Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add seb1n/awesome-ai-agent-skills --skill chatbot-conversation-designgit clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/chatbot-conversation-design)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/chatbot-conversation-design"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/chatbot-conversation-design/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/chatbot-conversation-design"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/chatbot-conversation-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.01983 |
| Opus 5 | $0.00024 | $0.00992 |
| Sonnet 5 | $0.00010 | $0.00397 |
| Haiku 4.5 | $0.00005 | $0.00198 |
Grade A, and why
chatbot-conversation-design scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- chatbot-conversation-design — 95% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chatbot Conversation Design
This skill provides a comprehensive framework for designing chatbot conversations that feel natural, handle ambiguity gracefully, and guide users toward successful outcomes. It covers the full design lifecycle — from persona definition and intent mapping through dialog state management, error recovery, and iterative testing. The focus is on building conversations that are resilient to unexpected inputs while maintaining a consistent, helpful tone.
Workflow
-
Define the bot persona and scope. Establish the chatbot's personality traits (friendly, professional, concise, witty) and guardrails. Define what the bot can and cannot do. A well-scoped bot that excels at five tasks outperforms a vague bot that attempts fifty. Document the persona in a style guide that includes vocabulary preferences, emoji usage rules, response length targets, and escalation triggers.
-
Map intents, entities, and user journeys. Identify every intent the bot must handle — both primary task intents (e.g.,
order.place,account.reset_password) and meta-intents (e.g.,help,cancel,speak_to_human). For each intent, list the required entities (slots) the bot must collect. Map the conversation flows as directed graphs showing happy paths, branching points, and exit conditions. Ensure every path terminates in either a resolution or a graceful handoff. -
Design slot-filling and disambiguation dialogs. For each intent, define the slot-filling sequence — which entities are required, which are optional, and in what order the bot should prompt for them. When user input is ambiguous (e.g., "the large one" when multiple products qualify), design disambiguation prompts that present clear options without overwhelming the user. Use confirmation prompts for high-stakes actions like payments or cancellations.
-
Build error recovery and fallback flows. Design three tiers of fallback: (1) rephrasing the question when confidence is low, (2) offering a constrained set of options when the intent is unclear after two attempts, and (3) escalating to a human agent when the bot cannot recover. Never let the conversation hit a dead end. Every error state should include a recovery path and a way to restart or exit gracefully.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 155 lines · 48 tokens per session scan A 78b505a28996
chatbot-conversation-design is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 1,983 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
etsy-shop-branding
Shop branding — banner design, logo, brand story, cohesive listing photography, packaging.
brand-assets-setup
Generate and deploy a complete brand asset pack for a Next.js App Router project. Scans the project, prints what to generate with exact specs, then imports and wires everything correctly.
payoff-action-modeling
Model product UI actions from user intent questions after a meaningful outcome, completion, created resource, imported data, uploaded file, synced integration, report, deployment, automation, review state, handoff, or workflow milestone. Use when deciding which actions to show, hide, group, name, prioritize, defer, or…
ui-final-polish
Final visual polish for an existing UI without redesigning it. Use after structure is clear, when asked to improve spacing, alignment, text hierarchy, readability, shadows, highlights, effects, action placement, or overall production feel. For screen structure, layer naming, code handoff, preview scenes, or animation…
figma-pencil-fsd-tailwind4
Convert Figma or Pencil designs into production-ready frontend code using Feature-Sliced Design and Tailwind CSS v4. Use when translating designs into code, extracting tokens, mapping UI to FSD layers (shared / entities / features / widgets / pages / app-shell / app), enforcing Server vs Client boundaries when…
pencil-to-code
Convert Pencil .pen design files and named Pencil node IDs into production frontend code. Use when asked to implement, migrate, reproduce, or refine a Pencil/Figma-like visual design in code, especially for responsive artboards, glassmorphism, typography matching, background image layers, design tokens, or visual…