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mrgyan

Auto Dream Light

作者 mrgyan · GitHub ↗ · v0.1.1 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install auto-dream-light
功能描述
Lightweight, memory-safe Auto Dream workflow for OpenClaw that consolidates recent notes into existing memory files without replacing the user’s current memo...
使用说明 (SKILL.md)

Auto Dream Light

A lightweight, memory-safe Auto Dream skill for OpenClaw that works with the memory system the user already has.

What this skill is for

Use this skill when the user wants:

  • on-demand memory consolidation
  • a lightweight “dream” workflow
  • a dream log with execution history
  • a gradual path from manual to semi-automatic memory cleanup
  • memory consolidation without replacing the current MEMORY.md structure

This skill is intentionally conservative:

  • it does not rebuild the memory architecture
  • it does not introduce dashboards, indexes, or archives by default
  • it does not assume the user's memory follows a template
  • it prefers small, explicit, high-value updates

Why install it

  • works with existing MEMORY.md and memory/
  • makes conservative, incremental updates
  • avoids noisy or fake “productivity” output
  • keeps runs easy to review and audit
  • supports a clean path from manual runs to semi-automatic operation

Core idea

Instead of forcing a new memory system, this skill works with the one the user already has.

Default approach:

  1. scan recent daily logs
  2. extract durable, high-value information
  3. route items to the correct destination
  4. update memory incrementally
  5. record the run in a dream log
  6. return a concise summary to the user

Read these references when needed

  • Read references/adapted-plan.md before designing or changing the workflow.
  • Read references/manual-run.md when running the workflow manually.
  • Read references/semi-auto.md when the user wants a fixed trigger-based flow or wants to prepare for cron later.

Default file roles

Typical targets:

  • MEMORY.md → long-term facts, stable preferences, system conclusions, reusable lessons
  • memory/projects/** → project-specific context
  • memory/system/auto-dream-log.md → dream execution history only
  • memory/YYYY-MM-DD.md → raw daily notes; only add a consolidation marker when appropriate

Operating rules

  • Preserve the existing memory structure.
  • Prefer project files over stuffing everything into MEMORY.md.
  • Skip low-value chat, tests, and one-off noise.
  • Do not invent memory just to make a run look productive.
  • If there is nothing worth consolidating, explicitly skip and say so.

Trigger guidance

Typical trigger phrases:

  • "整理记忆"
  • "dream now"
  • "跑 dream"
  • "做一次记忆整理"
  • "跑一次适配版 auto dream"

If the user says dream details, show recent dream-log history instead of running a new consolidation.

安全使用建议
This skill appears coherent and low-risk, but before enabling it: (1) run it manually on a copy of your memory directory to verify behavior; (2) review and approve any file changes before committing; (3) check your git configuration so commits won't be automatically pushed to a remote you don't intend; (4) ensure that sensitive secrets are not stored in the memory files the skill will scan; and (5) prefer manual or semi-auto triggers until you trust its extraction/deduplication choices.
能力评估
Purpose & Capability
Name/description claim a conservative memory-consolidation workflow and every declared requirement matches that: no binaries, no env vars, no installs. The files referenced (MEMORY.md, memory/**/*.md, memory/system/auto-dream-log.md) are directly related to the described purpose.
Instruction Scope
Runtime instructions are limited to scanning recent daily logs, extracting items, routing them into existing memory files, appending a dream-log entry, and optionally committing changes. This is within scope, but the spec is deliberately high-level (relies on the agent's judgment for 'candidate' selection and deduplication). The commit step implies use of VCS if present — the skill does not mention push behavior, so users should confirm commit/push settings.
Install Mechanism
Instruction-only skill with no install spec and no code files — nothing will be written or downloaded during installation. This is the lowest-risk install profile.
Credentials
No environment variables, credentials, or config paths are required. The skill only references project-local files under memory/ and MEMORY.md, which aligns with its function.
Persistence & Privilege
always:false and default model invocation are unchanged. The skill does not request persistent elevated privileges or modify other skills. Its intended writes are local memory files and a dream-log, which are appropriate for its goal.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install auto-dream-light
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /auto-dream-light 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.1
Streamlined the package for a cleaner ClawHub release. Removed non-essential README files, kept the skill focused on SKILL.md plus targeted references, refreshed the ClawHub-facing copy, and bumped the version to 0.1.1.
v0.1.0
Initial public release of a lightweight, memory-safe Auto Dream workflow for OpenClaw. Preserves existing MEMORY.md and memory/ structure, supports manual and semi-automatic consolidation, keeps updates conservative and auditable, and includes focused references for adapted planning, manual runs, and trigger-based operation.
元数据
Slug auto-dream-light
版本 0.1.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

Auto Dream Light 是什么?

Lightweight, memory-safe Auto Dream workflow for OpenClaw that consolidates recent notes into existing memory files without replacing the user’s current memo... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 89 次。

如何安装 Auto Dream Light?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install auto-dream-light」即可一键安装,无需额外配置。

Auto Dream Light 是免费的吗?

是的,Auto Dream Light 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Auto Dream Light 支持哪些平台?

Auto Dream Light 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Auto Dream Light?

由 mrgyan(@mrgyan)开发并维护,当前版本 v0.1.1。

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