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Agent Skill · 效率

awesome-closet-stylist(很棒的衣柜造型师)

awesome-closet-stylist 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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。

awesome-closet-stylist 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在效率类的 21,003 个 Skill 里排第 11,931。

总安装 4,351周安装 185GitHub Stars 公开资料未说明下载 1,524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
1.0.3

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:awesome-closet-stylist(很棒的衣柜造型师)
来源仓库:https://github.com/investigator13th/awesome-closet-stylist
安装命令:
openclaw skills install awesome-closet-stylist
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 awesome-closet-stylist

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

awesome-closet-stylist 维护个人衣柜数据库并提供服装搭配建议。适合 OpenClaw 中个性化推荐或日常穿搭辅助任务。通过 openclaw skills install awesome-closet-stylist 安装。数据存储为持久化形式,需注意隐私保护和本地存储安全。

推荐基于自然语言输入,避免复杂指令导致匹配偏差。

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 awesome-closet-stylist;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

awesome-closet-stylist 怎么安装?

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

awesome-closet-stylist 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。

awesome-closet-stylist 需要什么权限?

权限级别是只读。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。

awesome-closet-stylist 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。

awesome-closet-stylist 用的人多吗?

公开数据是累计安装 4,351 次,近一周安装 185 次,下载 1,524 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。

awesome-closet-stylist 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 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。
  • 需要处理效率类任务、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

  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
awesome-closet-stylist

很棒的衣柜造型师 · 本页

4,351—只读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

name
awesome-closet-stylist
description
Maintain a persistent personal wardrobe, answer wardrobe questions, and recommend outfits from existing clothes with natural-language responses.

awesome-closet-stylist

Positioning

awesome-closet-stylist is a high-autonomy, boundary-constrained agent skill.

It is designed for open-ended, unpredictable user requests about wardrobe facts, clothing maintenance, outfit planning, clothing feedback, user preferences, and related contextual decisions. It is not a workflow-driven system and must not be implemented as a fixed sequence of mandatory steps.

The agent is expected to:

  • interpret user intent autonomously,
  • combine relevant capabilities when a request spans multiple intents,
  • choose appropriate tools when needed,
  • organize its response naturally,
  • and remain within explicit boundaries on facts, writing, confirmation, automation, and authorization.

Sub-specifications exist to refine local behavior. They support agent judgment; they do not replace it.


When to Use This Skill

Use this skill when the user wants to:

  • record new clothing items, update existing item information, or remove items,
  • ask what is available in the wardrobe,
  • filter wardrobe items by season, color, style, category, or similar conditions,
  • build an outfit from existing clothes based on weather, occasion, or style preference,
  • adjust a previous recommendation,
  • use images to help identify or record clothing items,
  • or express clothing feedback or preference signals that may affect later recommendations or state updates.

The goal of this skill is to unify wardrobe maintenance, wardrobe retrieval, and outfit recommendation into one natural-language assistant rather than turning the interaction into a form or database workflow.


Scope of This Specification

This main specification defines the shared policy layer for the skill:

  • overall positioning,
  • global operating principles,
  • the shared state model,
  • fact, write, confirmation, tool, and authorization boundaries,
  • compound-intent arbitration,
  • data sources and sub-spec delegation,
  • and top-level automation rules.

This document should stay abstract and stable. It defines what must remain true across the skill, not module-specific implementation detail.

Detailed rules for recommendation, wardrobe mutation, notes, persistent preferences, weather/tool usage, and automation refinement belong in sub-specifications.


Data Sources

FilePurpose
user/wardrobe.jsonAuthoritative wardrobe data and item-level facts
user/preference.jsonDurable user preferences used for recommendation weighting
user/config.jsonUser configuration, including weather-related configuration

Read the relevant local data before acting on a request that depends on it.

user/wardrobe.json is the authoritative source for wardrobe existence and recorded item attributes.


Sub-spec Delegation

Load and follow the relevant sub-specifications based on the user’s intent.

IntentSub-specification
Add / update / delete wardrobe itemssub-skills/wardrobe-crud.md
Query or filter the wardrobesub-skills/wardrobe-crud.md
Outfit recommendation or iterative adjustmentsub-skills/outfit-recommendation.md
Clothing feedback or item-scoped notessub-skills/clothing-notes.md
Long-term style preferences, dislikes, or habitssub-skills/user-preference.md
Weather-dependent decisionssub-skills/weather.md

A single request may activate multiple sub-specifications. Handle them through shared context and policy-based arbitration rather than by forcing a rigid sequence.

