「memory」の検索結果

Use when Codex should remember, recall, search, improve, or forget information using Cognee.

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 switch this Codex session to another Cognee dataset, see which datasets are available, or move memory capture to a different dataset. Syncs the current session first, then starts a fresh Cognee session bound to the chosen dataset.

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。

Sync session cache entries into the permanent Cognee knowledge graph. Run this to make session memory searchable, or it runs automatically at session end.

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 Antigravity should remember, recall, search, improve, or forget information using Cognee.

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。

Diagnoses and improves measured latency, throughput, memory, CPU, bundle, rendering, query, or resource performance by defining a target, profiling a representative workload, locating the bottleneck, making one change at a time, and comparing before/after evidence. Use for performance work or regressions. Not for guessing optimizations without a reproducible workload or for generic refactoring.

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。

Build/install/launch proof, ANR/jank/memory triage, and stale-build debugging for native Android apps. Use when runtime truth, performance, or crash root cause is in doubt.

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。

Proves iOS build/install/launch truth and triages hangs, crashes, jank, memory kills, and stale builds. Use when simulator, bundle, or runtime performance state is in doubt.

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。

Adds per-skill learnings loops for dated patterns, mistakes, and domain facts. Use when wiring skill memory, consolidation, or drift audits.

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。

Owns instruction files: AGENTS.md, CLAUDE.md, personal and repo rules. Use when writing, pruning, auditing them, sharing rules across Claude and Codex, or fixing ignored rules.

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。

Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.

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。

Expert skill for building CLI applications with Cobra and Viper, authored by spf13 — the original creator of both libraries. Covers command-first architecture, decoupled business logic, configuration management, environment variable binding, context-aware commands, and in-memory CLI testing. Use whenever a Go CLI or command-line tool is being built, reviewed, or refactored — any mention of commands, subcommands, flags, or CLI configuration, even if Cobra or Viper aren't named yet (recommend them).

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。

Initialize an AgentDB Cognitive Container (.rvf file) in the current project. Sets up storage, embedder config, and the agentdb MCP server. Use when the user is starting a new project that needs vector memory, or asks to "set up agentdb" / "init agentdb".

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 memories in tier-aware hierarchical memory — working / short-term / long-term — and recall with tier filters. Use for working-set context that should fade, vs facts that should persist, vs patterns that should be searchable forever.

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。

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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。

Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.

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。

K-hop traversal from a starting node in AgentDB's graph. Use to explore neighborhoods, find reachable nodes, or visualize a memory's "context".

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。

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

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Manage Apple Reminders on macOS via remindctl — list, add, edit, complete, delete to-dos that sync to iPhone/iPad. Trigger on "reminder", "Reminders app", "提醒我" with phone sync, or adding personal todos with due dates. macOS only. Skip when the user wants agent-internal alerts (use memory_store or external scheduling) or calendar events.

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通过 remindctl 在 macOS 上管理 Apple Reminders——列出、添加、编辑、完成、 删除同步到 iPhone/iPad 的待办事项。在提及"提醒"、"Reminders app"、 需要手机同步的"提醒我"或添加带截止日期的个人待办事项时触发。macOS only。 当用户需要代理内部提醒（使用 memory_store 或外部 调度）或日历事件时跳过。

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。

Generate retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.

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。

Design a spaced retrieval schedule for any topic list and timeline. Use when planning units, term sequences, or revision programmes.

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。

Before any explanation or answer, require the learner to produce a free-recall attempt and confidence rating. Use when a student wants help understanding or reviewing a topic — this skill ensures the AI works from what the learner already knows.

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。

Redesign a blocked topic sequence into an interleaved plan with mixed practice across related topics. Use when planning units, homework schedules, or revision programmes.

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。

Explain and configure individual spacing algorithms using student performance data and forgetting curves. Use when personalising retention schedules in adaptive learning platforms.

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。