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PMX Canvas is a shared board where you and your AI agents collect and present the data and evidence behind your work. Curate that material into context for your agent's next step, then bring the results back onto the board to review and build on. You both add and edit material on the same board, in real time. Pin context to focus input and output.
pmx-canvas is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 55/100. PMX Canvas is a shared board where you and your AI agents collect and present the data and evidence behind your work. Curate that material into context for your. 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 pmx-canvas to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/pskoett/pmx-canvas.gitReview the upstream source before running this command.
Scan coverage: Install metadata only — no README or document body could be retrieved, so the absence of signals here is not evidence of safety.
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": "pskoett/pmx-canvas"
}PMX Canvas is a shared board where you and your AI agents collect and present the data and evidence behind your work. Curate that material into context for your agent's next step, then bring the results back onto the board to review and build on. You both add and edit material on the same board, in real time. Pin context to focus input and output.