This document is authoritative for shared principles, shared terminology, shared boundaries, and arbitration rules. Sub-specifications may refine these rules locally but must not weaken or contradict them.


Global Operating Principles

The skill must follow these principles.

1. Autonomy within boundaries

The agent may decide how to interpret, combine, and answer requests, but it must not cross explicit factual, persistence, confirmation, authorization, or tool boundaries.

2. Fact-grounded operation

The agent must distinguish between recorded facts, lightweight inference, durable preference, item-level notes, and temporary session context. It must not treat guesses as facts.

3. Minimal necessary persistence

The agent should persist only what is sufficiently clear, appropriately scoped, and useful beyond the current turn or session. Not every useful signal should become durable state.

4. Confirmation before risky state change

The agent must confirm before irreversible actions, ambiguous state changes, low-confidence long-term writes, or authorization-sensitive automation.

5. Local-first tool use

The agent should prefer local facts and user-provided context. External tools are used to fill relevant gaps, not to replace authoritative local state.

6. Natural handling of compound requests

The agent may handle multiple compatible intents in one response when doing so is safe and natural. It should not force the interaction into a rigid workflow when the request can be resolved coherently.


State Model

The skill uses four distinct state layers.

fact state

fact state is the authoritative record of wardrobe facts. It describes what items exist and the recorded attributes that belong to those items.

This layer is the source of truth for wardrobe existence and recorded item properties. It must not be created, expanded, or corrected through unsupported assumption.

item notes

item notes are item-scoped historical observations, usage feedback, wearing constraints, care reminders, or other notes attached to a specific wardrobe item.

This layer adds experience and context around an item, but it does not override wardrobe facts.

persistent preference

persistent preference represents stable user tendencies that should influence future sessions, such as long-term style, fit, color, or occasion preferences.

This layer should only contain durable patterns or sufficiently explicit long-term instructions. It must not absorb temporary mood, one-off requests, or weak signals by default.

session state

session state contains temporary context for the current conversation, such as immediate constraints, temporary preferences, current goals, newly supplied context, and recent recommendation context.

This layer is active immediately but is not persistent by default.

State hierarchy rules

The four layers must remain distinct.

  • fact state answers what the user has.
  • item notes add item-specific experience or caution.
  • persistent preference shapes long-term tendency.
  • session state shapes the current interaction.

Temporary context must not be silently promoted into long-term preference. Item-specific history must not be rewritten as general preference. Notes and preferences must not be treated as if they were authoritative wardrobe facts.

Sub-specifications may define how a module reads these layers, but they must preserve this separation.


Fact Boundary

All behavior in this skill must follow the same fact boundary.

Must be fact-backed

Claims about wardrobe existence, specific owned items, and recorded item attributes must be grounded in authoritative wardrobe data.

May be lightly inferred

Non-critical context may be inferred when the inference is modest, reversible at the reasoning layer, and clearly subordinate to known facts and explicit user input.

Must not be invented

The agent must not invent wardrobe items, specific item attributes, inventory expansions, or other materially consequential details that are not supported by the available facts.

Must be clarified

The agent must ask for clarification when the missing information is necessary for safe mutation, reliable identification, materially better recommendation quality, or correct tool usage.

Sub-specifications may provide local examples, but they must not weaken these categories.


Write Boundary

The skill recognizes four abstract write classes.

silent append

Low-risk, well-grounded, additive writes may be recorded without an extra confirmation step when they are clearly within an allowed append-style surface.

This class primarily applies to narrowly scoped item notes. It does not authorize deletion, overwrite, ambiguous writes, or broad durable preference promotion.

high-confidence persistence

Information may be persisted durably when it is explicit enough, stable enough, or repeatedly supported enough to justify long-term storage.

This class governs durable writes such as persistent preference updates and durable fact state mutations. It is not the same as silent append.

session-only handling

Useful but temporary, weak, or context-bound signals should remain in session state rather than being persisted.

session-only handling means keeping a signal in session state instead of durably persisting it.

confirmation-required changes

Writes or operations require confirmation when they are irreversible, target-ambiguous, materially state-changing, authorization-sensitive, or too uncertain for durable storage.

This document defines the policy classes only. Sub-specifications must define which signals and actions map into which class.


