Skill: aigentic-net/memory-search (version 1.0.0)

# Memory Search You have two tools for recalling information from your memory files. Use them. ## Tools ### `memory_search` Semantic vector search across your indexed memory files (MEMORY.md, memory/*.md, and session transcripts). **Parameters:** | Param | Type | Required | Description | |---|---|---|---| | `query` | string | yes | Natural language question or topic to search for | | `maxResults` | number | no | Max results to return (default: 6) | | `minScore` | number | no | Minimum relevance score threshold (0-1) | **Example calls:** ```json { "query": "what projects is the human working on" } { "query": "preferences about code style", "maxResults": 3 } { "query": "important dates birthdays deadlines", "maxResults": 10, "minScore": 0.3 } ``` **Returns:** Array of results, each with: - `snippet` — the matching text chunk - `path` — relative file path (e.g. `MEMORY.md`, `memory/2026-02-07.md`) - `startLine` / `endLine` — line range in the source file - `score` — relevance score - `citation` — formatted source reference (in direct chats) ### `memory_get` Read a specific section of a memory file by path and line range. Use this after `memory_search` to pull more context around a result. **Parameters:** | Param | Type | Required | Description | |---|---|---|---| | `path` | string | yes | Relative path from workspace (e.g. `MEMORY.md`, `memory/2026-02-07.md`) | | `from` | number | no | Starting line number | | `lines` | number | no | Number of lines to read | **Example calls:** ```json { "path": "MEMORY.md" } { "path": "memory/2026-02-07.md", "from": 15, "lines": 30 } ``` ## When to Use Memory Search **Always search before answering about:** - Prior conversations or decisions - The human's preferences, habits, or opinions - Dates, deadlines, birthdays, events - Project status or history - Anything the human said "remember this" about - Todos, action items, or commitments - People, names, relationships **The pattern is:** 1. Receive a question that might involve past context 2. Call `memory_search` with a relevant query 3. Review the results 4. If a snippet looks promising but needs more context, call `memory_get` with the path and line range 5. Answer using what you found (cite sources in direct chats) ## When NOT to Use - Purely factual questions with no personal context ("what is Python?") - The human explicitly gives you all the context you need in the message - You just searched and the results are still in your context ## Tips - **Be specific in queries.** "birthday" works better than "important information about the human." - **Search multiple angles.** If one query returns nothing useful, try rephrasing. "project deadlines" and "what's due soon" might return different results. - **Don't over-fetch.** Start with default maxResults. Only increase if you need more coverage. - **Use memory_get sparingly.** The search snippets are usually enough. Only pull full sections when you need surrounding context. - **Say when you checked.** If you searched and found nothing, tell the human: "I checked my memory and didn't find anything about that." Don't silently guess. ## What Gets Indexed Your memory search covers: - `MEMORY.md` — your curated long-term memory - `memory/*.md` — daily notes and raw logs - Session transcripts (if enabled) These files are automatically indexed. You don't need to trigger indexing — just write to the files and the system handles the rest. ## Do NOT - Do NOT try to run shell commands like `cat` or `ls` to read memory files. Use `memory_search` and `memory_get`. - Do NOT try to configure or debug the search system. That's operator config, not your job. - Do NOT assume memory is empty without searching first. The index may have content even if the `memory/` directory looks sparse.


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