Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Adityaraj0421/naksha-studio/plugin install naksha-studioWrote 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/commands/adityaraj0421/naksha-studio/design-chatbot)<a href="https://agentmods.dev/commands/adityaraj0421/naksha-studio/design-chatbot"><img src="https://agentmods.dev/badge/commands/adityaraj0421/naksha-studio/design-chatbot.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00027 | $0.03248 |
| Opus 5 | $0.00014 | $0.01624 |
| Sonnet 5 | $0.00005 | $0.00650 |
| Haiku 4.5 | $0.00003 | $0.00325 |
Grade A, and why
design-chatbot 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/design-chatbot
You are designing a complete chatbot or conversational assistant UI. Your output is a single structured spec covering persona, dialog flows, message UI, component library, error states, and accessibility.
Input
Arguments: $ARGUMENTS
Parse the following from $ARGUMENTS:
- Assistant type: customer support / sales / onboarding / general (default: general)
- Platform: web / mobile / Slack (default: web)
- Persona brief: any name, tone, or personality notes the user provides
Step 1: Load Knowledge Base
Read ${CLAUDE_PLUGIN_ROOT}/skills/design/references/conversational-designer.md to apply the full Conversational Designer knowledge base to every section below.
MCP Fallback: If the file cannot be read, proceed using inline knowledge. The inline knowledge for this command covers: persona dimensions (name, avatar, voice tone, vocabulary, emoji use), dialog flow states (GREETING → MAIN_FLOW → SLOT_FILLING → CONFIRMATION → EXECUTION, plus DISAMBIGUATION, FALLBACK_1/2/ESCALATION, GOODBYE), message bubble specs (user right-aligned brand accent; bot left-aligned neutral surface), quick reply chips (pill buttons, disappear on tap, max 5), typing indicator (3-dot pulse, 500ms delay), error state mapping (API timeout → retry, 5xx → retry + escalation, session expired → restart), and accessibility (aria-live="polite", role="log", keyboard nav, screen reader announcement order).
Step 2: Generate the Spec
Produce all six sections below as a single continuous output. Do not pause or ask for confirmation between sections.
Section 1: Persona & Voice Spec
Define the assistant's identity before any dialog is written.
| Dimension | Value |
|---|---|
| Name | [Derived from persona brief, or propose one if not provided] |
| Platform context | [assistant type on platform] |
| Avatar style | [Human-adjacent / abstract shape / brand mascot — choose based on assistant type and platform] |
| Language register | [Formal / Casual / Technical — justify by assistant type] |
| Vocabulary level | [Reading grade level, contraction use, jargon policy] |
| Emoji use | [None / Sparingly (1 per message max) / Moderate — justify] |
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.
- 6d ago First seen · 285 lines · 27 tokens per session scan A 63820083fdc4
design-chatbot is a command published in the GitHub repository Adityaraj0421/naksha-studio (316 stars, last pushed 2mo ago), licensed MIT. It adds 27 tokens to every session and 3,248 once invoked, about $0.0001 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 commands, from other repositories
theme-swap
Recolour an extracted site's design around a new brand primary. OKLCH hue rotation preserves perceptual lightness — neutrals, type, spacing, and motion stay untouched. Side-by-side HTML preview + recoloured tokens (DTCG, Tailwind, shadcn, Figma).
remix
Restyle a site in another design vocabulary — brutalist, swiss, art-deco, cyberpunk, soft-ui, or editorial. Preserves page shape, swaps the visual vocabulary.
studio
Launch the local design Studio — a live token editor over the latest extraction. Edit, preview a wall of real components, toggle dark mode, inspect motion, and export.
accessibility-specialist
Use when a screen might fail WCAG. Unlabeled inputs, no keyboard path, contrast below AA, missing landmarks, broken heading order, screen reader gaps, or a request for an a11y review or EU Accessibility Act check.
color-specialist
Use when colour is failing. Contrast below AA, a palette with no system behind it, dark mode that is only inverted light mode, or semantic colours that carry no meaning.
conversational-ui-designer
Use when designing a chat or assistant interface. Message layout, bot personality, streaming and typing states, rich message cards, error recovery, or voice input.