Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Use when you need code research over an indexed codebase with minimal context: structural graph queries (who calls X, call chains, architecture overview, dead code, cross-service HTTP routes) instead of file-by-file grep/read cycles. Also trigger to minimise token spend on repetitive code exploration.
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See how to installReview the instructions the agent will receive before you install.
codebase_memory_mcp is the MCP server for the third-party
codebase-memory-mcp binary: it
indexes a repository into a persistent, local SQLite knowledge graph and answers
structural queries in-process. Everything stays on the machine — no cloud calls,
no API keys. Prerequisite: requires the codebase-memory-mcp binary on PATH. Install with:
curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bash (or npm install -g codebase-memory-mcp).
If the server is absent, ask the user to install it per
docs/codebase-memory.md; do not pretend to have graph access.
codebase-memory-mcp cli index_repository '{"path": "."}' before deep dives.get_architecture (one call for the whole repo), trace_path
(call chains), search_graph (regex/label search), detect_changes
(git-diff impact). Only open a file to confirm the findings you already have.memb-mcp) holds project memory
and conversation semantics — query memB for "what we decided", the graph for
"how the code is wired".synapse map <repo>);
use the graph for precise symbol-level questions and Synapse for spatial
orientation, then the source files for verbatim confirmation.No reviews yet. Tell others what you thought.
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Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.
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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.
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Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.
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