「forgetting」の検索結果

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

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Memory systems specialist for hierarchical memory, consolidation, and outcome-based learningUse when "memory system, memory hierarchy, memory consolidation, forgetting strategy, salience learning, outcome feedback, temporal memory levels, entity resolution, memory, zep, graphiti, mem0, letta, hierarchical, consolidation, salience, forgetting, ml-memory" mentioned.

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。

Audit, classify, and selectively forget stored memories. Covers memory enumeration and classification by type/age/access frequency, staleness detection for outdated references, fidelity checks using external anchors, a decision tree for selective withdrawal, tombstoned deaccession so removed content stays recoverable, counter-memory inoculation for failed strategies that would otherwise be re-derived, preemptive filtering rules for what should never become memories, and an audit trail so forgetting itself is reviewable. Use when memory has grown large and uncurated, when project state has shifted significantly since memories were written, when retrieval quality has degraded, or as periodic maintenance alongside manage-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。

Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).

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。

Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).

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。

Gate fine-tuned checkpoints with drift budgets, paired comparison, and forgetting checks before promotion. Use after a training run produces a checkpoint, when deciding whether a tuned model ships, or when a promoted model needs re-gating against updated goldens.

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 Comet must decide whether a bounded semantic review packet contains a personal memory worth creating, updating, forgetting, or skipping.

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。

SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.

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。

Run a company event — the launch party, the customer day, the team celebration — as the operation it is: the goal that shapes every choice, the budget with its forgotten lines, the vendor and venue coordination, the run-of-show with owners, and the day-of roles that keep hosts hosting. Use when asked plan the company event, organize our customer day/holiday party/launch event, what am I forgetting for this event, or be the run-of-show for Thursday. Produces the goal-shaped plan, the budget with the forgotten lines, the run-of-show, and the day-of role card.

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.

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Universal skill diagnosis and optimization tool. Detect and fix skill execution issues including context explosion, long-tail forgetting, data flow disruption, and agent coordination failures. Supports Agy CLI for deep analysis. Triggers on "skill tuning", "tune skill", "skill diagnosis", "optimize skill", "skill debug".

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 for any task that spans more than a couple of steps, files, or sessions — refactors, migrations, multi-file features, anything that must survive a context reset or crash. Trigger when the user asks for a plan, says "keep track of this", "don't lose the thread", or when a task is large enough that forgetting step four is likely.

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 a coding session is burning context fast, output is drowning in noise, the model keeps forgetting earlier decisions, or the user asks to reduce tokens, stop the verbose narration, work in a huge repo, or make a long session survive. Covers what to read, what to summarize, what to write to disk, and when to start fresh.

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。

Vocabulary learning strategies and retention science for any language -- frequency-based word selection, spaced repetition systems (Ebbinghaus forgetting curve, Leitner system, SM-2 algorithm), cognate exploitation, word family networks, context-based learning, collocations, depth of word knowledge (form, meaning, use), reading progression from controlled to authentic texts, and productive vs. receptive vocabulary thresholds. Use when building vocabulary plans, optimizing review schedules, selecting learning materials, or diagnosing vocabulary gaps.

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。

This skill should be used when the user wants to build, set up, or operate an AI "second brain" — a persistent memory vault (Obsidian-compatible Markdown) that lets an AI agent resume work without re-explaining context, remembers correction instructions across sessions, and auto-organizes raw material into structured knowledge. Trigger on requests like "build my second brain", "set up an Obsidian vault for AI", "give my AI persistent memory", or "make AI stop forgetting".

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。

Rebalances context across Memory, Custom Instructions, knowledge files, and User Preferences in Claude Projects. Audits Memory for redundancy, staleness, and misplacement; prescribes optimization including Codification of stable Memory patterns into explicit User Preferences or Project CI rules. Use when user says "optimize my memory," "what should be in my memory," "trim my knowledge files," "reduce my context usage," "rebalance my project," "what should be in my preferences vs memory," or asks whether something belongs in Memory, a knowledge file, or User Preferences. Also trigger on symptom-phrased: "my project feels bloated," "Claude keeps forgetting things," "my context window keeps hitting limits." Activate whenever Memory-layer balance is the primary concern. Do NOT use for full Project audits (use rootnode-project-audit if available), single-prompt evaluation (use rootnode-prompt-validation if available), or global-layer audits that don't touch Project Memory (use rootnode-global-audit if available).

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。

Guide a person through structured learning of a new topic, technology, or skill. AI acts as learning coach — assessing current knowledge, designing a learning path, walking through material, testing understanding, adapting difficulty, and planning review sessions for retention. Use when a person wants to learn a new technology and does not know where to start, when someone feels overwhelmed by documentation, when a person keeps forgetting material and needs spaced repetition, or when transitioning between domains and needing a gap analysis.

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State management decision tree for React SPAs: useState/useReducer, Context API, Zustand, Jotai, Redux Toolkit, TanStack Query, SWR. Detect what's installed and apply matching patterns. Use this skill to: - Pick the right state tool for the data shape (local / shared UI / server / form). - Use Context correctly (and know when to switch to Zustand/Jotai). - Implement a Zustand or Jotai store. - Set up TanStack Query for server state with caching, pagination, mutations. - Avoid common mistakes (overusing Redux, prop drilling, forgetting query invalidation). Do NOT use this skill for: - General hook conventions (see react-conventions). - Form state specifically (see react-forms). - Routing state (see react-routing). - Testing stores/queries (see react-testing).

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。

Unity NPC behavior design-to-code translation. Perception system architecture, decision layer patterns, action execution pipeline, faction & relationship systems, NPC memory & forgetting, crowd & squad coordination. DESIGN INTENT format: INTENT/WRONG/RIGHT/SCAFFOLD/DESIGN HOOK. Based on Unity 6.3 LTS.

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。

Guides AI agents in using Tree Ring Memory for durable recall, project decisions, user preferences, warnings, future seeds, privacy-safe memory capture, and lifecycle-aware forgetting.

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Architect persistent, searchable memory for agents — episodic, semantic, and procedural stores with retrieval, consolidation, and forgetting policies. Use when designing how an agent remembers across sessions.

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。