If you're burning through Claude quota or paying steep OpenAI API bills, there are free GitHub projects right now that can cut those costs by up to 90% — and most developers have never heard of them. One tool alone compresses your context window so aggressively that a 65,000-token debugging session shrinks to just 5,000 tokens, with no loss in accuracy. Alongside that, you'll find a trending AI search engine, a fully local NotebookLM clone, and a structured agentic engineering skill — all free, all open source, and all installable in minutes.

What Free GitHub Tool Cuts Your AI Bill by 90%?

The most immediately impactful project here is called Headroom. It sits at just above 24,000 stars on GitHub — still flying under the radar — but its star count is currently exploding for good reason. Headroom compresses everything your AI agent reads before it ever reaches the LLM: tool outputs, logs, RAG chunks, files, and conversation history. Same answers. A fraction of the tokens.

The numbers are staggering. A code search returning 100 results normally consumes 17,000 tokens. With Headroom, that drops to 1,400 — a 92% reduction. Incident debugging sessions go from 65,000 tokens to 5,000. GitHub issue tracking shrinks from 54,000 to 14,000. Even codebase exploration, one of the most common Claude Code and Cursor patterns, drops from 78,000 tokens to 41,000 — saving nearly half your quota every single time.

And critically, accuracy is preserved. Headroom has been tested against GSMAK, TruthfulQA, SQuAD V2, and BFCL benchmarks, scoring near-perfect across the board. You're not trading quality for savings — you're just removing the bloat.

How to Install and Run Headroom

Installation is as simple as giving your agent the GitHub URL and saying "install this." Once set up, you wrap your existing Claude CLI session by typing headroom wrap-claude with the --no-proxy flag. It loads exactly like Claude Code normally does. After running it for a session, type headroom perf to see a full breakdown: tokens saved per model, cache performance, optimization overhead, and conversation size stats.

There's also a standout feature called headroom learn, which mines your failed sessions and automatically writes corrections to your claude.md and agents.md files. In one test, it found that deferring tool schema loading could save 8,000 tokens per session.

Two things to know before you install: Headroom installs a tool called Serena by default, which has nothing to do with Headroom itself. Add the --no-sa flag during install to skip it. Also, telemetry is enabled by default — disable that if privacy matters to you.

Is There a Free Local Alternative to NotebookLM?

Google's NotebookLM is genuinely impressive — you upload PDFs and documents, ask questions against them, and it even generates a full synthesized podcast discussing the content. The problem is it's fully hosted and closed. Open Notebook changes that entirely.

Sitting at just under 30,000 stars on GitHub, Open Notebook is a free, open-source, completely local NotebookLM clone. You can power it with hosted models like OpenAI's GPT-4 lineup, or run it entirely on-device using Ollama or LM Studio. No data leaves your machine if you choose the local path.

What Can Open Notebook Actually Do?

Drop in a link to any article or essay and it ingests the whole thing. You can then ask freeform questions — "Is the author pro-AI or anti-AI based on this article?" — and it responds with a cited answer, pointing back to exact passages in the source.

The podcast generation is where it gets interesting. Click "Generate Podcast," select your document, and within minutes you have a multi-host synthesized audio discussion of your content. You can customize the tone, number of hosts, script style, and more. If you want voices that actually sound human rather than robotic, pairing Open Notebook with ElevenLabs takes the quality to another level.

Beyond podcasts, Open Notebook includes a Transformations panel where you can extract key insights, generate dense summaries, analyze research papers, create reflection questions, or auto-generate a table of contents — all from a single document. Setup involves picking your models for chat, embedding, text-to-speech, and transcription, but once configured it runs seamlessly.

How Does Last 30 Days Search Trending Content?

Traditional search engines show you what's popular with their algorithm — and serve you ads alongside it. Last 30 Days works completely differently. It searches Reddit, Hacker News, X, YouTube, TikTok, Polymarket, and GitHub simultaneously, then ranks results purely by human engagement: upvotes, likes, real-money prediction market odds, and video engagement signals.

The result is a search engine that surfaces what real people are actually paying attention to right now, not what an ad-optimized algorithm thinks you should see. The project has over 40,000 stars and was built by Matt Van Horn, co-founder of the company that became Lyft.

How Well Does It Actually Work?

Type in a search term like "loop engineering" and Last 30 Days doesn't just search — it first figures out where to search. The V3 engine resolves your topic to specific Twitter handles, subreddits, and communities before the search even begins. It then synthesizes the top results into a single concise brief, with all sources listed at the bottom. In one test search, it surfaced 32 Reddit threads with 45,000 combined upvotes and 40 Hacker News stories — all ranked by real human attention.

You can also type emit=HTML to generate a shareable, cleanly formatted HTML summary page of any search result. It's perfect for sending a trend briefing to a team or saving a snapshot of what the internet was talking about this week.

Installation takes seconds — paste the GitHub URL into Claude Code, Cursor, or any agentic platform, tell it to install the skill, restart, and type /last30days followed by any search term.

What Is the Best Skill for Agentic Engineering?

If you're doing serious agentic engineering and want a structured workflow, Agent Skills is worth installing immediately. It's sitting at just above 56,000 stars and provides seven slash commands that map to seven stages of engineering: /spec, /plan, /build, /test, /review, /simplify, and /ship.

Think of it as a structured engineering methodology baked into your agent. It's similar in spirit to Gary Tan's GStack, but where GStack is focused on building entire companies, Agent Skills is laser-focused on the engineering workflow itself.

How Do You Install GitHub Skills in Claude or Cursor?

The installation pattern is the same across all four of these projects: copy the GitHub URL, paste it into your agentic coding environment, and say "install this skill." That's it. No terminal commands, no dependency management, no configuration files to edit manually.

Once Agent Skills is installed, start with /interview me. It runs a structured interview to extract exactly what you're trying to build, then outputs a clean markdown spec file. From there, the workflow guides you through spec-driven development — breaking your idea into small, achievable pieces — all the way through to security hardening, performance optimization, and code simplification. It's a complete engineering loop in a single skill.

Why These Four Projects Together Are a Power Stack

Each of these four tools solves a distinct problem that developers and AI engineers run into constantly. Headroom keeps your costs manageable as you scale up agentic workflows. Open Notebook gives you Google's NotebookLM with full local control and customization. Last 30 Days replaces algorithmic search with human-signal-driven intelligence for staying current. And Agent Skills gives structure to what is otherwise a chaotic, ad-hoc engineering process.

All four are free. All four install in under five minutes. And all four are actively maintained with rapidly growing communities behind them. Whether you're trying to save on your API bill, build better products faster, or just stay informed on what's actually trending in AI — this stack covers it.