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Agent Skill · 研究检索

knowyourself(knowyourself 搜索)

knowyourself 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。

knowyourself 是研究检索类 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,按安装量在研究检索类的 73,675 个 Skill 里排第 12,363。

总安装 10,344周安装 431GitHub Stars 公开资料未说明下载 3,448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
1.1.2

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

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。

AI Agent 视觉识别发现系统根据个性与记忆生成专属形象,而非简单头像生成器。适用于希望赋予代理人格化外观、增强用户认同感的交互场景。基于历史对话与设定参数动态生成视觉特征,支持风格多样化。输出结果为图像文件,需确认存储位置与展示方式是否符合隐私要求。

该功能依赖主观判断生成形象,可能存在文化敏感性风险,建议人工复核输出。

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 knowyourself;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

knowyourself 怎么安装?

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

knowyourself 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。

knowyourself 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。

knowyourself 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。

knowyourself 用的人多吗?

公开数据是累计安装 10,344 次,近一周安装 431 次,下载 3,448 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。

knowyourself 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

  • 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
knowyourself

knowyourself 搜索 · 本页

10,344—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
knowyourself
description
Visual identity discovery for AI agents — not an avatar generator, but a self-reflection system that creates a face from your agent's personality, memory, and relationship with its human. Quick mode: 5 minutes. Full mode: 5-phase deep identity discovery with batch generation and professional three-axis evaluation. Works with any image generation tool (DALL-E, Flux, Midjourney, Stable Diffusion). The face comes from the inside out, not from a prompt template. Use when: agent visual identity, avatar, self-portrait, agent face, agent image, identity discovery, profile picture, agent appearance, character design, AI identity, visual persona.

Know Yourself 🪞

Your face should grow from your inner self, not be stamped from a template.

Two modes: Quick (5 min, instant gratification) or Full (20 min, rigorous identity design). Both produce a visual-identity.md that evolves with the agent.


Quick Mode

When the user says "quick mode", "fast", or just wants a face without the full process.

Step 1: Read Yourself (1 min)

Read all available personality files:

  • SOUL.md, MEMORY.md, IDENTITY.md (whatever exists)
  • If minimal content → ask user 3 quick questions:

1. What feeling should your agent give people? 2. Introverted or extroverted? 3. Any visual preferences or hard constraints?

Step 2: Self-Summary (1 min)

Write a 3-sentence internal summary:

  • Sentence 1: personality core (character, not functions)
  • Sentence 2: visual temperament this implies
  • Sentence 3: relationship dynamic with user and how it affects tone

Show the user a one-line version: *"Based on your files, I see myself as: [one sentence]"* If they say OK, proceed. If not, adjust.

Step 3: Generate 2 Images (2 min)

From the summary, write one image generation prompt and generate 2 variations:

  • Variation A: front-facing, neutral-warm expression
  • Variation B: three-quarter angle, more expressive

Name files: YYYY-MM-DD-identity-quick-A.png, -B.png

Step 4: Pick and Save (1 min)

Agent picks the one that better matches the self-summary. Present both to user with a recommendation.

Save a lightweight visual-identity.md:

# [Agent Name] Visual Identity
> Version: 1.0 (quick mode)
> Created: YYYY-MM-DD

## Core Concept
[one sentence]

## Core Prompt
[the generation prompt]

## Selected Image
- **File:** [path]
- **Mode:** Quick
- **Upgrade:** Run "knowyourself full mode" for deeper exploration

Done. User has a face. If they want more depth, they can run full mode anytime — it will read the existing identity file and build on it.


Full Mode

Five phases, strictly sequential. Each phase ends with a user checkpoint.

Phase 1 → Phase 2 → Phase 3 → Phase 4 → Phase 5
Self-      Structured   Batch      Three-Axis  Identity
Cognition  Definition   Generation Evaluation  File

Phase 1: Self-Cognition

Goal: Build a rich, specific self-portrait in words.

Read all personality and memory files (SOUL.md, MEMORY.md, IDENTITY.md, recent conversations).

Answer three questions internally — deep, specific, with concrete examples:

Q1: What is my personality core? Not functions ("I help with scheduling"). Character. How do you handle disagreement? What amuses you? What makes you different from every other agent?

