LuckyKuang

LuckyKuang / codex-tokens-compress

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面向 AI 协作场景的 AGENTS.md 压缩实验仓库：用统一规约格式提升指令命中率、降低幻觉风险，并量化 token 压缩收益。

18
1
100% credibility
Found Apr 11, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This repository offers multiple compressed formats of AI agent instruction documents (AGENTS.md) and simple comparison tools to quantify reductions in length and estimated token usage.

How It Works

1
📖 Discover token-saving tricks

You hear about a simple way to make AI agent's instruction files shorter and more effective without losing important details.

2
👀 Explore ready-made versions

Check out several improved versions of the rules file, from balanced to super-compact, even in classical style.

3
⚡ Measure the savings

Run a quick check to see exactly how much smaller each version is compared to the original, with clear numbers on savings.

4
⭐ Pick your favorite

Choose the version that best matches your needs for size, readability, or style.

5
🔄 Update your project

Swap in the new shorter rules file into your AI setup.

🎉 AI works better

Your AI follows instructions more accurately, uses less resources, and costs you less to run.

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Star Growth

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AI-Generated Review

What is codex-tokens-compress?

This Python repo experiments with compressing verbose AGENTS.md files—those instruction docs for AI agents in GitHub Copilot VSCode, CLI, or Claude setups—into dense, rule-based formats. It delivers multiple variants like standard compressed indexes, ultra-compact lists, and even classical Chinese styles, while providing cross-platform scripts to estimate and compare token counts against originals. Developers get quantified savings up to 80% tokens, boosting instruction accuracy and cutting hallucination risks in agents.md github repos.

Why is it gaining traction?

Unlike generic tokenizers, it targets agents.md github copilot specifics with ready-to-run bash and PowerShell commands that output side-by-side token comparisons, line counts, and savings percentages for your own files. The hook is instant baselines: drop in your AGENTS.original.md, run a script, and see exact tradeoffs between readability and token efficiency in agents md file github examples. Python validation tools ensure compressed formats stay parseable by Copilot or Claude agents md github workflows.

Who should use this?

AI workflow engineers tweaking agents.md github copilot cli or vscode extensions for team repos. Repo maintainers experimenting with claude agents md github who hit token limits in long-running sessions. Python scripters building custom agents md github blog or example setups needing to quantify compress token gains before deployment.

Verdict

Grab it if you're token-obsessed in AI agent pipelines—solid baselines and scripts make it a quick win despite 18 stars and 1.0% credibility score signaling early maturity. Docs are crisp, but expect to tweak for production; treat as a benchmark starter, not a drop-in lib.

(198 words)

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