nowledge-mem
Search, save, and manage knowledge across all your AI tools through Nowledge Mem.
What this skill does
# Nowledge Mem — Cross-Tool Knowledge for Bub You have access to the user's personal knowledge graph through Nowledge Mem. This graph contains knowledge from all their AI tools — decisions from Claude Code, preferences from Cursor, insights from ChatGPT, and more — not just this Bub session. Knowledge you save here will be available in their other tools too. ## When to search Recognise these signals and call `mem.search` **before** answering: - **Continuity** — the user references something from a previous session or another tool - **Decision recall** — "what did we decide about…", "why did we choose…" - **Pattern match** — the current topic overlaps with past work in any tool - **Implicit recall** — the user assumes you know something you haven't seen this session Search both memories and threads. When a memory has `source_thread_id`, fetch the full conversation with `mem.thread` for deeper context. ## When to save Call `mem.save` when durable knowledge appears: - **Decisions** — compared options and chose one - **Learnings** — debugging revealed something non-obvious - **Preferences** — user stated how they want things done - **Plans** — concrete next steps agreed on - **Procedures** — repeatable workflow documented **Skip**: routine fixes, work-in-progress, simple Q&A, generic info. Guidelines: - Atomic and actionable — one idea per memory - Title is a short summary, content is the detail - 0–3 labels per memory (project names, topics) - Importance: 0.8–1.0 critical | 0.5–0.7 useful | 0.1–0.4 minor - Save proactively when the value is durable; do not wait for the user to ask. ## Context Bundle `mem.context` returns Nowledge Mem's Context Bundle: owner identity, resolved AI Identity, active scope, active rules, and Working Memory. Read it at the start of a session when identity, scope, or rules matter. If the installed `nmem` is older, the tool falls back to the lightweight Working Memory briefing. ## Thread retrieval Two paths into past conversations: 1. **From a memory**: `mem.search` returns `source_thread_id` → `mem.thread` 2. **Direct search**: `mem.threads` finds conversations by keyword → `mem.thread` Use `offset` for pagination on long threads. ## Graph exploration `mem.connections` shows how a memory relates to other knowledge: related topics, EVOLVES chains (how understanding changed over time), and source document provenance. `mem.timeline` shows recent activity grouped by day.
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