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chatbot-flow-design

Agent skill fromrampstackco/claude-skills

Designing conversational flows for website chatbots and AI agents. Intent recognition architecture, branching logic, fallback handling, escalation to human, conversation analytics. Honest about scripted-bot (rigid trees, fail edge cases), hallucinating-bot (LLM without structure, makes things up), and structured-guided-conversation (LLM-powered with intent architecture and fallback discipline) patterns. Distinguishes chatbot DESIGN (this skill) from chatbot IMPLEMENTATION (engineering and platform work). Triggers on chatbot, conversational AI, AI agent, chat widget, intent design, conversational flow, bot escalation, LLM grounding. Also triggers when a chatbot is hallucinating, when a scripted bot is failing edge cases, or when a chatbot is being scoped for the first time.

How to install chatbot-flow-design

install chatbot-flow-design
# install the claude-skills pack with the skills CLI
npx skills add rampstackco/claude-skills

# or add only this skill to Claude Code: copy the folder

skills/chatbot-flow-design/

# into

~/.claude/skills/chatbot-flow-design/ (all projects)

.claude/skills/chatbot-flow-design/ (one project)

skills/chatbot-flow-design/SKILL.md

MITFull file
1# Chatbot Flow Design
2
3A senior growth practitioner's playbook for designing conversational flows for website chatbots and AI agents. Intent recognition architecture, branching logic, fallback handling, escalation to human, conversation analytics. The discipline of building a bot that knows what it knows and routes appropriately when it does not.
4
5Most chatbots on the web fail in one of two ways. Scripted bots break the moment a user phrases something the script did not anticipate; the user gets pushed through a decision tree that does not fit their situation. LLM-powered bots without structure hallucinate; they confidently answer questions about pricing, policy, or capabilities and frequently make up answers, creating support burden and trust damage.
6
7The chatbots that work do something different. They have an intent architecture that defines what the bot can and cannot handle. They ground their responses in a knowledge base so they do not invent facts. They have explicit fallback paths for unclear or out-of-scope intents. They escalate to humans cleanly when the bot's job is done. The audience trusts the bot because the bot is honest about its scope.
8…

Excerpt shown under the repository's MIT license.

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