'setup memory' 검색 결과

Guides fuzz testing for Ruby libraries and gems with Ruzzy, using varied inputs to find failures. Covers pure Ruby and C extensions, including test harnesses, memory error detection, environment setup, and crash reproduction.

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• Adding fuzz tests to a Ruby library
• Testing memory safety in a gem's C extension
• Preparing tracer and harness scripts for pure Ruby

Set up Atheris to test Python code and C extensions with automatically varied inputs. It guides test harness creation, coverage tracking to explore more code, and AddressSanitizer setup for finding memory errors.

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• Fuzzing a Python package or parser
• Investigating memory corruption in Python C extensions
• Turning raw bytes into typed test arguments

Diagnose slow loading, high memory use, and low frame rates in USD 3D scenes. Coordinate scene structure checks, validation, optimization, and before-and-after performance comparisons, then produce a report documenting findings and changes.

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• Investigating slow USD scene loading
• Diagnosing low frame rates in CAD scenes
• Investigating high GPU or system memory use

Audit and improve an entire agentic-development setup, including skills, context, memory, tools, models, permissions, hooks, workflow habits, and secrets hygiene. Invoke for setup reviews, workflow grading, Goblin Mode, or questions about what to improve. Supports shallow, deep, ultra, and yolo modes. Use domain-specific skills for a single model, report, or tenant.

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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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Add persistent memory to Claude Code that survives across sessions. Use when: maintaining continuity across Claude Code sessions, building agents with persistent project memory, avoiding repeated context setup. Covers claude-mem (AI-compressed session logs) and Claude Subconscious (Letta-based background agent).

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Set up Claude Tag for oncall for a team, from that team's standing (public) oncall / monitoring channel. Two things: it sets the channel up for monitoring, so alerts and incident posts here (from a person or another Slack bot) get triaged and acted on automatically, and it finds and sets up the connectors, plugins and skills Claude uses for oncall. A workspace can have several such channels; run once in each, by anyone, and every run adds or updates that team's section in the one oncall memory in shared workspace memory. Use on "set up oncall", "init oncall", "configure Claude for on-call". Surveys the agent connectors an admin configured for Claude, proves one with a read-only pull so access is verified, explores tools, Slack, repos and docs to learn how oncall works here, then recommends what's missing and walks the team through setup. The result goes into the indexed oncall memory every channel reuses; the other skills (incident init, investigation, handoff) pick it up automatically.

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Deliberate system-improvement loop — audit your own Claude Code setup (rules / memory / skills / MCP / agents / registry), evaluate incoming tools via /vet, adopt the worthwhile ones with discipline (backup → verify), and record every decision. Use when bringing in a tool/idea/collection to improve the setup, or when doing a tooling-improvement pass. Triggers — "upgrade the workflow", "audit our setup", "let's improve the tooling", "evaluate this for us", when handed a post/repo/reel with a tool.

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ECC의 현재 파일을 확인해 작업에 맞는 스킬과 명령을 찾습니다. 설치와 초기화, 프로젝트 설정을 안내하고 중복 설치와 설치 위치를 조사하도록 돕습니다.

• 작업에 맞는 스킬과 명령 찾기
• 설치 및 초기화 절차 확인
• 기존 프로젝트의 설치 계획 준비

Train language models on pairs of preferred and rejected answers without a separate reference model. The skill guides training setup, model-specific configurations, parameter tuning, and troubleshooting for unstable training or memory limits.

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• Train a base model using preferred and rejected answer pairs
• Further tune an instruction-following model
• Adjust training settings for math and coding tasks

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

Customize what claude-mem remembers by defining note categories and reusable tags through a guided conversation. Install and activate the configuration, verify it appears in startup context, and optionally set up Telegram alerts for selected notes.

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• Tailor memory categories to work or study
• Define what to record and what to skip
• Receive Telegram alerts for selected notes

Get started with omo through a guided feature tour and workflow introduction. Optional setup migration and past-session analysis connect its features to existing work habits, while memory tools record useful preferences for future sessions.

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• Learning omo's features and basic workflow
• Migrating settings from another coding agent
• Reviewing past agent sessions for work patterns

Explain tips shown in senpi or omo by checking the installed tip list, user settings, and implementation code. Learn what a feature actually does, why a tip appears, and which tips your setup can show.

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• Understand a Tip: line shown during a session
• Check which tips your settings allow
• Explore how model fallback guidance works

Generate images, video, and music through ComfyUI in OpenMontage. Helps adapt community workflows, select the final output, identify missing models, and choose workflows suited to available GPU memory while recording settings for reproducibility.

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• Generating images, video, or music in OpenMontage
• Importing a community ComfyUI workflow
• Choosing workflows for limited GPU memory

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.

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

Consult bundled technical documentation to understand and integrate Hindsight, a memory system for AI agents. Find guidance on storing and retrieving memories, API calls, configuration, deployment, SDKs, troubleshooting, and performance.

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• Learning how memory storage and retrieval work
• Checking API endpoints and request parameters
• Planning server setup and SDK integration

Plan and configure large language model training with OpenRLHF to better match human preferences. Covers training method selection, distributed GPU setup, inference acceleration with vLLM, and troubleshooting memory or training stability issues.

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• Choosing a method for training models on human preferences
• Setting up PPO training with a reward model
• Configuring GRPO training without a critic model

Guide large language model training with OpenRLHF using human preference feedback. Covers training method selection, distributed GPU setup, reward model training, and memory tuning, with examples for PPO, GRPO, and DPO.

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• Choosing a training method for human preference alignment
• Setting up distributed training across multiple GPUs
• Training a reward model before PPO

Adapt existing PyTorch training scripts to run across multiple GPUs or machines with Accelerate. This skill covers device setup, distributed training configuration, and mixed precision, which uses lower-precision numbers to reduce memory use.

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• Adapt a single-GPU training script for multiple GPUs
• Configure mixed precision to reduce training memory use
• Set up training with DeepSpeed or FSDP

Use when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame encode/decode smoke test with official samples, and when interpreting what those readiness results, including CPU-buffer and device-memory sample modes, do and do not establish.

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Use when a Unity 6.3 game is slow or stutters: low fps, frame spikes, hitches when entering areas or on first effects, GC spikes, too many draw calls or SetPass calls, CPU vs GPU bound questions, Profiler, Profile Analyzer, Memory Profiler, Frame Debugger or Project Auditor work, GPU Resident Drawer or SRP Batcher setup, LOD and Mesh LOD, shader warm-up and variants, memory or VRAM budgets, IL2CPP and build size, or proving a fix with numbers from a player.

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First-time onboarding for new Agentara users. Use when user says "bootstrap", "/bootstrap", "get started", "first time setup", or when memory/USER.md and memory/SOUL.md are empty/missing. Inspired by the movie Her — warm, curious, subtly brilliant. The goal is to make the user feel understood within minutes and want to keep going.

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