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SkillsMP has collected 24 skills from ThinkfleetAI/memmesh. Open a skill to review its source and details.
Showing 24 of 24 collected skills.
Surface emergent behavior patterns MemMesh has mined from a subject's history — recurring habits nobody predefined, each with prevalence, stability, and the evidence behind it. Use when the user asks "what patterns do you see", "what are this user's habits",…
Load relevant MemMesh context before starting work — searches memory and, for a specific subject, assembles a token-budgeted bundle (profile + behavior patterns + forward predictions + top memories) in one call. Use when beginning a task, switching context,…
Query MemMesh's bi-temporal knowledge graph — multi-hop reasoning across entities, point-in-time "what did we believe on date X", and anticipatory retrieval via spreading activation. Use for questions no single stored fact answers, or to see how knowledge…
Forecast what a subject will do next from their mined behavior patterns — with a calibrated, horizon-decayed confidence and provenance. Use when the user asks "what is this user/account likely to do next", "will X churn/convert/ reorder", or wants a forward…
Explain a MemMesh prediction or recalled fact — surface its provenance (evidence memories), its calibrated confidence, and whether the model abstained and why. Use when the user asks "why do you think that", "what's this based on", "how sure are you", or…
Run MemMesh's competitive benchmark harness (LOCOMO / BEAM) to compare retrieval quality, tokens, latency, and cost against Mem0, Zep, full-context, and naive-RAG baselines. Use when the user wants proof MemMesh is better, is evaluating a migration, or asks…
Consolidate MemMesh memories — find duplicates and contradictions, merge or supersede them, and retire stale entries — to keep search results clean. Use when memory count is high, search feels noisy/repetitive, or for periodic hygiene. Respects pinned…
Export MemMesh memories for a project/user to a portable Markdown (or JSONL) file for backup, migration, or sharing. Use when backing up, moving to another project, sharing memory state with teammates, or archiving before a cleanup.
Delete or correct a MemMesh memory. Finds the item by search or id, confirms, then soft-deletes (default, sync-safe) or hard-deletes (GDPR/cleanup). Also handles "undo that" for a memory just added, and corrections via supersede. Use when removing…
Diagnose MemMesh connectivity and correctness — is the MCP server reachable, is the key/config valid, do read and write actually work? Use when memory operations fail, searches return empty unexpectedly, observe/save errors occur, or to confirm the plugin is…
Import memories into MemMesh from an exported file, a native MEMORY.md / CLAUDE.md, an ADR/decision log, or a mem0 export. Use when migrating from another project, restoring a backup, or seeding a new project with existing knowledge.
Set up MemMesh for a new project — verify the MCP server is wired, pick local vs hosted, import any existing project knowledge (MEMORY.md, CLAUDE.md, a mem0 export), and seed initial scopes. Use on first run in a repo, when the API key changes, or to re-run…
Quick memory lookup — search MemMesh and show compact one-liner results, or fetch one memory by id. Use for fast checks ("did we record X?"), resolving a [memmesh:id] citation, or browsing without full detail.
Protect a critical MemMesh memory from consolidation/pruning by raising its importance and marking it high-impact — or unpin to release it. Use for architecture decisions, security constraints, or immutable team conventions that must never be retired by a…
Store a memory in MemMesh from the user's input. Prefers memory_observe (the engine decides what to extract) and falls back to memory_save only for verbatim/structured notes. Use when the user says "remember this", "save this", "note that", "from now on", "we…
Show MemMesh usage stats for a project/user — total count and breakdowns by type, scope, and status, plus the oldest/newest timestamps. Use when checking "how many memories do I have", auditing distribution before a cleanup, or giving a quick health read.
Override the auto-detected project scope for MemMesh reads/writes, or widen to cross-project / user-level search. Use when working across repos, pulling a decision from another project, or when auto-detection resolved to the wrong projectId.
Browse all stored MemMesh memories for a project/user, grouped by type/scope with full content. Use when reviewing everything captured, onboarding to a project, or getting an overview of decisions, conventions, and learnings.
MemMesh CLI + local MCP server — the zero-infra, no-API-key path to the same engine as the hosted SDK. Runs fully local over SQLite. Covers install (wires MCP config + the teaching skill into Claude Code / Cursor / Windsurf / Codex), and the memory…
Integrate MemMesh into an existing repository using a goal-driven, test-first (TDD) pipeline. Detects the repo's language/stack, asks whether to use MemMesh Hosted (SDK, managed) or Local (CLI + MCP over SQLite), writes failing tests before any…
Migrate an existing memory setup ONTO MemMesh — either from another vendor (Mem0, Zep, Letta/MemGPT, a raw vector store) or from MemMesh Local (SQLite) up to MemMesh Hosted. Audits the current usage, produces a reviewable migration plan (API mapping + data…
MemMesh TypeScript SDK reference (@thinkfleet/memory-sdk) for the hosted platform at app.memmesh.ai. Covers the ThinkFleetMemory client — observe / search / list, the predict + lattice prediction surface, closed-loop learning (recordDecision / recordOutcome),…
Verify a MemMesh integration produced by memmesh-integrate. Runs in the same workspace: executes the repo's native test suite, then exercises a real end-to-end smoke flow (observe → search → optionally predict) against the user's live key or local engine, and…
Persistent hierarchical memory shared across every AI tool the user runs. The engine decides what's worth saving — your job is just to feed it raw text via `memory.observe` and to recall via `memory.search` when context would help. Use both on every session.