Delete stored context-mode data for one session or an entire project after confirming the scope. Remove an unwanted session while preserving others, or clear project knowledge, session records, and statistics for a fresh start.
Purge the context-mode knowledge base. Permanently deletes all indexed content and resets session stats. This is destructive and cannot be undone. Trigger: /context-mode:ctx-purge
Review the instructions the agent will receive before you install.
Permanently deletes session data for this project. Two scopes are supported (issue #520):
scope: "project"): wipes EVERYTHING — knowledge base, all session DB rows for every session, events markdown, and stats.sessionId: "<id>" or scope: "session"): wipes ONLY the matching session's rows + FTS5 chunks. Sibling sessions, project stats, and the FTS5 store file are preserved.sessionId.ctx_index, ctx_fetch_and_index, ctx_batch_execute)mcp__context-mode__ctx_purge MCP tool with the chosen parameters:
{ confirm: true, sessionId: "<id>" } — implies scope:'session'.{ confirm: true, scope: "project" } — explicit destructive form.{ confirm: true } still works but emits a deprecation warning. Prefer the explicit forms.confirm: true is always required.sessionId and scope: "project" together is REJECTED as ambiguous (the sessionId implies session scope; combining with project scope contradicts intent).scope: "session" without sessionId throws — sessionId is required.ctx_purge is the only way to delete session data. No other mechanism exists.ctx_stats is read-only — shows statistics only./clear and /compact do NOT affect any context-mode data.No reviews yet. Tell others what you thought.
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Analyze large logs, test results, API responses, and files outside the conversation, then return focused findings. It guides agents to save, process, index, and search outputs while limiting how much raw data enters their working context.
Route codebase exploration, file analysis, and web research through context-mode tools in Antigravity CLI. Process large outputs outside the conversation and return focused findings, with indexed content available for later searches.
Route data analysis, large-file processing, and web retrieval through context-mode tools, returning only relevant results to the conversation. This helps preserve the context window, the limited amount of information an AI can work with at once.
Coordinate multiple agents to maintain context-mode, from sorting GitHub issues and reviewing pull requests to preparing releases and announcements. Workflows require reproduced bugs, source evidence, test-first changes, and checks across supported platforms.
Run context-mode diagnostics to check runtimes, hooks, full-text search support, plugin registration, and npm and marketplace versions. Results appear directly in the conversation with clear status labels, with a CLI fallback if the MCP call fails.
Index local files or folders in context-mode’s persistent search database, so later searches can retrieve relevant passages without rereading entire files. Useful for repeatedly consulting project documents while keeping raw file contents out of the conversation.