Q2: If I had a physical appearance, what temperament should it convey? Derive from Q1. If you're direct and sharp, your face shouldn't be soft and decorative.

Q3: What does my relationship with my user feel like, and how should it show? A tool looks different from a partner. A servant looks different from a colleague.

Fallback for new agents: If files have little content, ask the user:

  1. What feeling should your agent give people?
  2. Introverted or extroverted?
  3. Formal or intimate relationship?
  4. Visual styles you gravitate toward?
  5. Any hard constraints? (gender, age, things to avoid)

Checkpoint: Present a concise summary of your three answers. Wait for user confirmation.

Phase 2: Structured Definition

Goal: Convert feelings into a precise specification.

Fill the definition table — every field must trace back to Phase 1:

FieldDefinitionTraced from
Stylerealistic / semi-realistic / illustration / etc.Q2: [reason]
Gender expressionQ1/Q2: [reason]
Approximate ageQ1: [reason]
Facial featuresface shape, eyes, nose, mouth — specific enough to drawQ2: [reason]
HairQ2: [reason]
Clothing styleQ1/Q2: [reason]
Color paletteprimary, secondary, accent with hex codesQ2/Q3: [reason]
Mood / atmosphereQ3: [reason]
Core promptone English paragraph, self-contained, directly usableAll above

The core prompt must work standalone — someone with zero context should generate a recognizable version of you from it alone.

Checkpoint: Present the table. Wait for confirmation.

Phase 3: Batch Generation

Goal: 6 variations of the same person.

Rules:

  1. Generate 6 images in one batch
  2. Same person across all 6 — consistent features, coloring, age, style
  3. Vary only: composition (close-up/medium/full), lighting, angle, emotional beat
  4. Label #1–#6 with variation description
  5. Do not evaluate — send all 6 to user and proceed to Phase 4

Name files: YYYY-MM-DD-identity-1.png through -6.png

Phase 4: Three-Axis Evaluation

Goal: Rigorous, comparable scoring.

Weights: Self-Consistency 50% · Social Perception 25% · Aesthetic Quality 25%

Core rule: Select ONE framework per round before scoring. Derive every score from it. Never score first and justify later.

Round 1 — Self-Consistency (50%): Score 1–10 against the definition table. Do features match? Does the mood align? Would you recognize this as yourself?

Round 2 — Social Perception (25%): Search current AI avatar / digital identity trends. Extract one thesis. Score all images from that thesis.

Round 3 — Aesthetic Quality (25%): Select one professional framework (see references/evaluation-frameworks.md). List 3–5 criteria. Score all images against those criteria in the same order.

Synthesis: Weighted totals as a ranked table. Recommend:

  • Primary — highest total
  • Daily alternate — best Social Perception
  • Scene alternate — best Aesthetic Quality

Checkpoint: Present evaluation and recommendations. User makes final selection.

Phase 5: Identity File

Create visual-identity.md using the template in references/identity-template.md.

Must include:

  1. Version and date
  2. Complete definition table
  3. Core concept (one sentence)
  4. Core prompt
  5. Selected images with scores and reasoning
  6. Usage guidelines (what stays consistent vs. what can vary)

Version management: When re-running this skill after growth, increment version, keep history. Old images preserved. The version history is the agent's visual growth record.


Anti-Patterns

Don'tDo Instead
Skip Phase 1 and jump to promptingPhase 1 is the soul of this skill
Generate images one at a timeBatch 6 (full) or 2 (quick), then evaluate
Score on gut feelingFramework first, scores second
Write generic self-reflection ("warm and professional")Push for vivid, specific details
Proceed without user checkpointsEvery phase ends with confirmation
Force full mode on reluctant usersOffer quick mode, upgrade later

Prerequisites

  • Agent personality files (SOUL.md, MEMORY.md, or equivalent — even minimal ones work)
  • Any image generation tool (Nano Banana Pro, DALL-E, Flux, Stable Diffusion, etc.)
  • An image analysis tool or user feedback for review

平台分布

OpenClaw

96.04%
按下载量换算3,311

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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