Persists context across conversations as plain markdown so every future session can enrich a topic-scoped memory (e.g. `project-acme`). Four operations: `write` (extract candidates, resolve as ADD / UPDATE / DELETE / NOOP per Mem0), `read` (load a ≤ 200-line INDEX, fetch detail on demand), `consolidate` (sleep-style merge + prune), `forget` (delete or redact with audit). Three storage tiers: home (`~/.agent-memory/<scope>/`, default), project-local (gitignored), project-shared (committed). Enforces a never-store list (secrets, keys, financial and identity numbers) and a consent preview before every write. `rules/scaling-tiers.md` covers scaling to SQLite FTS, vector DB, and managed memory, plus the LoreKit backend the self-improvement loops run on: scope mapping, the `loop::<skill>-lessons` tag and key convention, and the shared lesson schema. Triggers on "remember this", "save to memory", "recall memory", "what do you remember about", "consolidate memory", "forget that", "/persistent-memory".
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Low-risk findings worth noting
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
The persistent-memory write pipeline extracts candidate facts from the conversation turns and user-provided inputs, which can contain outsider-authored free text if an integrated host workflow processes external inputs.
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