'local memory' 검색 결과

Guides you through running large language models on CPUs, Apple Silicon, and AMD or Intel GPUs. Covers choosing GGUF model files, reducing memory through quantization, and starting an OpenAI-compatible chat server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running a local LLM on an Apple Silicon Mac
• Trying LLM inference on a CPU-only machine
• Choosing a model format that fits available memory

Set up and use ReMe as a file-native long-term memory system through the reme CLI. Use when an Agent needs to detect whether ReMe is installed or running, install and configure ReMe, start or verify its local service, retrieve prior context, or write and consolidate durable memory.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Train strict project ownership from repo-local docs/ai memory and central LLM Wiki project entities. Use when Codex or Claude Code should initialize a learning question bank, quiz or grade the user, run code tracing or failure drills, record misconceptions, schedule spaced repetition, or generate Markdown/JSON mastery dashboards without replacing llm-wiki or ai-project-memory.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Query the user's local and synced-device data via the screenpipe REST API at localhost:3030 — recordings, audio, UI, meetings, connected services, and memory. Use for screen activity, other-device or cross-device history, productivity, media export, connections, durable memory, or discovering and automating saved workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Hive CE (Community Edition v2.19.x) NoSQL object database for Flutter providing blazing-fast key-value and object storage with TypeAdapters. Use this skill when implementing offline-first architecture, high-performance local data caching, NoSQL document-style object stores, custom TypeAdapter serialization for complex objects, lazy loading boxes for memory efficiency, encrypted boxes (HiveAES encryption), database compaction for size optimization, storing large datasets without SQL schema overhead, implementing local-first sync patterns, or migrating from SQLite to NoSQL. Supports primitive types, custom objects, lists, and maps. Ideal for apps requiring ultra-fast read/write operations (microsecond latency), object persistence without ORM complexity, or local-first data architecture.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Understand how claude-mem turns Claude's actions into compact notes and adds relevant memories to later prompts. Learn when this starts, where local data is stored, and which external services receive information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understand how claude-mem records actions
• Check when memories start appearing in prompts
• Find where memory data and settings are stored

Recover context from local coding-agent transcripts and produce a checkpoint covering the objective, completed work, blockers, and next step. Explicit import requests support read-only recovery, with continuation governed by the current workspace rules and the user's request.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recover context using a session ID
• Summarize unfinished work from local logs
• Review JSONL exports or Markdown handoffs

Set up and maintain MemPalace as a searchable store of project and conversation memories. Configure a private installation, a shared hub, or a hub client, connect agent tools, and audit how stored information is organized.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a private memory store
• Sharing a memory hub between AI agents
• Connecting Claude or Codex through MCP

Store conversation history and project material locally, then search by meaning and retrieve original text. Organize memories by person or project, track dated relationships and facts, and keep diary entries that connect work across sessions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Import conversation history or project files
• Search past discussions by meaning
• Organize records by person, project, and topic

Develop exploit code from a known memory corruption flaw, then investigate differences between local and remote execution. Covers stack, heap, and Linux kernel cases, with attention to library versions, debugging, and repeatable results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Developing exploit code for a known vulnerability
• Investigating why a CTF exploit fails remotely
• Checking library versions and memory protections

Develop and debug CopilotKit Inspector interfaces in a dedicated local workbench. It guides baseline screenshots, visual checks after each change, and additional narrow-screen or theme checks when layout or contrast is involved.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fixing Inspector panes and overlay controls
• Checking narrow-screen layouts and the collapsed rail
• Comparing light and dark theme appearance

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Turn the latest Granola call into action items with owners, teams, due dates, and matching existing Linear issues. It uses cognee memory of calls, issues, and Gmail, then prints the results or posts them to configured Slack.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extract action items from the latest Granola call
• Match follow-up tasks to existing Linear issues
• Clarify owners, teams, and due dates using email context

Connect a company SQL database, support ticket exports, and documents in cognee, linking shared people, teams, and projects so questions about ownership, decisions, and ongoing work can draw on several sources together.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding owners across company databases, tickets, and documents
• Tracing decisions across multiple company sources
• Checking who is handling a support issue

Summarize WeChat group conversations into organized digests with discussion highlights, verified speaker attribution, and message statistics. Keep group history, participant profiles, and factual notes across runs, or summarize only messages received since the previous digest.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Catching up on a week's WeChat group discussions
• Summarizing messages since the previous digest
• Reviewing message counts and active participants

Maintain Verdaccio’s bundled plugins and investigate loading failures using checks that identify resolution, export, construction, or interface problems. Review shared plugin interfaces for compatibility, update affected plugins, and validate fixes with plugin and integration tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Diagnosing why a plugin fails to load
• Fixing bundled authentication or storage plugins
• Checking compatibility when changing plugin interfaces

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Find AI models for a specific task using official Hugging Face benchmark leaderboards. Compare scores, model sizes, and licenses, with memory-based estimates of device suitability and flags for models requiring quantization or API access.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare coding or reasoning models by benchmark scores
• Find model candidates for a laptop's available memory
• Choose models for OCR or speech recognition

Run Apache Tika as a Docker container when you need guaranteed OCR (scanned PDFs, images) or geospatial raster support with zero local install — `apache/tika:<version>-full` bundles Tesseract, GDAL, ImageMagick, and fonts. Also covers the minimal image, port/volume/memory conventions, the path-identity mount gotcha, and how to confirm OCR actually ran rather than silently returning no text. Powered by Apache Tika. Use when a local `tika-app`/`tika-server` doesn't have Tesseract installed, or you want a disposable, self-contained parsing environment. Companion to the `file-to-markdown` skill, which covers the parsing calls themselves once a server is up.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze a project's past Codex sessions, memory files, and existing local skills to recommend the highest-value skills to create or update. Use when a user asks what skills a project needs, wants skill ideas grounded in real project history, wants an audit of current project-local skills, or wants recommendations for updating stale or incomplete skills instead of creating duplicates.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when the user is doing hands-on DOCA DMA programming — bringing up a doca_dma context, configuring the single doca_dma_task_memcpy task type, sizing buffers via the doca_dma_cap_task_memcpy_* queries, setting LOCAL_READ_ONLY / LOCAL_READ_WRITE permissions on source / destination doca_mmap regions (plus doca_mmap_export_* for cross-peer copies), driving the progress engine, or debugging DOCA_ERROR_* returns. Trigger even when the user does not explicitly mention "DOCA DMA" or "doca_mmap" — typical implicit phrasings include "memcpy host buffer to BlueField without using the CPU", "offload a bulk copy to the DPU", "copy returns NOT_PERMITTED on first submit", "buffer too big for one DMA task", "task submitted but no completion", or "scatter-gather copy between two memory regions". Refuse and route elsewhere for cross-network copies (DOCA RDMA), producer/consumer messaging (DOCA Comch), DOCA Core / progress-engine internals, or DOCA install — those belong to other skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use Waggle automatically as portable, local project memory during meaningful coding work; retrieve prior decisions and constraints, then store only durable outcomes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when setting up project memory in a new folder or on the first brain:init in a project. Creates project_brain.db, registers the project, and bootstraps it from CLAUDE.local.md, memory files, docs, and emails; safe to re-run.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create or update a structured change log for a product or platform. Use when someone asks to "create a change log", "log this change", "what changed", "update the change log", "release notes", or "changelog". Reads project memory (CLAUDE.local.md Recent Changes) and meeting notes to build chronological change records. Supports init (backfill from memory), update (append new entries), and release (summarize for a version).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.