slm-recall

Solid

Search and retrieve facts, decisions, and past context from SuperLocalMemory. Use when the user asks to recall, find, search, or "what did we decide/say about X". Triggers multi-channel semantic retrieval with reranking; always call before storing anything new.

AI & Automation 199 stars 34 forks Updated today AGPL-3.0

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Skill Content

# slm-recall — Search & Retrieve Memory Retrieve stored facts, decisions, and past context from SuperLocalMemory using multi-channel retrieval. The golden rule: **recall before you remember**. --- ## When to use recall vs search vs fetch vs list_recent | Situation | Tool | |-----------|------| | Conceptual or paraphrase query ("what did we agree on for auth?") | `recall` — full multi-channel retrieval + rerank | | Exact keyword match needed ("find facts containing BM25") | `search` — FTS5 BM25 only, lower latency | | You have a specific `fact_id` from a prior result | `fetch` — exact lookup, full detail | | Browse newest entries without a query | `list_recent` | Use `recall` as the default. `search` is a fallback for zero-result recall on a known exact term. `fetch` is for when you already know the ID. --- ## Recall-before-remember discipline Before storing anything new, always call `recall` first. If a near-duplicate fact already exists, call `update_memory(fact_id, content)` to refine it rather than creating a duplicate. Duplicates degrade retrieval quality for every future session. --- ## MCP-first workflow ### 1. Standard recall ``` recall( query="authentication strategy decision", limit=20, # default 20; reduce to 5 for quick pre-task checks session_id="<sid>", # pass the session_id returned by session_init fast=False, # default False; True enables faster, reduced-channel retrieval ) ``` Real response shape (`--json` equivalent):...

Details

Author
qualixar
Repository
qualixar/superlocalmemory
Created
5 months ago
Last Updated
today
Language
Python
License
AGPL-3.0

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