「memori」の検索結果

Mine a project or conversation into your MemPalace — extract and store memories for later retrieval.

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 your MemPalace — semantic search across all mined memories, projects, and conversations.

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Load relevant OpenViking experience memories via case-linked experience candidates before solving a task.

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Retrieve and apply OpenViking Experience memories through the Agent runtime's generic OpenViking search and read tools. Use before or during executable, multi-step, or tool-based work such as coding, file or data changes, configuration, deployment, workflow execution, and failure recovery when prior operational guidance could improve reliability. Do not use for casual chat or simple factual questions.

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Work with OpenViking, the persistent context database behind this agent's memory. Use it whenever the user refers to earlier sessions or shared history ("like last time", "what did we decide"), asks to remember or forget something, shares files, URLs, or repos worth keeping, or when the task needs context this session does not have — even if nobody says the word "memory". Also use it when the user asks where memories are stored: per project, per folder, or shared between repositories. Covers choosing between context search, find, list search, and grep, reading viking:// URIs, and when (not) to write.

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Diagnose OpenViking memory issues by tracing backward from a memory file to its archive memory_diff.json and, when needed, session messages. Read-only; use for incorrect content, wrong paths or owners, missing memories, and unexplained updates or deletes.

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Delete selected durable memories from agentmemory after showing them and getting explicit confirmation. Use when the user says "forget this", "delete memory", "remove that note", or wants to scrub specific data for privacy.

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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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Retrieve relevant durable BB memories or save verified knowledge useful to future threads.

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Ingest Hermes agent history into Obsidian as distilled knowledge. Use for importing or mining ~/.hermes sessions, memories, or logs; use wiki-agent for targeted topic-only cross-agent recall.

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Introspect, explain, and improve Ralph Orchestrator using its published llms.txt doc map. Use this skill whenever the user asks questions about Ralph's behavior, wants to understand how a Ralph internal works (event loop, hats, memories, tasks, backends, presets), debug an unfamiliar failure mode, or propose a code change to the ralph-orchestrator repo. The skill teaches the agent to discover authoritative answers from the live docs via llms.txt before guessing, and to scope improvements through the published architecture rather than the local checkout alone.

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Manage skills, agents, extras, hooks, plugins, and MCP connection settings with the Skillshare CLI. Use when the user asks to configure or run Skillshare, install or sync resources across AI tools, manage shared memory notes, review a session for lessons or dream over saved memories, import MCP settings, manage targets, audit skills, recover backups, or troubleshoot Skillshare configuration and sync. Covers global and project modes, noninteractive automation, and guidance for the terminal UI.

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Use the memmy-memory CLI when an agent needs to check the Memory service, open or close agent sessions, start or complete turns, search, add, read, or delete memories, or call uncommon HTTP routes through raw requests.

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Search relevant memories by keyword and optional type filter. When you need to recall past information, find user preferences, or check learned patterns.

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This skill should be used when working on Lean 4 formalization projects to maintain persistent memory of successful proof patterns, failed approaches, project conventions, and user preferences across sessions using MCP memory server integration

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Curates insights from reflections and critiques into CLAUDE.md using Agentic Context Engineering

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Build a small, coherent opening repertoire that fits your style and level — the few lines actually worth learning, plus the plans and traps behind them. Use when asked what chess opening should I learn, build me a repertoire, help with my openings, or what to play against [opening]. Produces a compact repertoire for White and Black keyed to your rating and style, the main ideas and typical plans (not just moves to memorize), the common traps to know from both sides, and what to study next — kept small enough to actually learn.

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Store and recall with Supermemory: add memories and documents, hybrid search, upload files, tune settings. Trigger phrases: supermemory, memory engine, remember this.

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Ricerca giuridica su fonti italiane e UE: normativa, prassi e giurisprudenza in ogni materia (civile, penale, amministrativo, lavoro, tributario e specialistiche). Usare quando l'utente chiede di trovare, inquadrare o citare una norma ("cosa dice l'articolo...", "quale legge disciplina...", "è ancora in vigore...", "che estremi ha..."), cercare sentenze, massime o orientamenti, verificare la vigenza di una disposizione, verificare una citazione o l'esistenza di una pronuncia ("questa sentenza esiste?", "controlla le citazioni di questo atto"), cercare precedenti a favore e contro una tesi ("conformi e difformi", "sentenze pro e contro"), trovare un CCNL o le decisioni di ABF, ACF, AGCM o del Garante, creare un documento fondato su fonti (atti, pareri, clausole, contratti, memorie, diffide), o impostare una strategia processuale ("come imposto la causa", "come gestiresti questo caso", "conviene agire o transigere"), anche quando la fonte non è nominata. Non usare per domande non giuridiche.

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Generate elaborative interrogation prompts that deepen encoding through targeted why and how questions. Use when students memorise without understanding or need deeper processing of content.

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Expert blueprint for romance games and dating sims (Tokimeki Memorial, Monster Prom, Persona social links) focusing on affection systems, multi-stat relationships, dated events, and route branching. Use when building relationship-centric games, social simulations, or otome games. Keywords romance, dating sim, affection system, relationship stats, date events, character routes, love interest.

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Query a user's TrailSnap photo library through scoped MCP tools to find photos, reconstruct memories, build person timelines, create evidence-backed narratives, or propose an album plan for explicit in-app approval. Use for the user's own TrailSnap library; never claim a proposal was executed or changed the library.

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Hierarchical memory organization for multi-session context retention. Wings (projects) > Rooms (domains) > Drawers (decisions). Semantic search across all memories with zero cloud dependency.

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Open the Cross-Code Organizer (CCO) dashboard — view and manage all memories, skills, MCP servers, hooks, and configs across scopes

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。