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Chat Refiner

作者 Ash · GitHub ↗ · v0.1.0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install chat-refiner
功能描述
Refine conversation history by deleting useless/redundant/process exchanges. Keep explicit instructions, disciplines, important configs, skills learned (how)...
使用说明 (SKILL.md)

Chat Refiner

When to Use

User requests:

  • "精简聊天记录"
  • "clean/summarize history"
  • "update MEMORY.md from transcripts"
  • Memory maintenance during heartbeats.

Workflow

  1. Input: sessions_history (this session or other), memory/*.md, chat transcripts.
  2. Principles (see references/principles.md):
    • Delete casual/heartbeats/repeated tools.
    • Keep: disciplines, configs, skills learned/install, explicit "remember", decisions.
  3. Process:
    • Read raw.
    • Extract key.
    • Write summary to memory/YYYY-MM-DD-summary.md or MEMORY.md.
  4. Output: Clean file + "Refined X items kept".

Tools

  • sessions_history: fetch transcripts.
  • memory_search/get: prior context.
  • write/edit: output summary.

Examples

User: "精简对话" → read history → refine → write memory/summary.md.

Ref: principles.md

安全使用建议
This skill will read chat transcripts and write persistent memory/summary files — but its included principles explicitly tell the agent to keep and persist 'Configs (API keys, models)' and other potentially sensitive data. Before installing or using it, consider: (1) Do not run it on transcripts that contain secrets (API keys, tokens, passwords) unless you trust and have reviewed the output destination. (2) Edit the SKILL.md or principles to explicitly redact or exclude secrets (e.g., remove the 'Configs (API keys, models)' line and add a redaction step). (3) Require user confirmation before writing memory files or make it user-invocable only (avoid autonomous invocation on sensitive corpora). (4) After use, inspect the created memory files and delete any secrets you find. (5) If you need automated redaction, add explicit redaction rules to the workflow (detect common key patterns and remove them). These mitigations reduce the risk of accidental secret persistence; without them, the skill's behavior is suspicious and potentially dangerous.
功能分析
Type: OpenClaw Skill Name: chat-refiner Version: 0.1.0 The OpenClaw skill 'chat-refiner' is designed to summarize and clean chat history and memory files. All instructions in SKILL.md and references/principles.md align with this stated purpose, detailing how to read chat transcripts and write summarized content to memory files. There is no evidence of prompt injection attempts, unauthorized data access, exfiltration, or any other malicious or suspicious behavior. The file read/write operations are explicitly for the skill's core function, and instructions like 'Keep: Configs (API keys, models)' refer to preserving relevant information *within* the chat history, not accessing system-level configurations.
能力评估
Purpose & Capability
Name/description (refine chat history and produce MEMORY.md) matches the SKILL.md workflow and tools (sessions_history, memory_search/get, write/edit). However the included principles explicitly instruct the agent to 'Keep: Configs (API keys, models)', which is not something a typical 'chat refiner' legitimately needs to persist and is unexpected for a history-summary tool.
Instruction Scope
The SKILL.md tells the agent to read sessions_history, memory/*.md and chat transcripts and then write persistent summary files (memory/YYYY-MM-DD-summary.md or MEMORY.md). That is coherent for summarization, but the instructions also direct keeping and persisting sensitive items (API keys, model configs). There is no guidance to redact or protect secrets, and the skill gives the agent broad discretion to delete or retain items, which could lead to inadvertent persistence of secrets or sensitive context.
Install Mechanism
Instruction-only skill with no install spec, no binaries, and no code files — minimal installation risk.
Credentials
The skill declares no required environment variables or credentials, but the principles explicitly tell the agent to keep 'Configs (API keys, models)'. That is a mismatch: the skill does not request special access but its instructions encourage collecting and persisting secrets that are unrelated to the stated benign purpose and that should not normally be stored in memory files.
Persistence & Privilege
The skill is not always-enabled and does not request system-wide persistence or modification of other skills. It writes summaries to memory files (expected behavior), but combined with the instruction to retain secrets this persistent storage becomes sensitive — the privilege level itself (not always) is reasonable.
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 chat-refiner
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /chat-refiner 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
v0.1.0
Initial refine skill per Ash rules.
元数据
Slug chat-refiner
版本 0.1.0
许可证
累计安装 1
当前安装数 1
历史版本数 1
常见问题

Chat Refiner 是什么?

Refine conversation history by deleting useless/redundant/process exchanges. Keep explicit instructions, disciplines, important configs, skills learned (how)... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 332 次。

如何安装 Chat Refiner?

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

Chat Refiner 是免费的吗?

是的,Chat Refiner 完全免费(开源免费),可自由下载、安装和使用。

Chat Refiner 支持哪些平台?

Chat Refiner 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 Chat Refiner?

由 Ash(@sslisen)开发并维护,当前版本 v0.1.0。

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