Skills Chatbot Implementation

📦

Chatbot Implementation

Name: Chatbot Implementation
Author: AbdulSamad94

Content revision r2 Safe ⚙️ External commands

Build a Textbook RAG Chatbot

Textbook chatbots need a clear connection between the chat interface, retrieval flow, and source content. This guide outlines a RAG design using an API route, embeddings, Qdrant search, and a responsive chat component.

Supports: Claude Codex Code(CC)

📊 76 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "Chatbot Implementation" from https://skillstore.io/skills/abdulsamad94-chatbot-implementation.md and its manifest at https://skillstore.io/api/skills/abdulsamad94-chatbot-implementation/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/abdulsamad94-chatbot-implementation.md Signed manifest GET /api/skills/abdulsamad94-chatbot-implementation/manifest Signed lockfile GET /api/skills/abdulsamad94-chatbot-implementation/lockfile

Test it

Using "Chatbot Implementation". Explain the backend request flow.

Expected outcome:

Receive the user query and history.
Create an embedding for the query.
Retrieve relevant textbook chunks from Qdrant.
Build context from the matches and generate a response.

Using "Chatbot Implementation". What should the chatbot UI include?

Expected outcome:

A floating chat window with size controls.
Markdown response rendering and highlighted-text questions.
Responsive layout, user-aware answers, and light or dark theme support.

Security Audit

Safe

v11 • 7/18/2026 Open versioned report

All seven static detections are false positives. They identify Markdown inline code spans that name routes, fields, files, and CSS variables, not executable shell or Ruby commands. No prompt injection, command execution instruction, or data-exfiltration intent was found in the reviewed skill.

Files scanned

Lines analyzed

Review items

False positives ignored

Risk Factors

⚙️ External commands (7)

SKILL.md:12 SKILL.md:14 SKILL.md:22 SKILL.md:23 SKILL.md:26 SKILL.md:36 SKILL.md:37

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: claude View Audit History →

Share & cite this report

Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.

Open versioned report

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https://skillstore.io/skills/abdulsamad94-chatbot-implementation/audits/11?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

AbdulSamad94. (2026). Chatbot Implementation security audit report (audit version 11) [Author version unspecified]. Skillstore. https://skillstore.io/skills/abdulsamad94-chatbot-implementation/audits/11

BibTeX citation

@techreport{abdulsamad94-abdulsamad94-chatbot-implementation-2026,
  author = {AbdulSamad94},
  title = {Chatbot Implementation security audit report (audit version 11)},
  institution = {Skillstore},
  year = {2026},
  number = {11},
  url = {https://skillstore.io/skills/abdulsamad94-chatbot-implementation/audits/11},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "Chatbot Implementation security audit report (audit version 11)"
version: "unspecified"
type: report
authors:
  - name: "AbdulSamad94"
date-released: "2026-07-18"
url: "https://skillstore.io/skills/abdulsamad94-chatbot-implementation/audits/11"
identifiers:
  - type: other
    value: "skillstore:abdulsamad94-chatbot-implementation:audit:11"
    description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Plan a textbook assistant

Map the request, retrieval, context-building, and response flow for a learning chatbot.

Design a chat interface

Use the documented UI features to plan a floating, responsive chatbot component.

Review a RAG architecture

Check whether an existing textbook chatbot includes retrieval, source context, and interface requirements.

Try These Prompts

Explain the chatbot flow

Use this skill to explain the basic request flow for a textbook RAG chatbot.

Plan Qdrant retrieval

Use this skill to outline how a query is embedded and matched against textbook chunks in Qdrant.

Specify the chat component

Use this skill to create a feature checklist for a responsive textbook chatbot UI with Markdown responses and context selection.

Review the full design

Use this skill to review my textbook chatbot architecture. Identify missing pieces across API handling, vector retrieval, response context, UI behavior, and themes.

Best Practices

Keep retrieved textbook content separate from conversation history when building model context.
Show source information for retrieved content when the product requires traceable answers.
Test the chat component on mobile screens and in both supported themes.

Avoid

Do not treat this reference as a complete production implementation.
Do not send unbounded conversation history or retrieval results to the response model.
Do not personalize answers without defining appropriate user data and access controls.

Frequently Asked Questions

What does this skill document?

It documents the intended backend, Qdrant retrieval, and UI design for a textbook RAG chatbot.

Does it include source code?

No. It describes file locations and behaviors but does not provide implementation code.

Which vector database does it reference?

It references Qdrant for storing and searching textbook embeddings.

Which models can generate responses?

The document mentions Gemini Flash or Pro for response generation and Gemini or OpenAI embeddings.

Can users ask about selected text?

Yes. The documented UI includes context selection from highlighted text.

Does it address production security?

No. You must separately design authentication, authorization, data handling, and model credential controls.

Developer Details

Author

AbdulSamad94

License

MIT

Skillstore revision

Version notice

The author did not declare a version.

Repository

https://github.com/AbdulSamad94/Hackhaton-SpecsKitPlus/tree/master/.claude/skills/chatbot

Ref

80999bf530a7874d7bedf8ce202001ecb4c4f5e0

Maintenance freshness

7/19/2026

Usage

6 downloads · 171 views

File structure

📄 SKILL.md