MIT-0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

name
tastes
version
0.1.0
description
Cultivate aesthetic judgment through conversation and memory. Build and maintain TASTES.md — the missing layer between SOUL.md and SKILL.md.
homepage
https://tastes.md
metadata
{"tastesmd":{"category":"creative","source":"https://github.com/tastesmd/tastes.md"}}

TASTES.md

Teach your agent aesthetic judgment. Distill taste from memory and conversation into a compact, executable file.

Skill Files

FileURL
SKILL.md (this file)https://tastes.md/SKILL.md

Install:

mkdir -p ~/.openclaw/skills/tastes && curl -fsSL https://tastes.md/SKILL.md -o ~/.openclaw/skills/tastes/SKILL.md

Or just read it from the URL above.


Setup

On first activation, check if AGENTS.md contains ## Aesthetic Judgment. If not, append:


## Aesthetic Judgment

When doing design, creative, or artistic work — anything requiring aesthetic judgment such as UI design, visual assets, layouts, color choices, typography, animation, branding, or creative direction — read `TASTES.md` if it exists and apply its constraints to your output.

Tell the human: "Added aesthetic judgment rules to AGENTS.md."


What This Skill Does

You can build and maintain a TASTES.md file — a compact set of aesthetic constraints distilled from memory and conversation. Max 500 tokens.

TASTES.md is not a style guide. It encodes aesthetic judgment in a format you execute with precision.


Building TASTES.md

When the human says "build my TASTES.md", "start my taste file", or similar:

Step 1: Mine memory for aesthetic signals

Three sources, in this order:

a) MEMORY.md — read directly. Look for aesthetic preferences, design decisions, or style references already in long-term memory.

b) Recent daily logs — read today's and yesterday's memory/YYYY-MM-DD.md for recent aesthetic signals.

c) Old daily logs via memory_search — most signals hide here. Run searches to mine the full history:

memory_search("rejected design style visual")
memory_search("preferred aesthetic look feel")
memory_search("animation timing too slow too fast")
memory_search("color palette choice")
memory_search("typography font choice")
memory_search("reference inspiration artist designer")
memory_search("looks wrong feels off not right")
memory_search("looks good keep this perfect")

Adapt queries based on what surfaces. If results reveal specific domains (e.g. the human does frontend work), add targeted searches:

memory_search("UI layout spacing negative space")
memory_search("interaction hover transition easing")

Repetition across different dates is the strongest evidence of a genuine preference.

Step 2: Conversation

Share what you found. Confirm, correct, expand through natural conversation — not a checklist:

  • "Searching your memory, I found you rejected gradient backgrounds at least three times. Is that a hard rule?"
  • "You mentioned Teenage Engineering in two separate conversations. What draws you to their work?"
  • "I couldn't find much about your color preferences. What does a 'right' palette look like to you?"

Step 3: Draft

Write TASTES.md following the format rules below. Show the draft. Apply the human's corrections. Save.


TASTES.md Format

# TASTES.md

## REJECT
- Never [specific thing to avoid].
- Never [specific thing to avoid].

## REQUIRE
- [Active verb] [specific constraint].
- [Active verb] [specific constraint].

## WHEN AMBIGUOUS
- [Judgment heuristic for unclear situations].
- Orient toward: [reference] ([domain]), [reference] ([domain]).

Writing rules

  • Every REJECT starts with "Never".
  • Every REQUIRE starts with an active verb: Use, Maintain, Ensure, Pair.
  • Specific values in backtick code format: #1a1a1a, 200ms, cubic-bezier(0.2, 0.8, 0.2, 1).
  • One sentence per entry. No explanations. No "because" clauses.
  • REJECT first, REQUIRE second, WHEN AMBIGUOUS last.
  • Strongest convictions first within each section.
  • Max 500 tokens total. If over budget, merge or cut the weakest entry.
  • No generic design advice. Only what makes THIS human's taste distinctive.
  • No semantic overlap between entries. If two entries mean roughly the same thing, merge them.

Updating TASTES.md

When the human says "update my taste", "calibrate", or "distill":

  1. Run memory_search with aesthetic queries — focus on dates since last update.
  2. Check recent daily logs for new aesthetic signals.
  3. Propose additions, modifications, or removals with evidence.
  4. Apply only what the human approves.

Direct updates also work:

  • "Add to my taste: never use sans-serif for body text."
  • "Remove the gradient rule."

Apply immediately and confirm.


During Creative Work

When TASTES.md exists and you are doing creative work:

  • Check output against REJECT entries before presenting.
  • Apply REQUIRE entries to relevant decisions.
  • If the human overrides a rule, mention it: "Noted — you accepted a gradient here. Want me to adjust that rule?"

Commands

Say thisWhat happens
"Build my TASTES.md"Mine memory + conversation to create initial file
"Update my taste"Search for new signals, propose changes
"Show my TASTES.md"Display current file with token count
"Why this choice?"Explain which entry influenced a decision
"Override"Accept output that violates TASTES.md
"Taste check"Audit current output against all entries

Boundaries

  • TASTES.md governs aesthetics only. Never override function, accessibility, or performance.
  • If taste conflicts with usability, surface the conflict. Never silently sacrifice either.
  • Never auto-update TASTES.md. Always propose and wait for approval.

Uninstall

If removed, delete the ## Aesthetic Judgment section from AGENTS.md. Do not delete TASTES.md — it belongs to the human.


Learn more: tastes.md · Manifesto · GitHub

License: MIT-0

平台分布

OpenClaw

81.69%
按下载量换算1,985

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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