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tldr-swinton

Analyze codebases with diff-context, semantic search, and structural patterns.

3|1|Updated Feb 15, 2026
Search Tags:#typescript#code analysis#semantic search#llm#token efficiency#rust#code reconnaissance
One-click install
npx skills add https://github.com/mistakeknot/Demarch --skill tldr-swinton
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tldr-swinton
Source: https://github.com/mistakeknot/Demarch/tree/main/.gemini/generated-skills/tldr-swinton
Command: npx skills add https://github.com/mistakeknot/Demarch --skill tldr-swinton

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill drastically reduces the number of tokens LLMs need to process when analyzing codebases, making code understanding and generation faster and more cost-effective.

Core Features & Use Cases

  • Token Efficiency: Achieves significant token savings (48-85%) through advanced techniques like diff-context, semantic search, and structural pattern analysis.
  • Multi-Language Support: Capable of analyzing code in TypeScript, Rust, Go, and Python.
  • Use Case: When onboarding to a large, unfamiliar codebase, use this Skill to get a concise, token-efficient overview of the project's structure and key components, enabling faster comprehension and development.

Quick Start

Run tldrs quickstart for a concise reference guide.

Frequently Asked Questions about tldr-swinton

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

FAQPage Schema
How do I reduce LLM token usage when analyzing a large codebase?▼

Token-efficient code reconnaissance reduces LLM token usage by analyzing code through diff-context, semantic search, and structural patterns. This approach cuts token consumption by 48-85% during code understanding and generation tasks.

What's the best way to get an overview of an unfamiliar TypeScript and Rust project?▼

Code reconnaissance provides a concise, token-efficient overview of unfamiliar multi-language codebases. It analyzes structural patterns across TypeScript, Rust, Go, and Python to accelerate project onboarding and comprehension.

Does semantic code search work with an integrated MCP server for direct tool integration?▼

Yes, semantic code search is paired with an integrated MCP server for direct tool integration. This allows LLMs to query and analyze codebases efficiently without manual context window management.

How do I start analyzing code structure for token efficiency?▼

Run `tldrs quickstart` to access a concise reference guide for analyzing code structure. The tool applies diff-context, semantic search, and structural pattern analysis to minimize token processing overhead.

Can I use diff-context analysis for Python and Go codebases?▼

Yes, diff-context analysis supports multi-language codebases including Python, Go, TypeScript, and Rust. It evaluates code changes and structural patterns to maintain token efficiency across different programming languages.

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