280 件 ・ 関連度順
概要と使いどころ
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.
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。
覚えたい事実を質問と答えのカードにし、回答に応じた間隔で復習できます。YouTube動画からのクイズ作成や、分野別の整理、CSVの入出力にも対応します。
Beta readers for any draft, run by simulating how a real reader experiences it, moment by moment. A skim gate, a no-lookahead timed read producing an attention transcript with quit points, a recall test of what a reader remembers the next day, and a trust ledger of the implied author. Use when the user asks for a beta read, beta readers, test readers, human review, reader review, to read something like a human, to be a first reader, whether a piece holds attention or will actually get read, for a quick read of whether a busy skimmer would even open a post, what readers will remember or take away tomorrow, where readers stop reading or bounce, or why a draft still feels off or hollow after anti-slop or humanizer edits. Final gate before publishing; reports where the reading broke; never rewrites.
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。
Use a file-based PARA memory system to store, retrieve, and organize durable knowledge across sessions. Trigger on saving facts, daily notes, entity records, weekly synthesis, recall, tacit user patterns, or plan memory.
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。
Search screen and audio history recorded by Screenpipe and optionally save selected context into claude-mem. Use when the user asks to recall desktop activity, a meeting, or something seen in another app through Screenpipe.
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。
Stores approved context for one AI team and searches it later, with provenance. Use when the user asks to remember a decision, constraint, or fact for the team, or to recall what the team already knows. Memory is separate for each team and tenant.
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。
Query AgentDB through the controller bridge -- semantic routing, hierarchical recall, causal graphs, context synthesis, pattern store/search
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。
MemPalace — Local AI memory with 96.6% recall. Semantic search, temporal knowledge graph, palace architecture (wings/rooms/drawers). Free, no cloud, no API keys.
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。
MemPalace — mine projects and conversations into a searchable memory palace. Use when the user asks about MemPalace, memory palace, mining memories, searching memories, palace setup, wings, rooms, or drawers; or when they want to recall past work that may already be filed in their palace.
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。
构建或调优 RAG 检索管道时使用——涵盖文档分块策略与参数、稠密嵌入（BGE-M3、余弦相似度、ANNOY/HNSW 选型）、稀疏嵌入（TF-IDF 到 BM25）、混合检索三阶段（并行召回、RRF 融合、跨编码器重排序）与 recall@k/MRR/nDCG 指标，含稀疏 vs 稠密的胜负判断。
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。
Store team knowledge, project conventions, and learnings from tasks. Use to remember what works and recall context before new tasks. Connects to a self-hosted Hindsight server. (user)
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。
Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user)
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。
Search long-term memory for relevant context from past coding sessions using Hindsight MCP tools
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。
Carry state between runs of a scheduled routine using Hindsight memory. Use when creating or editing a routine, to write the recall and retain steps into the routine's own instructions, and at the start and end of every scheduled or event-triggered routine run.
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。
Store team knowledge, project conventions, and learnings from tasks. Use to remember what works and recall context before new tasks. Connects to Hindsight Cloud. (user)
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。
Long-term memory for the agent via Hindsight. Use to recall relevant past context before answering, retain durable facts as you learn them, and reflect over accumulated memory for the "why" behind a decision. Load whenever continuity across sessions matters — the user refers to earlier work, states a lasting preference, or asks a question that prior context could answer.
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。
Semantic + keyword search and connection-discovery across the user's own Apple Notes via the apple-notes MCP server. Use when the user wants to find, recall, or synthesize something from their notes, or surface non-obvious bridges/related notes. macOS, on-device.
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。
Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.
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。
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
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。
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
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。
Find, evaluate, and assemble the smallest compatible set of AI Agent Skills for an end-to-end natural-language goal. Use when a user wants Skills for a multi-step workflow, asks which Skills fit a project, needs an installed-Skill audit or conflict check, has low Skill recall, wants indirect helpers such as humanizers or compliance checks, or wants a project-specific Skill Stack with controlled installation. Search local Skills, registries, GitHub, and OpenCLI; compare adoption, verified fit, safety, and overlap. Do not use for locating one known or common Skill; use the generic find-skills workflow.
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。
Recall and persist long-term memory through the OpenViking MCP tools. Use at the start of any substantive task (coding, configuration, debugging, multi-step or tool-based work) to retrieve relevant prior knowledge with find/search/read, and during or after work to persist durable facts, preferences, decisions, and lessons with remember. Do not use for casual chat or simple factual questions the model can answer directly.
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。
Use when the user wants to install cognee and run their first remember → recall flow with the Python SDK — fresh setup, virtual env, extras selection, or a minimal working example.
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。
Use when the user wants to drive cognee from the terminal with cognee-cli — remember/recall/forget/improve memory commands, managing datasets and config, or database migrations.
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。