「recall」の検索結果

Add persistent graph-based memory via mnemon. Agents recall past context before responding and remember insights after each turn.

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Drive a native GUI app (macOS, Windows, Linux) via the cua-driver CLI (default) or MCP server; snapshot its accessibility tree, act through snapshot-bound element tokens, native menu paths, exact window geometry, or pixel coordinates, and verify from fresh state. Use when the user asks you to operate, drive, automate, or perform a GUI task in a real application on the host, or to continue, resume, or recall recent Cua activity.

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The session loop that makes agentmemory pay off, recall before starting work, save at decision points, learn from corrections. Use when starting a nontrivial task, after settling a decision or debugging a gotcha, or whenever deciding if something belongs in memory.

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Use this skill when the user asks to save, recall, find, or organize notes. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check the notebook', 'find in carnet'. Also use proactively when discovering findings worth preserving across sessions.

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Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).

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Use when the user asks to deeply read a book, article, PDF, or document set; extract claims and evidence; build a knowledge map; or learn through Feynman explanation and recall. Covers quick, deep, map, Feynman, and whole-book reading modes.

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Find a past decision, document, or conversation in Screenpipe with source links.

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Search what was said in existing screenpipe, Codex, Claude, Cursor, and Gemini CLI chats, then continue or steer one exact chat when the user explicitly asks. Use for requests about finding another agent conversation, recalling what an agent said, or sending work to one.

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Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.

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Deliberately search, add to, or curate your long-term memory with the `memory` tool — beyond the automatic recall/capture every turn already does. Use when asked who someone is or what they're working on, when you learn a durable fact worth keeping now, or when your notebook holds stale or wrong entries you should clean up.

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Use the MemOS Local memory system to search and use the user's past conversations. Use this skill whenever the user refers to past chats, their own preferences or history, or when you need to answer from prior context. When auto-recall returns nothing (long or unclear user query), generate your own short search query and call memory_search. Available tools: memory_search, memory_get, memory_write_public, memory_share, memory_unshare, task_summary, skill_get, skill_search, skill_install, skill_publish, skill_unpublish, network_memory_detail, network_skill_pull, network_team_info, memory_timeline, memory_viewer.

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Guide the agent to recall, remember, and route durable learning into Memory, Skills, Scheduled Tasks, or Tape.

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Use the enabled Memcode MCP connection to explicitly save approved facts and recall relevant memories across DeepChat conversations. Never save a transcript or retrieve memory automatically.

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Use Nowledge Mem for cross-tool startup context, recall of prior decisions, durable learnings, explicit conversation saving, and connection diagnosis. Use only the connection selected for this DeepChat agent or session.

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Prepare a concise exam review from study materials and a syllabus supplied by the user. Use for topic summaries, recall questions, MCQ cues, and time-limited revision plans that must stay grounded in those materials.

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Recommend practical study strategies matched to the material, learning goal, assessment, time, and learner constraints. Use for revision planning, homework routines, independent study, replacing ineffective habits, or adapting recall, spacing, explanation, and practice activities.

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Diverga Memory System v7.0 - Context-persistent research support with checkpoint auto-trigger and cross-session continuity. Triggers: memory, remember, context, recall, checkpoint, decision, persist, 기억, 맥락, 세션, 체크포인트

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Store a learning, pattern, or decision in the memory system for future recall

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Query the memory system for relevant learnings from past sessions

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Search past reasoning for relevant decisions and approaches

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Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.

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Checks what a shipped AI product's system prompt and tool schema actually say by reading a dated public archive instead of recalling them. Use when the user asks what some agent is instructed to do, quotes a system prompt and wants it verified, shows an extracted or leaked prompt, or compares how two products are set up. Read-only; fetches public files from github.com.

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Evaluate retrieval and citation behavior for RAG pipelines from deterministic JSONL fixtures. Use when an agent needs offline Recall@K, reciprocal rank, context precision, citation coverage, citation validity, diagnostics, Markdown/JSON reports, or threshold-gated evaluation in CI.

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Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.

日本語の概要は準備中です。原文の説明を表示しています。