The agentic repo standard.
Atlas gives a git repository the structure that local coding agents need before they can work safely: shared instructions, repo memory, artifact paths, setup skills, and lightweight gates for plans, decisions, research, and results.
The payoff compounds over time. You keep working normally, while Atlas turns useful project context into documentation that grows with the repo and makes each next agent run more effective.
Run one command in the root of your project. Atlas creates the deterministic structure, then hands the rest to your local agent so it can inspect the repository and finish the setup with project-specific context.
npx --yes @blazity-atlas/core@latest initAtlas previews the files it wants to write, asks for confirmation in an interactive terminal, creates the .ai/ workspace and agent entrypoints, then prints the next prompt to give your coding agent.
Claude Code users can start from the Atlas marketplace instead:
/plugin marketplace add Blazity/atlas
/plugin install atlas@blazity
/atlas:setup
Both paths use the same published package, @blazity-atlas/core. The Claude Code plugin exposes the setup skill; the CLI still owns the deterministic file structure.
Atlas keeps AI-facing documentation small, explicit, and owned by the repository:
.ai/
config.json
LANGUAGE.md
memory/
plans/
research/
decisions/
decisions/adrs/
results/
skills/setup/
AGENTS.md
CLAUDE.md
.claude/skills -> ../.ai/skills
.agents/skills -> ../.ai/skills
.cursor/skills -> ../.ai/skills
.ai/config.json is the source of truth for artifact locations. If your repo already has useful docs, Atlas can map conventional paths into the .ai/ workspace instead of inventing another documentation system.
The first command only writes the shared structure. The local setup skill then:
- inspects the repository before asking questions;
- lets the agent recommend the right template after reading the project;
- fills
AGENTS.md, project vocabulary, and stable memory files; - keeps Claude, Codex, Cursor, and similar agents pointed at one shared workspace;
- leaves plans, decisions, research, and results in predictable locations.
This keeps the human flow simple: install Atlas once, then let the local agent adapt it to the actual repository. As work continues, plans, decisions, research, and lessons accumulate where agents can find them instead of disappearing into chat history.
AI can generate code quickly. That is no longer the hard part.
The hard part is keeping the output understandable, reviewable, secure, and aligned with the system your team actually needs to run.
Atlas is built for teams that want AI acceleration without giving up ownership. It brings agents into the delivery process through explicit rules, traceable artifacts, machine-enforced gates, and human review at the points where judgment matters.
- Ownership stays with the team. Tools run in your repo, your cloud, or your chosen substrate.
- Every agent needs a gate. Tests, evals, policy checks, and review rubrics should run before merge.
- Artifacts compound. Plans, decisions, logs, and lessons make the next run stronger.
- Review moves up the stack. Humans should review architecture, adapter boundaries, risk, and product intent instead of every generated line.
- No black boxes. Agent work should be traceable, auditable, and reversible.
- Substrates change. Systems remain. Atlas is designed to survive model and infrastructure churn.
Run atlas doctor to inspect an existing Atlas workspace for drift. Run atlas doctor --fix to apply safe deterministic repairs when the worktree is ready for changes.
Examples of projects built on the Atlas standard:
Migrate existing websites into structured Next.js projects with guided discovery, component planning, build gates, and visual verification.
Move software work from issue to plan, implementation, review, and pull request through governed agent workflows.