R-KV: Optimize Reasoning
R-KV provides efficient cache compression for reasoning models by reducing redundancy in key-value stores, optimizing performance and memory usage.
- •When deploying large-scale reasoning models that require significant cache storage
- •When optimizing model performance is crucial, and cache compression can provide a significant speed boost
- •When memory usage needs to be minimized, such as in edge devices or low-resource environments
python examples/example.py --cache_size 1000 --compression_ratio 0.5
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/r-kv && curl -fsSL https://workflowstacks.com/api/skills/r-kv/claude-skill -o ~/.claude/skills/r-kv/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
What's inside — free to inspect
Read the entire source before you build — unlike paid marketplaces that hide it behind a buy button.
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/r-kv && curl -fsSL https://workflowstacks.com/api/skills/r-kv/claude-skill -o ~/.claude/skills/r-kv/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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