Filter and compress command-line tool output to reduce LLM token consumption.
npx skills add https://github.com/k1lgor/mega-mind-skills --skill rtk-k1lgor
Please help me install this Agent Skill. Skill: rtk Source: https://github.com/k1lgor/mega-mind-skills/tree/main/.agent/skills/rtk Command: npx skills add https://github.com/k1lgor/mega-mind-skills --skill rtk-k1lgor
This Skill significantly reduces the number of tokens sent to Large Language Models by intelligently filtering and compressing the output of common command-line tools, thereby lowering costs and speeding up interactions.
git log, cargo test, and npm install.git log to review recent commits, use rtk git log to get a concise, token-efficient output suitable for AI analysis.Use rtk git log --oneline -10 to view the last 10 commits in a compact format.
High-intent search queries and answers about installing and using this skill.
Token reduction for command line operations works by intelligently filtering and compressing tool output before sending it to the LLM. This mechanism lowers costs and speeds up AI interactions by removing irrelevant verbose logs.
You can compress git log output for AI analysis by running the command through a token optimization wrapper. For example, use the wrapper followed by `git log --oneline -10` to view recent commits in a compact, token-efficient format.
Yes, token optimization works with Rust, Node.js, and Python package managers. It provides optimized wrappers for various languages and tools, significantly reducing tokens from outputs like `cargo test` and `npm install`.
You can track token savings for unsupported command line tools using proxy mode. This mode allows the wrapper to proxy commands while still monitoring and tracking cumulative token usage even without direct optimization support.
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