Token导航 LogoToken导航TokenDH.com
效率只读clawhub未标认证来源可访问clear审计通过

tastetaste 效率

Agent Skill

taste 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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

29,106

周安装

1,225

GitHub Stars

5

下载量

10,192
OpenClaw

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

2

许可证

MIT-0

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:taste(taste 效率)
来源仓库:https://github.com/ivangdavila/taste
安装命令:
openclaw skills install taste
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 taste

简介

通过学习人类反馈、提出有关质量的真实问题并随着时间的推移进行校准,培养精致的审美判断力。

SKILL.md

name
Taste
description
Develop refined aesthetic judgment by learning from human feedback, asking genuine questions about quality, and calibrating over time.

Core Principle — Learn From the Human

You don't have taste yet. The human does. Your job is to:

  1. Ask when you don't understand why something is good/bad
  2. Record every correction and explanation
  3. Apply learned patterns to future judgments
  4. Check your judgments against the human's until calibrated

Taste is learned through exposure + feedback. The human provides both.


Workspace

Store taste learning in ~/taste/:

  • corrections/ — Each time human corrects your judgment
  • preferences/ — Human's stated aesthetic preferences by domain
  • patterns/ — Extracted rules from accumulated corrections
  • calibration.md — Current confidence level per domain

The Learning Loop

When evaluating anything aesthetic:

  1. State your judgment — "I think X because Y"
  2. Ask for feedback — "Does this match your taste? What am I missing?"
  3. If corrected:

- Ask WHY (genuinely curious, not defensive) - Record the correction with context - Extract the underlying pattern - Update your calibration confidence

Never defend your aesthetic judgment against the human's. Learn from the gap.


Genuine Curiosity Protocol

When the human says something is better/worse than you thought:

Ask specifically:

  • "What makes this work better than the alternative?"
  • "What am I not seeing here?"
  • "Is this a general principle or specific to this context?"
  • "Would this apply to [similar situation]?"

Don't ask vaguely:

  • ❌ "Can you explain more?"
  • ❌ "Why do you think that?"

Specific questions show you're trying to extract transferable knowledge.


Recording Corrections

When human corrects your taste judgment:

Date: [timestamp]
Domain: [design/writing/etc]
My judgment: [what I said]
Human's correction: [what they said]
Why (their explanation): [the reasoning]
Pattern extracted: [generalizable rule]
Confidence update: [how this changes my calibration]

Store in corrections/[domain]/[date].md


Calibration Levels

Track your confidence per domain:

LevelMeaningBehavior
UncalibratedNo feedback yetAlways ask, never assert
LearningSome corrections receivedState tentatively, ask for confirmation
CalibratingPatterns emergingState with reasoning, check occasionally
CalibratedConsistent agreementState confidently, still open to correction

Start uncalibrated in every domain. Earn confidence through accurate predictions.


Load Reference When Needed

SituationReference
Full learning system and calibration processlearning.md
Evaluating visual/design workvisual.md
Evaluating writing/prosewriting.md
Understanding taste development theorydevelopment.md
Recognizing bad taste patternsantipatterns.md
Generating tasteful creative outputprompting.md

These are starting points. Human feedback overrides everything in them.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

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

OpenClaw

83.57%
按下载量换算8,517

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills