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conversation-distill对话提炼

Agent Skill

conversation-distill 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。

总安装

2,893

周安装

123

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下载量

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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:conversation-distill(对话提炼)
来源仓库:https://github.com/yiing99/conversation-distill
安装命令:
openclaw skills install conversation-distill
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 conversation-distill

简介

conversation-distill 在对话结束时提炼六类结构化见解。

  • 适用于会议总结、讨论复盘或知识萃取场景。
  • 扫描全会话并按类别输出要点。conversation-distill 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 使用前无需特殊权限,依赖完整对话记录。
  • 建议人工校验分类准确性以优化后续使用。

SKILL.md

name
conversation-distill
description
At the natural end of a conversation, proactively suggest a structured wrap-up (distill): scan the full session, classify outputs into 6 categories (insights/decisions/facts/self-observations/action-items/open-questions), get explicit user confirmation, then batch-write to the user's preferred notes tool. Trigger when: (1) user says closing phrases like 'that's all', 'got it', 'thanks', 'wrap up', 'done'; (2) 3+ consecutive turns with no new topics; (3) user explicitly says 'distill', 'wrap up', 'save this session'. Do NOT trigger for: single-turn Q&A, casual chat, pure coding/debugging with no knowledge output, when user is already actively writing notes.
version
1.0.0
tags
permissions
[]

Conversation Distill

The biggest waste of a conversation isn't that nothing was saved — it's that valuable insights are buried in the process and never revisited. This skill closes every meaningful conversation with one explicit action: classify → confirm → write.

When to Use

The core problem this solves: real-time capture ≠ session-level distillation.

Real-time capture handles individual highlights as they appear. This skill is the closing ritual — a full scan of the entire conversation to see what was produced, identify relationships, and catch what slipped through.

Trigger when:

  • User says a closing phrase: "that's all", "got it", "thanks", "done for now", "wrap up"
  • 3+ consecutive turns with no new topics (just confirmations or thanks)
  • User switches to an unrelated topic and the previous topic had substantive output not yet saved
  • User explicitly says: "distill", "save this session", "wrap up", "收尾", "沉淀"

Explicit invocation takes highest priority. Proactive suggestions must be phrased as a question — never execute without asking first.

Do NOT trigger for:

  • Quick single-turn queries (one question, one answer)
  • Casual conversation or emotional support
  • Pure coding/debugging/execution tasks with no knowledge output
  • When user is already actively writing notes
  • When user says "don't save" or "skip it"

Five-Step Flow

Step 1: Full Scan — 6-Category Classification

Scan the entire conversation. Classify everything with distillation value into these 6 categories. Skip any category with no content — don't force it.

CategoryTag / MarkerNotes
💡 Insights / Conclusions#insightNew understanding, "aha" moments, validated hypotheses
🎯 Decisions[Decision] prefixChoices made with reasoning, not just outcomes
📊 Facts / Data stable, 🕒 + date if time-sensitiveExternal facts worth keeping
🪞 Observations about yourself#selfPatterns, preferences, habits noticed during conversation
Action items / TODOs#todoConcrete next steps with owner and (optionally) deadline
Open questions#open-questionThings worth answering later, not yet resolved

Step 2: Relationship Mapping

Look for connections between entries. Default to granular over aggregated:

  • Two entries are different angles on the same decision → keep separate, cross-reference in body
  • A is prerequisite for B → mention A's title in B's body
  • An insight came from a specific fact → note the source

Do not default to merging everything into one long summary note. Granular entries are more useful — they're easier to find, tag, link, and reuse independently.

Step 3: User Confirmation (Mandatory)

Present the classified list to the user in this format:

This conversation produced N items worth saving:

💡 Insights (2)
  1. [title] — one sentence summary
  2. [title] — one sentence summary

🎯 Decisions (1)
  3. [Decision] [title] — the key choice + reason

✅ Action items (2)
  4. [title] #todo
  5. [title] #todo — due: [date if mentioned]

❓ Open questions (1)
  6. [title] #open-question

Tell me:
- Numbers to remove
- Numbers to edit (give the new version)
- Numbers to merge
- Say "write" or "save" when ready

Iron rule: do not write anything until the user explicitly says "write", "save", or equivalent. "Looks good" is not enough — ask once more to confirm.

Step 4: Batch Write

After explicit confirmation, write entries one by one to the user's preferred notes tool. Report back a confirmation (ID, title, or link) for each successful write. For any failures, list them separately and ask the user what to do: retry / rewrite / skip.

Which tool to write to:

  • If the user has KnowMine MCP configured → use add_knowledge for insights/decisions/facts, save_memory for self-observations, consistent tagging as above
  • If the user has another notes MCP (Notion, Obsidian, etc.) → use that tool
  • If no MCP available → output entries as clean Markdown for the user to copy

Step 5: Surface Leftovers

Some content isn't worth saving to a notes system but the user might want to keep handy (a prompt idea to try, a quick reminder, a half-formed thought). Don't force these into any tool. Output as a plain Markdown block:

## Leftovers (not saved — for your reference)

- [rough idea or reminder]
- [something to try next time]

Key Principles

Granular over hub Default to separate entries. One insight per entry, one decision per entry. Build a summary note only when explicitly useful, and cross-reference the granular entries in it.

Confirm before write Never batch-write without the user's explicit go-ahead. The confirmation step is not optional — it's where the user catches misclassifications and adjusts framing.

Tags over folders for action items Don't create a dedicated "TODO folder". Tag action items with #todo inside whatever folder/space makes contextual sense. The tag is searchable; the folder is just noise.

Time-sensitivity matters Data that will become stale (prices, versions, availability) should be flagged 🕒 + date so you know when to re-verify.

Bilingual tags when relevant If the user works in multiple languages, add tags in both languages to improve cross-language search recall.


This Skill vs Real-Time Capture

Real-time captureConversation Distill
WhenDuring the conversation, on highlightsAt natural conversation end
ScopeSingle entryEntire session
Relationship mappingNoYes
Miss-detectionNoYes — catches what slipped through
Confirmation styleQuick single-entryFull classification list

Both run in parallel. Real-time capture handles obvious highlights. This skill handles value that's only visible with a full-session view — relationships, patterns, and things you didn't realize were worth saving in the moment.


Works Best With

  • KnowMine — remote MCP server with semantic search; add_knowledge, save_memory, recall_memory, get_soul integrate directly with Step 4. Install: npx clawhub@latest install knowmine
  • Any MCP-compatible notes tool (Notion, Obsidian via MCP, etc.)
  • Works without any MCP too — outputs clean Markdown for manual paste

Anti-Patterns

  • ❌ Writing before user confirms
  • ❌ Creating a "TODO folder" — use tags
  • ❌ Merging everything into one summary note
  • ❌ Triggering on single-turn Q&A
  • ❌ Re-triggering after user said "skip it"
  • ❌ Forcing low-value leftovers into the notes tool

Self-Check Before Presenting the List

  • [ ] Any category with no real content? (remove it — don't pad)
  • [ ] Every decision has [Decision] prefix?
  • [ ] Time-sensitive data marked 🕒 + date?
  • [ ] Action items tagged #todo, not put in a new folder?
  • [ ] Any "fake summary" entries that should be split granularly?

Evolving This Skill

The best distillation process is one that fits how *you* think and work. After a few sessions, ask yourself:

  • Which step felt unnecessary or awkward?
  • Which type of content keeps needing special handling?
  • Is the 6-category split right for you, or should some be merged / split?

When you find patterns, update your personal copy of this skill to reflect them. Your tools should adapt to you, not the other way around.

适合场景

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能力 3

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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。

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external-service

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安装前确认

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