headroom
Compress tool outputs and RAG chunks to cut agent token usage.
About headroom
headroom compresses tool outputs, logs, files, and RAG chunks before they ever reach the LLM. The claim: 20% fewer tokens for coding agents and 60–95% fewer for JSON — with the same answers.
It ships as a library, a proxy, and an MCP server, so you can drop it into an existing agent stack without rewriting your pipeline. For anyone burning context window on noisy tool results, it is a cheap efficiency win.
More Github
chrome-devtools-mcp
chrome-devtools-mcp is an official MCP server that hands coding agents the full Chrome DevTools protocol — inspect the DOM, read console errors, trace network requests, and drive the page like a real browser session. It turns "the button doesn’t work" into something your agent can actually debug, live, instead of guessing from source. If you build web apps with an agent in the loop, this is one of the highest-leverage MCP servers to add.
serena
serena is a powerful MCP toolkit that gives coding agents semantic retrieval and editing — essentially an IDE for your agent. Instead of blindly grepping and pasting whole files, the agent navigates code by symbols and structure. It plugs into Claude Code, Codex, and JetBrains via a language server, making edits more precise and token-efficient on large codebases. A strong pick for anyone whose agent struggles once a repo gets big.
claude-mem
claude-mem gives your coding agent a long-term memory. It captures everything your agent does during a session, compresses it with AI, and injects the relevant context back into future sessions — so you stop re-explaining your project every time you start fresh. It works across Claude Code, Codex, Gemini, Copilot, OpenCode and more, storing memories in a local SQLite + embeddings pipeline. If you live inside an agent all day, this closes the biggest gap in the workflow: continuity.
Spec Kit
Spec Kit is GitHub's official toolkit for Spec-Driven Development — a workflow where you write a clear specification first and let AI coding agents implement against it. It turns vague vibe-coding into a repeatable, reviewable process. The idea Rather than prompting your agent one feature at a time, you author a structured spec (a PRD-like artifact), and the agent plans, breaks down, and builds from it. That keeps large agent-built projects coherent instead of drifting. Spec-first workflow that scales beyond one-off prompts Works with Copilot and other coding agents Bridges product intent and generated code Get started on GitHub .
Superpowers
Superpowers is an open-source agentic skills framework and software development methodology built for AI coding agents. It packages battle-tested workflows — brainstorming, subagent-driven development, and structured SDLC practices — into reusable skills your agent can invoke on demand. Why builders care Instead of re-prompting the same instructions every session, Superpowers lets you codify how your agent should think and work. It leans into subagent-driven development, where a lead agent delegates focused tasks to specialized subagents, keeping context clean and output high-quality. Reusable skills that encode proven engineering workflows Subagent-driven development to parallelize and isolate context A methodology, not just a prompt pack — designed to compound over time Explore the framework on GitHub .