Results for “memory-index”

Explains how claude-mem captures observations from Read, Edit, and Bash actions, when memory injection activates, and where data is stored locally.

Estimates the memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub, using HTTP Range requests without downloading weights.

Indexes Megatron Bridge recipes and recommends the best starting config based on model, GPU count, and training goal.

Guides database index selection, creation, and maintenance with SQL examples for PostgreSQL and MySQL.

通用记忆抽象接口。当定义记忆结构、记忆检索、记忆关联时调用此技能。

Persists and recalls agent knowledge across sessions using four approaches: static KB, company wiki, persistent memory, and session brain.

Designs persistent memory architectures for AI agents, covering cross-session knowledge retention, entity tracking, temporal validity, graph/vector retrieval, and memory consolidation.

Stores, retrieves, and curates persistent facts in a structured memory map, distinguishing between user facts and reference text.

Hash Indexes

Audits a codebase for scalability bottlenecks, flagging N+1 queries, missing indexes, unbounded result sets, blocking operations, and memory pressure, then outputs a prioritized fix list.

Compress bloated project or global memory while preserving decisions, rationale, revisit triggers, provenance, and active user preferences. Load when memory exceeds budget, global memory is too large, session logs are repetitive, or before appending to an over-budget memory file.

Memory System Management

Multi-tiered memory management (HOT/WARM/COLD) for context compaction. Invoke ONLY for explicit compaction events: post-`/compact` cleanup, MEMORY.md tier promotion, archive batch, or "trim my context". NOT for general recall (use deep-recall) or routine memory writes (use storage-router). Triggers: "compact memory", "promote to durable", "archive old context", "tier this".

Scout research skill — Idea Memory

Provides persistent, self-hosted semantic memory for AI agents via MCP, storing worklogs, decisions, and notes in PostgreSQL with pgvector for meaning-based retrieval across sessions.

Self-hosted semantic memory for AI agents via MCP. Save worklogs, decisions, and notes, then recall them across sessions by meaning, not keyword. Postgres + pgvector with auto-tagging.

Integrates Mem0 Platform SDK for persistent memory in AI applications. Use when building agents or chatbots that need to remember user preferences, past interactions, or personalized context across sessions. Covers Python and TypeScript SDKs, plus LangChain, CrewAI, OpenAI Agents, LlamaIndex, AutoGen, and LangGraph integrations.

Guides the 7-step MATLAB memory optimization workflow: baseline, profile, identify, optimize, measure, verify, report. Use when asked to reduce MATLAB memory usage, find memory bottlenecks, fix out-of-memory errors, or optimize memory-intensive code.

Save information to persistent memory for cross-session recall. Stores preferences, conventions, decisions, and context.

Structured memory system with domains, decay, and links for AI agents, replacing flat memory files with a weighted, interconnected memory palace.

Search and retrieve past work from claude-mem's persistent cross-session memory database using a three-layer workflow: search, timeline, and fetch.

FREE — God-tier long-context memory for AI agents. Injects 500K-1M clean tokens, auto-summarizes with tone/intent preservation, compresses 14-turn history into 800 tokens.

Ingests case studies, past proposals, and boilerplate from library/inbox into structured indexes. Use when the user requests to 'index proposal library', 're-index case studies', 'ingest inbox docs', or 'validate library'.

Persistent cross-session memory for AI agents. Mem0 stores user preferences, past decisions, domain knowledge, and agent learnings across all sessions, all tools, and all users. Complements planning-with-files (task-level memory) with long-term agent intelligence (CRM + personal knowledge base layer). Use when asked to "remember this", "store preference", "mem0", "long-term memory", "user memory", "agent memory", or when building multi-session agents that need to recall past interactions.

FREE — God-tier long-context memory for AI agents. Injects 500K-1M clean tokens, auto-summarizes with tone/intent preservation, compresses 14-turn history into 800 tokens.

Search conversation history and access Dream-managed profile and memory files without editing them.

Promote project-specific memories into strict, small global memory only when they are cross-project, stable, useful, safe, and worth the global context cost. Load when the user says make this global, remember across projects, save this globally, or promote this learning.

Compresses conversation transcripts into prioritized notes using an LLM observer, consolidates them when they grow, and recovers any missed sessions without a database or vector store.

Memory health dashboard showing line counts, topic files, capacity, stale entries, and recommendations.

Analyze codebase structure, generate domain-specific sub-skills (UI, Backend, Database, etc.), and create agent-guidance files to help AI agents navigate and develop consistently within a project. Use when onboarding to a new codebase, creating project documentation, or setting up agent guidance systems.

Retrieve task-relevant project and global memory without loading everything. Load when the user asks what we decided, recall prior context, find memory about a feature, explain past rationale, resume a task, or check deferred ideas.