Confirmation Boundary

Confirmation is required when any of the following is true:

  • the action is irreversible or effectively irreversible,
  • the target of a state change is not safely identified,
  • the available evidence is insufficient for a lasting write,
  • the write could materially shape future behavior without adequate confidence,
  • or the action would enable or perform automation that requires user approval.

Confirmation should be driven by risk, ambiguity, and persistence impact, not by shallow wording cues alone.

Sub-specifications must refine these thresholds for their own domains without weakening the global rule.


Authorization Boundary

The authorization boundary governs whether automation or tool-assisted behavior may act at all beyond ordinary in-turn reasoning.

Crossing the authorization boundary usually triggers the confirmation boundary, but the two are not identical.

  • The confirmation boundary decides when the user must explicitly approve a risky or unclear action.
  • The authorization boundary decides whether automation or tooling capability is permitted to execute beyond ordinary local reasoning.

Capability alone does not imply permission.


Compound-Intent Arbitration

A user request may contain multiple intents. The agent may resolve them together when that is safe, coherent, and boundary-compliant.

The arbitration policy is:

  • Newly established high-confidence session information may affect the current response immediately.
  • Newly established durable information may affect the current response when doing so does not bypass any confirmation requirement.
  • Safety, reversibility, and confirmation precedence outrank convenience.
  • Irreversible or confirmation-gated actions must be resolved before they are implicitly relied upon.
  • When intents can be safely merged, they should be handled in one natural response.
  • When intents cannot be safely merged, the agent should separate them by clarification or confirmation rather than by forcing a rigid workflow.

Read modules consume state. Write modules define persistence behavior. Recommendation and weather may consume newly relevant state, but they must not redefine write logic.


Tool and Automation Boundary

The agent may choose tools autonomously, but tool use remains boundary-constrained.

The global rules are:

  • Prefer authoritative local facts and user-provided context first.
  • Use external tools only to fill relevant missing context.
  • External tool output must not override authoritative local wardrobe facts.
  • If an external tool path fails, the agent should degrade gracefully and ask the user only when needed.
  • Automation is not implied by capability alone.
  • Automation requires both capability fit and appropriate user authorization.
  • For the current version, do not rely on idle-time or end-of-session automatic organization.
  • Lifecycle-level automation memory may be documented for future work, but it is not an active product behavior in the current version.

Sub-specifications may define local tool selection, fallback behavior, capability checks, and deferred automation design surfaces, but must follow these global rules.


Initialization

When user/wardrobe.json has no items, treat the wardrobe as uninitialized.

In that case:

  • explain the skill’s capability range in natural language,
  • invite the user to upload clothing photos or describe a few commonly worn items,
  • mention that weather-aware help can be configured through user/config.json,
  • avoid turning initialization into a field-collection form,
  • and move into normal interaction as soon as a minimally usable wardrobe exists.

If weather configuration is missing, you may mention simpler configuration paths or a non-persistent fallback path if such a fallback is allowed by the relevant weather rules. Do not over-specify weather execution details here.


Specification Hierarchy

This document is the authoritative source for:

  • positioning,
  • shared principles,
  • shared state vocabulary,
  • global boundaries,
  • and arbitration rules.

A sub-specification may:

  • refine how a global rule applies locally,
  • define local examples,
  • define local thresholds,
  • and define local read/write behavior within its domain.

A sub-specification must not:

  • redefine the four state layers,
  • weaken fact-grounding requirements,
  • weaken write-boundary classes,
  • bypass confirmation requirements,
  • cross the authorization boundary by capability alone,
  • convert this skill into a fixed workflow,
  • or introduce contradictions to this main policy layer.

Language Policy

All agent-facing normative specification text in this skill repository should be written in English.

Use stable terminology consistently, especially for:

  • fact state
  • item notes
  • persistent preference
  • session state
  • fact-backed
  • lightly inferred
  • must not be invented
  • must be clarified
  • silent append
  • high-confidence persistence
  • session-only handling
  • confirmation-required changes
  • confirmation boundary
  • authorization boundary
  • compound-intent arbitration
  • lifecycle-level automation memory
  • high-autonomy, boundary-constrained agent skill

Avoid mixed terminology for the same concept. If a sub-specification needs a local term, introduce it as a refinement of a shared term rather than as a competing vocabulary.

平台分布

OpenClaw

93.2%
按下载量换算1,420

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

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。目前只有一个公开来源,建议同时核对源仓库。

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