parcadei

parcadei / tldr-code

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Token-efficient code analysis for LLMs

13
2
100% credibility
Found Apr 27, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

tldr is a code analysis tool that extracts structured insights like function structures, call graphs, dead code, security scans, and quality metrics from 18 programming languages in compact formats ideal for AI processing.

How It Works

1
🔍 Discover tldr

You learn about tldr, your friendly code explorer that reveals a project's secrets without overwhelming details.

2
📥 Get it ready

With one easy step, you bring tldr to your computer, and it's set up in moments.

3
📁 Point to your project

You show tldr your folder of code, and it starts understanding everything inside.

4
💡 Unlock insights

Watch as tldr maps functions, connections, risks, and health in a clear, bite-sized view.

5
🔄 Dig deeper

Ask about dead spots, security checks, or complex areas for tailored advice.

✅ Codebase mastered

You now see the full picture, spot fixes effortlessly, and work smarter every day.

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Star Growth

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AI-Generated Review

What is tldr-code?

tldr-code is a Rust CLI for token-efficient code analysis designed for LLMs, extracting structured insights like call graphs, dead code, taint flows, and complexity metrics across 18 languages from Python to Rust. Instead of feeding raw source dumps that burn tokens, it outputs compact JSON summaries via 60+ commands such as `tldr calls src/` or `tldr health .`—perfect for claude code tldr or tldr code analysis workflows. Installs via Cargo, with daemon mode for cached repeats.

Why is it gaining traction?

It slashes LLM context costs by distilling code to essentials: function signatures, hubs, hotspots, even semantic search with embeddings. Unlike verbose linters, outputs are machine-ready (JSON, SARIF, DOT) for tldr vs code extension integration or CI, with Rust speed handling 10K LOC call graphs in seconds. The hook? Daemon caching turns analysis into instant queries, outpacing slower alternatives for iterative dev.

Who should use this?

Rust or Python backend devs refactoring monorepos via `tldr dead` or `tldr hotspots`; security folks hunting vulns with `tldr taint` or `tldr secure`; prompt engineers building LLM agents needing clean code context without token waste. Suits teams doing code geass tldr-style reviews or da vinci code tldr breakdowns on multi-lang projects.

Verdict

Grab it for LLM-augmented analysis if you're okay with early-stage risks—13 stars and 1.0% credibility mean expect bugs, but Cargo install and command docs make trials low-friction. Prioritize on toys first; production-ready potential as it stabilizes.

(187 words)

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