chatbot-flow-design

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.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

85%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-organized, opinionated design playbook with excellent progressive disclosure and a clear sequenced framework, but it leans verbose and conceptual for a design skill, with abstract rather than executable guidance.

Suggestions

Tighten conciseness by collapsing the repeated scripted-bot/hallucinating-bot/structured-guided framing that appears in the intro, the dedicated section, and the closing into a single canonical statement.

Trim the voice/philosophy prose (e.g. 'The voice is the senior growth practitioner who has watched...') since a capable model needs the design rules, not the persona description.

Add at least one concrete, copy-paste-ready artifact — e.g. a minimal intent-architecture template or a fallback decision table — to lift actionability from named patterns toward executable guidance.

Dimension	Reasoning	Score
Conciseness	The body is largely efficient and well-structured, but the ~260 lines repeat the same three-way scripted/hallucinating/structured framing across the intro, a dedicated section, and the closing, and spend prose on voice/philosophy a capable model already understands.	2 / 3
Actionability	It offers concrete, named patterns (Named intents, RAG, clarifying-question fallback, escalation triggers) and a 12-item checklist, but as instruction-only design guidance it lacks executable examples or copy-paste specifics — guidance is actionable but mostly abstract.	2 / 3
Workflow Clarity	The 12-consideration framework gives an explicitly numbered, well-sequenced walkthrough, and the decision section pairs 'earn deployment'/'do NOT earn deployment' lists with a concrete litmus test, providing a clear sequence for auditing or designing a bot.	3 / 3
Progressive Disclosure	The body is a clear overview with each topic section ending in a one-level-deep pointer to a real references/ file (all 9 referenced files exist), plus a consolidated 'Reference files' listing for navigation.	3 / 3
	Total	10 / 12 Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that lists concrete capabilities, provides explicit trigger terms, answers both what and when, and actively disambiguates itself from neighboring skills. No notable weaknesses.

Dimension	Reasoning	Score
Specificity	The description enumerates concrete actions across the domain — 'Intent recognition architecture, branching logic, fallback handling, escalation to human, conversation analytics' — naming multiple specific capabilities rather than vague language.	3 / 3
Completeness	It clearly answers 'what' (designing conversational flows, the listed sub-tasks) and 'when' via the explicit 'Triggers on...' / 'Also triggers when...' clause, satisfying both halves.	3 / 3
Trigger Term Quality	It lists natural terms a user would say ('chatbot, conversational AI, AI agent, chat widget, intent design, conversational flow, bot escalation, LLM grounding') plus failure-state triggers ('when a chatbot is hallucinating'), giving broad natural-keyword coverage.	3 / 3
Distinctiveness Conflict Risk	It carves a distinct niche and explicitly distinguishes itself from adjacent skills ('Distinguishes chatbot DESIGN (this skill) from chatbot IMPLEMENTATION'), with triggers unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: rampstackco/claude-skills
Commit: f1bc195

Reviewed: 9 days ago

Table of Contents

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