tlancas25

Context Cooler — Development skill for Claude Code

Development community

Context Cooler — eliminate token burn with the coolest MCP on the net.

How to install Context Cooler

This entry records only its repository, not the path inside it, so there is no exact command to give. Open tlancas25/openclaw-context-saver and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Context Cooler does

Context Cooler — eliminate token burn with the coolest MCP on the net. Sandboxed exec in 11 languages, FTS5 search, multi-messenger delivery, 5 platform adapters. MIT, zero callbacks.

Alternatives in Development

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  • Exec Summary — Compress a document or update into a crisp executive summary 1.3k ★

README

Context Cooler — Burn fewer tokens. Ship cooler agents.

Context Cooler

Eliminate token burn with the coolest MCP on the net.
Burn fewer tokens. Ship cooler agents.

License: MIT MCP server Outbound: zero callbacks Version 6.2

A standalone Model Context Protocol (MCP) server that gives any MCP-compatible coding agent — Claude Code, Cursor, OpenAI Codex CLI, Gemini CLI, OpenCode, Grok CLI, Shadow — a sandboxed runtime, an FTS5 knowledge base, and a multi-messenger delivery channel. Built from scratch on the MCP spec. Zero outbound dependencies beyond the four pinned ones in `package.json`. MIT-licensed, audit-readable end-to-end.


The "Think in Code" philosophy

When an agent needs to analyse a directory, a JSON dump, or 47 source files, the temptation is to *Read* every file and let the model figure it out from raw text. That's how 750 KB of cached context disappears in a single afternoon: every turn re-pays the read cost.

**Don't pull data into the model. Push code at the data and pull back the answer.**

Canonical example

"Across these 47 TypeScript files, find every `await` that's missing a `try/catch`."

Approach Bytes consumed Tokens (rough)
Read × 47 (/src/**/*.ts) ~700 KB raw text in context ~175,000
ctx_execute (one shell+grep call, prints summary) ~3.6 KB summary ~900

The 195× reduction isn't theoretical — it's what a real morning-brief pipeline measures every day. The agent's job is to **write a script**, not to memor