Ccr

ccr — reversible payload compression: shrink any blob (tool output, log, RAG chunk, file) BEFORE it enters an LLM's context, while caching the full original locally so it can be expanded byte-for-byte on demand. The payload-axis counterpart to a knowledge-graph loader (the retrieval axis): the model sees a compact lossy view + a handle (ccr://<sha256>), and calls retrieve(handle) only when it needs the bytes back. Content-routed deterministic compactors (json skeleton / code outline / text head-tail); stdlib-only, no ML. Lifted from the CCR component of github.com/chopratejas/headroom. USE WHEN — a tool output / log / RAG chunk / file is too large for context and you want to compress it reversibly; "compress this payload", "shrink this before the model", "reversible compression", "cache the original and give me a handle". NOT FOR — loading knowledge-graph entities (that's the kg loader, the retrieval axis); lossless whole-file compression (use gzip); semantic summarization that does not need byte-exact recove

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broomva/skills/tree/main/skills/knowledge/ccr commit 571c4e1c31

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npx skillmds@latest add broomva/ccr