Claude-skill-registry Chatbot Implementation

Details of the RAG Chatbot, including UI and backend logic.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/chatbot" ~/.claude/skills/majiayu000-claude-skill-registry-chatbot-implementation && rm -rf "$T"

manifest: skills/data/chatbot/SKILL.md

Chatbot Logic

Overview

A specialized RAG (Retrieval Augmented Generation) chatbot that helps users learn from the textbook content.

Backend

Route:
```
app/api/chat/route.ts
```
Logic:
1. Receives
```
query
```
  and
```
history
```
  .
2. Embeds query using Gemini or OpenAI embedding model.
3. Searches Qdrant (vector DB) for relevant textbook chunks.
4. Constructs context from matches.
5. Generates response using Gemini Flash/Pro.

Vector Search (Qdrant)

We use Qdrant for storing embeddings of the textbook.

Collection:
```
textbook_chunks
```
(or similar).
Fields:
```
text
```
,
```
source
```
,
```
chunk_id
```
.

UI Component

Location:

textbook/src/components/Chatbot/index.tsx

Features:
- Floating chat window.
- Size controls (Small, Medium, Large).
- Markdown rendering of responses.
- Context selection (highlight text to ask about it).
- Mobile responsive design.
- Auth awareness (personalizes answer based on user profile).

Styling

CSS:
```
styles.module.css
```
(Premium animations, shadow effects).
Themes: Dark/Light mode compatible (using
```
--ifm
```
variables).