Filter large files and codebases to reduce token count before sending to AI models.
npx skills add https://github.com/ContentsUS/Agentic-AI-Paji --skill ggg
Please help me install this Agent Skill. Skill: ggg Source: https://github.com/ContentsUS/Agentic-AI-Paji/tree/main/.claude/skills/ggg Command: npx skills add https://github.com/ContentsUS/Agentic-AI-Paji --skill ggg
This Skill dramatically reduces the amount of text sent to AI models, cutting down on processing time and API costs by filtering out irrelevant information.
ggg to focus the AI's attention on the critical sections, leading to faster and cheaper analysis.Use the ggg skill to filter the file 'src/auth.ts' for the task 'Fix login bug' using auto mode.
High-intent search queries and answers about installing and using this skill.
Token reduction for AI models involves filtering large codebases to retain only relevant sections, shrinking the context sent to the API by 50-90% to lower processing time and costs.
Context filtering saves LLM API costs by removing irrelevant text from large files before processing, which can achieve significant cost reductions per task, such as a 69% saving demonstrated in bug analysis examples.
Yes, Python scripts are required to execute the context filtering process. The scripts support multiple modes, including auto and local, to manage your codebase context efficiently before sending it to AI models.
Yes, you can optionally use Gemini Flash or local LLMs for filtering codebases. These models help identify and retain the most relevant parts of a file, reducing token usage before sending the focused context to your primary AI.
The best way to shrink context for complex configuration files is using an auto filtering mode that focuses the AI's attention on critical sections needed for tasks like bug fixes, leading to faster and significantly cheaper analysis.
You should use context filtering for LLM efficiency when analyzing large files or complex codebases for specific tasks like bug fixes, as sending the full file wastes tokens and increases API costs without improving the AI's focus.
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bamechoRewrite prompts to reduce token usage while preserving meaning.
baphledFilter, aggregate, and summarize large tool outputs to reduce token usage.