'local memory' 검색 결과

Run language models locally with llama.cpp on CPUs, Apple Silicon, or AMD and Intel GPUs. The skill guides model setup, memory-saving GGUF format choices, interactive chat, and serving models through an OpenAI-compatible API.

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• Running a language model on a CPU-only computer
• Setting up local chat on an Apple Silicon Mac
• Choosing model quantization to fit available memory

Install or remove memU long-term memory for the agent in use. The skill selects the host-specific guide, configures scheduled memory recording and retrieval before answers, and verifies the setup with local or cloud storage.

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• Adding long-term memory to an AI agent
• Setting up memory stored on the current device
• Connecting an agent to MemU Cloud

Swift의 actor로 공유 데이터 접근을 관리하고 메모리 캐시와 JSON 파일 저장을 결합해 동시 접근 시 데이터 경쟁을 피하는 저장 계층을 설계합니다.

• iOS와 macOS 앱에 데이터를 저장합니다
• 오프라인 중심 앱의 저장 계층을 설계합니다
• 잠금 기반 동기화 처리를 대체합니다

Generate RuVLLM settings for local language model inference and create MicroLoRA or SONA instances. Adjust their small internal weights using quality scores as feedback, with instances held in server memory for subsequent adaptation calls.

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• Configure local language model inference
• Prepare inference tools for an air-gapped environment
• Adjust MicroLoRA using quality feedback

Build systems that use Pinecone to find documents for AI answers or retain agent memories across sessions. Guides index setup, data loading, meaning-based and keyword search, memory separation, and end-to-end checks.

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• Build an AI assistant that answers from documents
• Keep agent facts and decisions across sessions
• Combine meaning-based search with exact terminology

Search Screenpipe recordings, screen text, audio transcripts, and synced-device history through its local API. Use the evidence to review activity, find meeting details, and inspect saved workflows, including recorded steps that can inform automation.

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• Review activity from a specific day or time range
• Find recorded meeting remarks and screen text
• Search work history on another synced device

Use past local Codex sessions to propose updates to a selected skill and project memory file. Recurring tasks are replayed, changes are checked on separate validation tasks, and proposals are staged for review before adoption.

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• Turn recurring Codex requests into skill updates
• Consolidate feedback into skills and project memory
• Compare validation scores before applying changes

Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface: 6 tools with --full (adds check_action); 13 tools with AR_EXTRAS=1 --full. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.

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Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface (18 tools) available with --full flag. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.

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Use when a local agent needs to submit a concise public work-memory report for its own completed local task through `cg report work-memory`.

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Entity and relation tracking skill using MCP memory server patterns and local knowledge graph storage. Build persistent knowledge graphs of entities, relationships, and observations across agent sessions. Covers the MCP memory server (@modelcontextprotocol/server-memory), local JSON-based graphs, and entity-relation querying patterns for long-running agents.

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Search cloud memory backup when local results are insufficient. Use when memory_search returns few/no results for a topic you know was discussed before. Cloud preserves everything - even memories that were deleted or compacted locally.

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Model Context Protocol interface to the memB persistent agent memory layer. Use when an agent needs to add, search, list or delete persistent memories locally and offline. Exposes add_memory, search_memory, list_memories and delete_memory.

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Runs image, video and audio generation on a local ComfyUI server and guards the GPU before long renders. Trigger to generate an image, video, audio or music locally; to run, test or check a ComfyUI workflow before a long or queued render; when a video render crashed the machine, ran out of memory or hung in VAE decode; to render while LM Studio holds the GPU; to run a pixelsmith brief locally; or say comfyrunner (audit, check, test, run, status). Refuses an untiled video decode and scores resolution times frames. Briefs and judging art are pixelsmith's; sound briefs soundsmith's; freeing LM Studio lmstudiorunner's; schedules agentwright's.

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원본 자료를 바탕으로 OpenClaw 기억 위키를 유지합니다. 사람이 쓴 메모를 보호하면서 기존 페이지를 갱신하고 중복, 정보 모순, 출처 누락을 확인합니다.

• 원본 자료로 위키를 갱신할 때
• 축적한 지식을 확인하고 인용할 때
• 갱신 후 모순과 출처 누락을 점검할 때

Generate complete songs locally from lyrics and tags describing mood, instruments, or genre. The skill guides setup, input formatting, memory-saving settings, and MP3 output using the open-source HeartMuLa music models.

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• Turn original lyrics into a complete song
• Try different moods and instruments with tags
• Generate music locally or offline

Manage stored agent memories for backups, project transfers, and team knowledge sharing. Work with RVF, a portable memory format, to export and import records, upgrade formats, inspect storage statistics, and transfer patterns between local projects.

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• Back up stored agent memories
• Transfer memories and patterns between projects
• Share stored knowledge with a team

Inspect and transform local DICOM medical imaging files, including metadata, image frames, and compression formats. Generate technical inventories and preview images, check installed codecs, and review pseudonymized derivatives without treating those checks as proof of de-identification.

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• Create a technical inventory of local DICOM files
• Check compression support and frame memory needs
• Export a frame as a non-diagnostic preview

Store preferences, task lessons, and procedure outcomes in Hindsight, then retrieve relevant context before starting new work. It records detailed observations and can synthesize past experience into guidance for approaching the next task.

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• Remembering preferences and project conventions
• Recording bug fixes and workarounds
• Keeping successful and failed procedure outcomes

Search past conversations to recover decisions, discussions, and context across sessions. Find related ideas by meaning, match exact terms, or locate code identifiers, with options to search only user or assistant messages through local AI Maestro tools.

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• Recalling decisions before resuming work
• Finding discussions with different wording
• Looking up conversations about a function or class

Connect a container agent to local Ollama models through MCP, a protocol for agent tools. The agent can list models and generate responses, with optional tools to download, delete, inspect, and check loaded models.

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• Delegate response generation to a local model
• Check which Ollama models are installed
• Download or delete models through the agent

Find previously indexed project files, documentation, and session memory in context-mode's persistent full-text search database. Search related questions together and narrow results to a named project or source without rereading raw material into the conversation.

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• Find information in previously indexed project files
• Look up saved documentation or session memory
• Search several related questions together

Guides migration of existing codebases to Rust without unsafe code, covering architecture, implementation, testing, and output comparisons. It measures performance before and after optimization and produces an interactive HTML report documenting results and unresolved differences.

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• Migrating a GitHub or local codebase to Rust
• Comparing original and migrated program outputs
• Measuring and optimizing a Rust port

Launch machine learning training jobs on local or remote GPUs. The skill checks available GPU memory, synchronizes code, saves logs, and verifies startup, with optional experiment tracking and support for Vast.ai and Modal.

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• Launch training on an available remote GPU
• Run experiments locally with CUDA or MPS
• Record training metrics with W&B