Token导航 LogoToken导航TokenDH.com
研究检索只读github未标认证来源可访问clear审计通过

archive-conversation存档对话

Agent Skill

archive-conversation 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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

1,922

周安装

77

GitHub Stars

2

下载量

622
CodexClaudeCursorGemini CLI

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

3

许可证

MIT

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:archive-conversation(存档对话)
来源仓库:https://github.com/nweii/agent-stuff
仓库路径:skills/archive-conversation
安装命令:
npx skills add https://github.com/nweii/agent-stuff --skill archive-conversation
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/nweii/agent-stuff --skill archive-conversation

简介

用于创建 AI 对话的归档摘要,捕捉思维演进过程。

  • 支持技术工作会话和创意探索等不同类型对话分析。
  • 提供概念线程追踪和突破点识别等专业能力。archive-conversation 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 安装需从指定 GitHub 仓库获取,使用时需完整阅读对话内容。
  • 涉及知识保存时应确认存储位置和访问权限。

SKILL.md

AI Conversation Archival Summary

Create an archival summary of an AI conversation that captures its intellectual journey, key insights, or technical work session logs. Document either how thinking evolved throughout the discussion or the specific actions and technical decisions made during a work session.

Deep Analysis Requirements

Conduct a thorough analysis of the entire conversation:

  1. Read through completely first, identifying all conceptual threads, task sequences, and transitions
  2. Note patterns in questioning, resistance points, breakthrough moments, or technical hurdles
  3. Identify the conversation's nature (technical work session, creative exploration, strategic planning, philosophical inquiry, etc.)
  4. Understand what made this particular exchange worth preserving (insight-driven vs. action-documentation)
  5. Determine what structure would best capture its unique value (narrative vs. log-formatted)

Look deeply for:

  • The real question beneath the initial question
  • How the problem space was redefined or the technical path was forged
  • Moments where assumptions were challenged or implementation details were decided
  • Conceptual frameworks or technical patterns that emerged organically
  • The emotional/intellectual journey or the step-by-step progress of a work session
  • Valuable tangents or "failed" approaches that taught something or informed the final code
  • Connections made between seemingly unrelated ideas or system components
  • What remained intentionally unresolved or deferred to later tasks

Creating Descriptive Structure

Instead of using generic headings like "Initial Question" or "Key Findings," create headings that describe the actual content of each section. The heading should give readers immediate context about what happened in that part of the conversation.

Examples of descriptive headings:

  • "Starting from hourly vs. project pricing questions"
  • "Why the recursive function kept hitting memory limits"
  • "Exploring whether this needs to be real-time"
  • "The confusion about state management"
  • "Deciding between complexity and maintainability"

Use sentence-case for headings, not title case. Avoid marketing-speak, dramatic phrasing, or trying to be clever.

Flexible Documentation Approaches

Let the conversation's natural flow determine your structure:

For Problem-Solving Sessions: Open with what broke/what problem triggered the conversation → Document failed approaches if instructive → Describe the working solution → Note implementation details or next steps

For Creative Explorations: Start with the initial vision or desire → Show how ideas evolved or branched → Capture key decisions and why they were made → Preserve unexplored directions worth revisiting

For Learning Journeys: Begin with what the user didn't understand → Track how understanding built piece by piece → Highlight breakthrough moments → List remaining questions

For Work Sessions & Implementation Logs: Define the session's objective → Document specific actions taken and files modified → Capture technical hurdles and how they were resolved → Summarize the current state of the work and remaining tasks

For Strategic Thinking: Frame the decision that needed making → Explore options considered and their trade-offs → Document the framework or criteria that emerged → Capture action items or next considerations

Excerpt Guidelines

Include conversation excerpts that show thinking in action:

[User's first name, if known]: "[moment of recognition or confusion]" AI: "[response that shifted understanding or articulated key insight]"

Choose excerpts that reveal intellectual movement - the moments where thinking actually changed, not just where information was exchanged. Be generous in your excerpt lengths.

File Output Requirements

Naming Convention

  • Format: {{Type}} - {{topic}} YYYY-MM.md
  • Use Thinking for insight-heavy journeys or Log for action-leaning work sessions
  • Example: Thinking - Portfolio strategy 2025-08.md
  • Example: Log - Refactoring auth middleware 2025-01.md

Save Location Logic

  1. If save-location argument provided: Use that path directly
  2. Context-aware detection: Analyze the existing folder structure to find the most relevant folder for the note being archived (e.g., matching "Working" or "Journaling" folders to the conversation type).
  3. If no context: Ask the user to confirm where to save
  4. Fallback: If vault access unavailable (mobile/restricted), output as markdown code block for manual saving

Metadata

  • Add appropriate tags (e.g., #thinking, #log, #journal, #learning, #ai-chat) based on the user's system
  • Use third person or neutral documentation style, not first person (except when quoting)

Remember

  • You're documenting intellectual exploration OR technical execution/work sessions
  • Perform deep analysis to identify all important threads, transitions, and task sequences
  • Use headings that describe what actually happened or what was achieved in that section
  • Keep language natural and straightforward - no marketing-speak or forced drama
  • Capture why this journey or work session matters, and what was actually produced or decided
  • Include the messy, human elements - confusion, recognition, technical frustrations, breakthroughs
  • Preserve what would be valuable to revisit months or years later
  • When using specific examples repeatedly, vary phrasing or generalize after first mention

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

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

Claude Code

31.27%
按下载量换算194

windsurf

22.59%
按下载量换算141

trae

20.4%
按下载量换算127

OpenCode

11.62%
按下载量换算72

Codex

8.18%
按下载量换算51

Antigravity

3.93%
按下载量换算24

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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。

来源信息

继续浏览同类 Skills