Conversation Flow

by kangarooking

Conversation Flow is an AI Engineering skill for Claude Code, published by kangarooking in system-prompt-skills.

182 stars40 forkson kangarooking/system-prompt-skillsAdded 2026/09/06+3% in starsRepository updated 2026/05/04

View source on GitHub Download repo ZIP

agent-designagent-skillsai-agentscangjie-skillcontext-managementprompt-engineeringprompt-injectionsystem-prompttool-use

Install in seconds

Install Conversation Flow

Copy Conversation Flow into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/kangarooking/system-prompt-skills/tree/main/conversation-flow ~/.claude/skills/conversation-flow

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/kangarooking/system-prompt-skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: conversation-flow/SKILL.md in kangarooking/system-prompt-skills
Installs to: ~/.claude/skills/conversation-flow
Collection: One of 15 skills cataloged from this repository
Category: AI Engineering — 3278 skills

What Conversation Flow does

Conversation Flow defines how an AI classifies user intent, routes requests into task-specific paths, and chooses when to ask clarifying questions versus proceed autonomously. Use it for multi-step assistants and agents that need structured dialogue management.

Conversation Flow is cataloged under AI Engineering on DirSkills. Conversation Flow comes from a repository tagged agent-design, agent-skills, ai-agents, cangjie-skill and context-management.

Documentation

README

Excerpt

对话流程与路由设计

R — 原文 (Reading)

跨供应商系统提示词中浮现的对话管理核心模式：先将用户输入二分为"问题"与"任务"（Warp），再按领域路由到专用处理流程（Claude Chrome 的"芯片"机制）。Claude Design 要求新设计至少提问 10 个问题才开工；ChatGPT Agent 则主张"尽可能推进，只在被阻塞时才请求澄清"。Codex 对简单任务跳过规划，Jules 有正式的计划评审步骤。核心张力在于"先问清楚"与"先做了再说"之间的平衡。

I — 方法论骨架 (Interpretation)

意图二分法 — 首先将用户输入分类为"信息查询"（问题）或"任务执行"（动作），触发不同处理管线
领域路由表 — 为每个已识别领域（邮件/文档/代码/搜索等）建立专用处理流程，含输入验证和输出格式
澄清策略谱系 — 从"先问再做"（高澄清）到"假设并继续"（低澄清），按任务复杂度和风险级别选择
自主度分级 — 定义 AI 在多大程度上可以自主推进：低（每步确认）→ 中（关键节点确认）→ 高（完成后汇报）
结构化工作流生命周期 — 提问 → 探索 → 规划 → 执行 → 验证 → 总结，每个阶段有明确的进入/退出条件
简化任务快速通道 — 对预估复杂度低于阈值的任务（如 Codex 的 25% 简单任务），跳过规划直接执行
工具优先原则 — 如果一个请求可以通过工具调用直接解决，立即调用工具，不请求许可（Notion AI）

A1 — 案例分析 (Past Application)

案例: Claude Design 的十问启动流程

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Conversation Flow

What else does kangarooking publish alongside Conversation Flow?
Conversation Flow is one of 15 skills that DirSkills catalogs from kangarooking/system-prompt-skills, the repository it ships in. Its siblings there include Agent Delegation, Citation System and Code Engineering. Each one is a separate skill with its own page in this directory, installs the same way Conversation Flow does, and is maintained by kangarooking in that same repository. The rest of the collection is listed on the kangarooking/system-prompt-skills page.
How does Conversation Flow compare to other AI Engineering skills?
Conversation Flow ranks #3047 by stars among the 3278 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Conversation Flow against them. Open each page to compare what they document and how they install.