Search claude-mem's persistent memory index for relevant prior work across sessions.
npx skills add https://github.com/dwaynimay/IoTProject --skill mem-search-dwaynimay
Please help me install this Agent Skill. Skill: mem-search Source: https://github.com/dwaynimay/IoTProject/tree/main/.agents/skills/mem-search Command: npx skills add https://github.com/dwaynimay/IoTProject --skill mem-search-dwaynimay
It helps you quickly determine whether a question, bug fix, or implementation detail was solved in earlier sessions, so you don’t repeat work and can reuse proven context.
Use the mem-search skill to search for prior work on the topic by asking: “Did we already solve authentication token expiration in a previous session, and what was the fix?”
High-intent search queries and answers about installing and using this skill.
To avoid repeating work, cross-session memory search queries a persistent index using keywords and optional filters like project name, date range, and type to retrieve relevant prior decisions. This mechanism helps you reuse proven context and skip redundant debugging or implementation steps.
The best way to retrieve feature history while optimizing tokens is a search-then-filter workflow. You search and filter the memory index first to establish relevance, then fetch full details only for selected IDs, significantly reducing token usage compared to pulling all records immediately.
Timeline context anchors your search by pulling surrounding records around key results, helping you understand what led to a specific outcome or decision. This provides broader context than a single entry, allowing you to trace the progression of a bugfix or feature implementation across sessions.
Yes, you can filter cross-session memory search results using optional constraints for a specified project, date range, and record type. These filters narrow the search scope, ensuring you only retrieve observations and decisions relevant to the specific session you are investigating.
No, cross-session memory retrieval uses a safe retrieval workflow that avoids fetching full details before relevance is established. It uses MCP tools to search, apply timeline context, and retrieve observations, fetching full records only for the specific IDs you select.
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