Compress large data blobs into lossy representations with stdlib-only algorithms.
npx skills add https://github.com/broomva/skills --skill ccr
Please help me install this Agent Skill. Skill: ccr Source: https://github.com/broomva/skills/tree/main/skills/knowledge/ccr Command: npx skills add https://github.com/broomva/skills --skill ccr
This skill solves the problem of context window exhaustion by shrinking large blobs like logs, tool outputs, or RAG chunks into compact, lossy views that the model can expand on demand.
Use the ccr skill to compress the file named big-data.json and generate a retrieval handle.
High-intent search queries and answers about installing and using this skill.
To optimize LLM context window usage, compress large JSON payloads into compact, lossy representations like JSON skeletons using specialized compactors. The original data is cached locally, allowing the model to inspect the structure and retrieve full content via a unique retrieval handle when necessary.
Recover original data after lossy compression using a content-addressed local cache that ensures byte-exact recovery. This mechanism preserves non-UTF-8 characters and specific formatting, allowing retrieval of the exact original payload through a unique retrieval handle.
Yes, you can compress code and text formats alongside JSON for LLM context optimization. The system automatically applies specialized compactors for code outlines or text head-tail views, routing compression based on the content type to shrink large blobs.
Context window compression for LLMs does not require external dependencies. The system uses deterministic, stdlib-only compression algorithms to shrink large blobs like logs, tool outputs, or RAG chunks into compact views without needing additional libraries.
Use lossy payload compression for LLM context management when a massive log file or minified JSON response exceeds your context budget. It shrinks these large blobs into small handles, allowing the model to inspect the structure and expand on demand.
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