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Provide local-first code intelligence, impact analysis, and safety controls for AI coding agents.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "uacos" yet — see the docs or source repo.
Using the current repository dependency graph, analyze which files, call chains, and related components would be affected if I modify the user authentication module, and rank them by risk.
A ranked impact analysis showing affected files, dependency relationships, and modules likely to be impacted.
Generate AST-compressed context for this repository, focusing on payment-flow code so an AI can safely modify that functionality next.
A compact repository context optimized for AI consumption, highlighting key structures, module relationships, and relevant code areas.
Before applying this code patch, use patch-scope safety gates to check for potential risks; if the change fails or produces abnormal results, roll back to the pre-change state.
A patch risk assessment, with transactional rollback if needed to prevent unsafe changes from being applied.
When developers ask AI to generate or edit code in a complex repository, they can first use dependency graphs, impact analysis, and compressed context to reduce risky blind changes.
When an AI is about to apply a patch, this tool can enforce patch-scope safety gates to identify high-risk edits and block unsafe changes.
If a team does not always rely on Git for recovery, this tool provides git-independent transactional rollback to undo problematic AI-generated changes.
It is a local-first code intelligence and safety layer for AI coding agents. Its MCP server exposes dependency graphs, impact analysis, and AST-compressed repository context.
Based on the provided information, it includes patch-scope safety gates and git-independent transactional rollback to control and undo code changes.
The provided material does not include installation steps, runtime requirements, or key information. For specific prerequisites, see the source repository.
Analyze local codebases securely with AI-powered code intelligence and automation.
Query local codebases, guide safe changes, and produce audit-ready evidence.
Analyze codebases with semantic search, dependency insights, and natural language Q&A.
Build local codebase memory for AI agents with search and architecture inspection.
Query a local repo index for deterministic code search and analysis.
Give AI coding assistants memory, code graph insight, and safe multi-agent coordination.