---

name: memory-and-search description: "Routes Memori attribution, sessions, recall/search, embeddings, and native Rust-core workflows." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

Memory and Search

Use this sub-skill for Memori's memory lifecycle, recall/search behavior, embeddings, augmentation timing, and native Rust-core troubleshooting.

Use when

• The request mentions attribution, sessions, recall, empty recall, search, embeddings, TEI, augmentation wait, native runtime behavior, or memory deletion.
• The user is debugging why memories are not showing up or why the Rust core path is unavailable.
• The task is about retrieval behavior rather than database setup or provider registration.

Read first

• references/memory-lifecycle.md for attribution and session flow.
• references/search-and-embeddings.md for search and embedding APIs.
• references/native-and-embeddings.md for native-core and model notes.
• references/troubleshooting.md for recall, wait, and native failures.
• scripts/search_candidates_smoke.py for a safe offline search smoke.

What this sub-skill owns

• attribution(...), new_session(), set_session(...), recall(...), and delete_entity_memories(...).
• search_facts(...), FactCandidate, FactSearchResult, and candidate-mode search.
• embed_texts(...), TEI(...), and the native Rust core fallback path.
• Short-script augmentation advice such as augmentation.wait().

What it does not own

• Cloud API keys and MCP setup: use cli-and-cloud.
• Storage schema and provisioning: use byodb-storage.
• Provider/framework registration: use llm-integration.
• TypeScript-specific memory APIs: use typescript-sdk.

Safe first check

Run the bundled search smoke before assuming retrieval is broken:

python scripts/search_candidates_smoke.py

That helper exercises pre-scored candidates and does not require a database, network, or embedding model download.