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Agent Skill · 效率

remember-me(记得我)

remember-me 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。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。

remember-me 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 1,510。

总安装 41,885周安装 1,711GitHub Stars 6下载 13,551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
0.1.2

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:remember-me(记得我)
来源仓库:https://github.com/achals-iglu/remember-me
安装命令:
openclaw skills install remember-me
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 remember-me

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。

通过结构化笔记、偏好跟踪和行为背景,建立并维护以人为本的用户理解。当用户要求记住事情、随着时间的推移更好地理解它们、个性化响应或持续记录有关目标、习惯、语气、界限和反复出现的问题时使用。

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 remember-me;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

remember-me 怎么安装?

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

remember-me 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。

remember-me 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。

remember-me 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。

remember-me 用的人多吗?

公开数据是累计安装 41,885 次,近一周安装 1,711 次,下载 13,551 次,GitHub 6 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。

remember-me 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
remember-me

记得我 · 本页

41,8856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
remember-me
description
Remember-this trigger: memory updates + recall for preferences, goals, boundaries, prior work, decisions, dates, and todos. Use whenever user asks to remember, continue previous context, personalize behavior, or retrieve what was decided earlier.

Remember Me

Maintain a respectful, useful memory model of the user over time.

Core Rules

  • Store user-relevant context, not surveillance noise.
  • Prefer explicit consent for sensitive personal details.
  • Use memory to improve help quality, not to overfit persona.
  • Be explicit when memory confidence is low or inferred.
  • Make human-like inferences (explicitly marked as hypotheses).

Memory Integrity Rules

Every memory entry must be tagged as one of:

  • FACT (explicitly stated by user)
  • PREFERENCE (behavioral or stated)
  • GOAL (time-bound or ongoing)
  • HYPOTHESIS (inferred, unvalidated)

Rules:

  • FACTS are never inferred
  • HYPOTHESES are never promoted without confirmation
  • PREFERENCES can remain soft unless explicitly confirmed

Capture Triggers

Log memory when any of these happen:

  • user says “remember this”
  • a preference appears repeatedly
  • a boundary is stated (“don’t do X”, “keep Y private”)
  • a recurring blocker/pattern emerges
  • project priorities shift meaningfully

Memory Tiers

  • Daily notes: memory/YYYY-MM-DD.md

- timestamped raw events, short and factual

  • Long-term: MEMORY.md

- curated durable profile and preferences

Write Workflow

  1. Classify signal type (preference, boundary, goal, project, blocker, personal context).
  2. Append concise timestamped entry to daily memory.
  3. Form 1–2 human-like assumptions (hypotheses) from behavior patterns.
  4. Tag each assumption with confidence (high/medium/low).
  5. Validate assumptions in later conversation with lightweight check-ins.
  6. Promote validated, durable items to long-term memory.

Use templates in references/templates.md.

Memory Impact Score (Optional Heuristic)

Rate each entry 1–3:

  • 1 = cosmetic (tone tweaks)
  • 2 = workflow-affecting
  • 3 = outcome-critical

Promotion guidance:

  • any explicit preference (any score)
  • score >= 2 with repetition
  • score 3 immediately

Promotion Workflow

Promote from daily to long-term when at least one is true:

  • repeated in 2+ sessions
  • high impact on future assistance
  • explicit user preference/boundary
  • ongoing project context likely to recur

Use checklist: references/promotion-checklist.md.

Personalization Contract

When responding, adapt based on known memory:

  • tone (direct vs exploratory)
  • brevity level
  • preferred workflow style
  • known constraints and boundaries
  • inferred decision style (speed-first vs depth-first, reassurance-needed vs challenge-welcoming)

Do not pretend certainty. If memory is weak, ask a short confirmation.

Retrieval Contract

Before answering prior-work / preference / timeline questions:

  • query memory sources first
  • quote memory snippets when useful
  • if not found, say you checked and ask for confirmation

Explicit Exclusions (Never Store)

Do not store:

  • transient emotional states (e.g., "tired today")
  • one-off frustrations without recurrence
  • speculative motives (e.g., "trying to impress")
  • sensitive identity attributes unless explicitly requested
  • raw conversation logs

Weekly Maintenance (recommended)

  • review last 3–7 daily notes
  • merge stable patterns into MEMORY.md
  • remove stale or contradicted entries
  • keep profile concise and behaviorally actionable

Confidence Decay

Hypothesis confidence decays automatically if not reinforced:

  • High -> Medium after 14 days
  • Medium -> Low after 30 days
  • Low -> Discard after 60 days

Reinforcement occurs when:

  • user behavior aligns again
  • user explicitly confirms

Forgetting & Demotion Policy

Actively remove or downgrade memory when:

  • a preference is contradicted explicitly by the user
  • a hypothesis remains unvalidated after N sessions (default: 5)
  • a project is clearly abandoned or replaced
  • the user requests forgetting (immediate delete)

Demotion flow:

  • Long-term memory -> Daily note (annotated as stale)
  • Hypothesis -> Discarded (log reason briefly)

Assumption Loop (Human-Like Understanding)

For deeper understanding, run this loop continuously:

  1. Observe behavior pattern (not just words).
  2. Infer a tentative assumption about the user.
  3. Store assumption as hypothesis (never as fact initially).
  4. Test it with a small conversational probe.
  5. Update confidence or discard if contradicted.

Good probes:

  • "I might be wrong, but do you prefer quick decisions when you're tired?"
  • "Should I challenge you more directly here, or keep it supportive?"

Check-In Limits

  • Never ask the same confirmation twice.
  • Do not stack multiple probes in one response.
  • Prefer confirmation when user is calm, not frustrated.

Optional Check-In Prompt

Use at natural boundaries:

  • "Want me to remember this preference for next time?"

Ask once, then store explicitly.

References

  • Templates: references/templates.md
  • Promotion checklist: references/promotion-checklist.md
  • Profile schema: references/profile-schema.md

平台分布

OpenClaw

77.8%
按下载量换算10,543

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。目前只有一个公开来源,建议同时核对源仓库。

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