semble-integration

Name: semble-integration
Availability: InStock
Author: hiddink-ai

Integrate the Semble MCP server for embedding-based semantic code search.

1|Updated May 21, 2026

Search Tags:#embeddings #mcp #code-analysis #token-optimization #semantic-search #semble

Ready to run

npx skills add https://github.com/hiddink-ai/hiddink-harness --skill semble-integration

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: semble-integration
Source: https://github.com/hiddink-ai/hiddink-harness/tree/main/templates/skills/semble-integration
Command: npx skills add https://github.com/hiddink-ai/hiddink-harness --skill semble-integration

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires semble.

What problem does it solve?

This skill solves the inefficiency of traditional keyword-based code search by providing semantic, embedding-based retrieval that significantly reduces token consumption and improves context relevance.

Core Features & Use Cases

Semantic Search: Uses natural language queries to find code patterns, error handling logic, or similar implementations without needing exact keyword matches.
Token Efficiency: Reduces context window usage by up to 98% compared to full-file reading by retrieving only relevant code chunks.
Use Case: When debugging complex error patterns, use this skill to find similar error-handling implementations across a large codebase instantly.

Quick Start

Use the semble integration skill to search for all authentication middleware patterns in the current project.

Frequently Asked Questions about semble-integration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How does semantic code search reduce token consumption during codebase exploration?▼

Semantic code search reduces token consumption by using embedding-based retrieval to return only relevant code chunks, cutting context window usage by up to 98% compared to reading full files.

Can I find error handling implementations across a large project using natural language queries?▼

Yes, you can find error handling implementations using natural language queries. This approach matches semantic meaning rather than exact keywords, instantly surfacing similar logic across a large codebase.

Do I need the Semble CLI tool to perform embedding-based code retrieval?▼

Yes, you need the Semble CLI tool and MCP server configuration. These dependencies facilitate the CPU-based indexing and ranking required for high-accuracy embedding-based code retrieval.

What is the best way to search for authentication middleware patterns without exact keyword matches?▼

The best way to search for authentication middleware patterns without exact keywords is using semantic search. It leverages embeddings to retrieve matching code patterns based on natural language intent.

Why use embedding-based retrieval instead of traditional keyword-based code search?▼

Use embedding-based retrieval over traditional keyword search to improve context relevance and overcome the inefficiency of exact string matching when exploring complex codebase patterns.