by bitloops
Give AI coding agents the context they need to ship production-quality software.
# Add to your Claude Code skills
git clone https://github.com/bitloops/bitloopsLast scanned: 5/30/2026
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}bitloops is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by bitloops. Give AI coding agents the context they need to ship production-quality software. It has 232 GitHub stars.
Yes. bitloops passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/bitloops/bitloops" and add it to your Claude Code skills directory (see the Installation section above).
bitloops is primarily written in Rust. It is open-source under bitloops on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh bitloops against similar tools.
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Fast scripted setup installs Bitloops and applies the default daemon config:
macOS, Linux, WSL:
curl -fsSL https://bitloops.com/install.sh | bash -s -- --default-config
Windows PowerShell:
& ([scriptblock]::Create((irm https://bitloops.com/install.ps1))) -DefaultConfig
Then, from inside the repo you want Bitloops to capture:
bitloops init
Prefer to configure manually? Install without the default-config flag, then run:
bitloops configure --web
bitloops init
Work normally with Codex, Claude Code, Cursor, Gemini, Opencode or Copilot. Commit as usual. Bitloops captures the relevant context around every change and keeps your codebase model fresh in the background.
Open the local dashboard:
bitloops dashboard
Other install paths and full setup → Docs
You've already built the workaround. A CLAUDE.md that grew to 2,000 lines. A .cursor/rules folder you copy-paste between projects. An AGENTS.md you swear you'll keep updated. A shell script that concatenates the "right" files into every prompt.
The pattern is always the same: a second brain for your codebase, built in markdown, manually maintained, going stale every week.
AI coding agents are bottlenecked by state, not generation. A codebase isn't fundamentally text — it's a system of files, symbols, APIs, tests, decisions, and history. Most agents force that structured system through an unstructured medium (prompt text, RAG chunks, embeddings) and have to infer the system over and over.
The loop today:
retrieve → infer → act → forget
Bitloops changes the loop to:
capture → persist → rank → serve → refresh
Agents can't reliably change systems they can't model. Bitloops builds and maintains the model.
| You're doing this today | With Bitloops |
|---|---|
| Re-explaining your architecture every session | Substrate captured once. Re-served on every prompt. |
Hand-writing CLAUDE.md / .cursor/rules / AGENTS.md |
Decisions and constraints captured automatically from your agent conversations. |
| Agent re-implements a util that already exists three folders over | Static analysis + clone detection feeds existing code into pre-edit context. |
| "Why is this here?" → the prompt is gone | Commit → prompt → rejected alternatives, all traceable. |
| Five rules files for five agents | One substrate. Claude Code, Cursor, Copilot — same model underneath. |
| RAG embeddings rebuilt every session | Long-lived daemon. Index is always-on, always-current. |
| Repository uploaded and stored on a vendor's infra | Runs locally. Code processed, not stored. |
┌──────────────────────────────────────────────────────────────────────┐
│ Your agent (Claude Code / Cursor / Copilot) makes a request │
└──────────────────────────────────────────────────────────────────────┘
↓
┌──────────────────────────────────────────────────────────────────────┐
│ Bitloops hooks capture the prompt, transcript, and tool events │
└──────────────────────────────────────────────────────────────────────┘
↓
┌──────────────────────────────────────────────────────────────────────┐
│ Local daemon updates the codebase graph (files, symbols, deps) │
└──────────────────────────────────────────────────────────────────────┘
↓
┌──────────────────────────────────────────────────────────────────────┐
│ DevQL ranks and serves the right artifacts back into the agent │
└──────────────────────────────────────────────────────────────────────┘
↓
┌──────────────────────────────────────────────────────────────────────┐
│ Commit links to the prompt, model, and decision that produced it │
└──────────────────────────────────────────────────────────────────────┘
Architecture deep-dive → Docs › Architecture
.cursor/rules, or AGENTS.md and quietly knowing it's already out of date.Agents:
Languages:
Your code isn't stored on our infrastructure. The daemon runs on your machine; configuration, repository model, event data, and blobs stay local by default.
LLM reasoning over commits, code, and conversations is part of the system — that processing requires sending content to a model provider, and an account is required to authenticate it. The line: your code isn't stored, it is processed.
Apache-2.0. See [LICENSE](https://github.com/bitl