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remember-me记得我

Agent Skill

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。

总安装

41,885

周安装

1,711

GitHub Stars

6

下载量

13,551
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: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

简介

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。

SKILL.md

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

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

77.8%
按下载量换算10,543

安全审计

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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

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