Index and search code across local and remote repositories with token-efficient semantic queries.
npx skills add https://github.com/akillness/jeo-skills --skill semble
Please help me install this Agent Skill. Skill: semble Source: https://github.com/akillness/jeo-skills/tree/main/.agent-skills/semble Command: npx skills add https://github.com/akillness/jeo-skills --skill semble
Semble provides token-efficient, semantic code search that returns only relevant code chunks for AI agents, avoiding full-file grep overhead.
Install semble, index your project, and perform a natural-language search to locate relevant code.
High-intent search queries and answers about installing and using this skill.
Semantic code search across local and remote repositories is achieved by indexing code chunks and querying them with natural language or symbols, returning only the most relevant matches to avoid full-file grep overhead for AI agents.
Token-efficient code search requires zero GPU or API keys to run. It achieves high-speed indexing at around 250ms and fast queries at approximately 1.5ms without external dependencies or specialized hardware.
Semantic code search integrates with Claude Code, Cursor, and OpenCode through MCP server integration. It also provides a Python library for programmatic access to query indexed repositories.
Discovering related code patterns without reading entire files is done using semantic similarity search and find-related capabilities, which locate and return only the specific relevant code chunks.
Querying code using natural language and symbols in a large project involves indexing the repository first, then executing semantic or programmatic searches that quickly return targeted code chunks.
Grep limitations for AI agent code discovery include full-file overhead and lack of semantic understanding. Semantic search overcomes this by indexing and returning only relevant code chunks using natural-language queries.
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