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The open-source company brain that powers a closed loop — model-free, over MCP. Ingest your docs; your AI tool (Claude, Cursor, Copilot) answers with citations or refuses. Record how-work-gets-done as skills, govern real actions with human approval + audit, and let the loop learn from outcomes. Bring your own model, data, and infra.
open-company-brain is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 39/100. The open-source company brain that powers a closed loop — model-free, over MCP. Ingest your docs; your AI tool (Claude, Cursor, Copilot) answers with citations. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding open-company-brain to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/PDgit12/open-company-brain.gitReview the upstream source before running this command.
Scan coverage: Install metadata and repository README.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "PDgit12/open-company-brain"
}The open-source company brain that powers a closed loop — model-free, over MCP. Ingest your docs; your AI tool (Claude, Cursor, Copilot) answers with citations or refuses. Record how-work-gets-done as skills, govern real actions with human approval + audit, and let the loop learn from outcomes. Bring your own model, data, and infra.