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Ditto190
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Code Indexing with Journal-Style Embeddings

Generate semantic embeddings for code chunks and store them in dated journal directories.

Updated Jan 1, 2026
Search Tags:#indexing#embeddings#offline#code#privacy#semantic-search
One-click install
npx skills add https://github.com/Ditto190/modme-ui-01 --skill code-indexing-with-journal-style-embeddings
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Code Indexing with Journal-Style Embeddings
Source: https://github.com/Ditto190/modme-ui-01/tree/main/agent-generator/src/skills/code-indexing
Command: npx skills add https://github.com/Ditto190/modme-ui-01 --skill code-indexing-with-journal-style-embeddings

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables private, offline indexing of code by generating semantic embeddings and storing them in a dated journal for auditability and easy retrieval.

Core Features & Use Cases

  • Semantic code search: Find code by meaning rather than exact keywords.
  • Chunk management: Break code into semantically meaningful units for better search.
  • Journal storage: Persist embeddings in dated directories with metadata for versioning.
  • Privacy-first: All processing happens locally using transformers.js and Python tools.
  • Use case: Index a codebase and perform rapid semantic searches for function definitions, error-handling patterns, or API usage.

Quick Start

To start using this Skill, index your codebase with a journal path and then run a search against the index. Example steps:

  • Index code: node scripts/knowledge-management/index-code.js --paths src/,agent/ --journal-path ./.code-journal
  • Search: node scripts/knowledge-management/search-code.js --query "authentication middleware" --limit 5

Frequently Asked Questions about Code Indexing with Journal-Style Embeddings

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

FAQPage Schema
How do I perform offline semantic code search locally?▼

Perform offline semantic code search by generating local embeddings with transformers and storing them in a dated journal directory, enabling private retrieval without external API calls.

What is the best way to index code chunks for auditability?▼

Index code chunks for auditability by storing semantic embeddings in a dated, versioned journal directory with metadata, ensuring historical tracking and easy retrieval.

How do I search code by meaning rather than exact keywords?▼

Search code by meaning by generating semantic embeddings of code chunks and comparing query embeddings against the local index to retrieve functionally relevant sections.

Does semantic code indexing work with codebases of varying sizes and languages?▼

Semantic code indexing works with codebases of varying sizes and languages by applying configurable chunking to break code into meaningful units before generating embeddings.

Can I run local code embeddings without sending my source code to an external API?▼

Run local code embeddings without external APIs by processing entirely offline using transformers.js and Python tools, ensuring privacy and preventing source code leakage.

How do I search a local code journal for authentication middleware patterns?▼

Search a local code journal by running the search script with a query string like "authentication middleware" and a limit parameter to retrieve the top matching indexed chunks.

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