'persistent' 검색 결과

Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.

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• Add conversation history to a chat system
• Remember user preferences across sessions
• Reuse patterns from successful interactions

Start AI Engineering from Scratch with a brief interview and placement quiz. Create a persistent LEARNING.md study plan recording your goals, starting phase, pace, and learning path for later sessions with the learn skill.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a study plan when starting the curriculum
• Choosing a starting phase based on current knowledge
• Recording learning goals, available time, and a build goal

Automate browser tasks by exploring Chrome pages like folders. Agents can list page elements, read and search text, click controls, and fill forms through commands, with structured output and a persistent connection mode.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explore page structure and read content
• Find text and click matching page elements
• Enter text into forms

Organize repeated experiments to improve models, prompts, or agent systems against measurable goals. The skill records hypotheses and lessons in a persistent tree, isolates trials, and compares promising candidates using evaluation data kept separate from development.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare repeated experiments to improve model training
• Improve an agent system's measured success rate
• Test prompt changes against a fixed evaluator

Build persistent agents on Azure AI Foundry with the Microsoft Agent Framework Python SDK. Connect functions, search, and MCP tools, preserve conversation context, stream responses, and return data in a defined structure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an agent that preserves conversation context
• Connecting Python functions and hosted search tools
• Displaying responses as they arrive

Store project knowledge and design decisions in persistent memory that AI agents can search and read. New findings sync automatically to Markdown documentation, while usage statistics help you inspect the memory bank.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Save architecture decisions for later tasks
• Find previously recorded implementation patterns
• Keep agent memory synced with project documentation

Provides persistent project memory for AI agents through a local MCP server, a tool interface for agents. Stores and searches architecture decisions and implementation patterns, syncs with project documentation, and includes a dashboard for viewing memory usage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Save architecture decisions for later reference
• Search previously recorded implementation patterns
• Retrieve design notes by key

Keep a short, persistent work journal so an agent can resume across sessions. Record completed work, current status, next steps, and unsuccessful attempts, then read the latest entry when restarting or summarize the work when closing it.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording progress at the end of a session
• Resuming work from the latest journal entry
• Saving key decisions and unsuccessful attempts

Coordinate multiple agents in tmux, a terminal session manager, using shared tasks and persistent messages. Assign work, monitor progress and blockers, verify worker results, and shut down the team after tasks and state updates are complete.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Divide a task among multiple agents
• Track progress through a shared task list
• Coordinate workers with persistent messages

Guides setup and operation of Lambda Labs GPU instances for machine learning training and inference. Covers SSH access, persistent storage, API automation, and multi-GPU or multi-node training, including how to retain checkpoints and terminate idle instances.

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• Setting up a GPU instance for LLM fine-tuning
• Keeping datasets and checkpoints between sessions
• Automating instance launch and termination through the API

Gives AI agents persistent project memory for architecture, implementation patterns, and decisions. Supports saving, searching, and retrieving knowledge, automatically syncing it with project documentation, and viewing memory usage through a dashboard.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Save architecture decisions for later reference
• Search saved implementation patterns by type or tag
• Retrieve a specific design record by key

Run cognee in Docker, starting with a prebuilt image for local document ingestion and questions. It also covers Docker Compose profiles for adding the UI, MCP server, Postgres, and Neo4j, plus persistent data storage.

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• Try cognee locally using a prebuilt Docker image
• Test document ingestion and questions through the API
• Start the UI, MCP server, and databases with Docker Compose

Add persistent memory to NanoClaw agents using the default Claude provider. With mnemon installed in the container, agents retrieve relevant context before responding and save new insights after each turn, retaining memory across container restarts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recalling earlier conversations in a new session
• Keeping agent memory across container restarts
• Checking memory hooks and storage settings

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Index local files or folders in context-mode’s persistent search database, so later searches can retrieve relevant passages without rereading entire files. Useful for repeatedly consulting project documents while keeping raw file contents out of the conversation.

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• Preparing project documents for repeated searches
• Finding relevant passages in local files
• Indexing a repository with selected folders excluded

Build a persistent connection that receives Zoom events, such as meeting starts and participant arrivals. This reference covers choosing WebSockets, a connection kept open for events, plus authentication, connection maintenance, and reconnection.

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• Receive meeting start and end events
• Track participant arrivals and departures
• Receive events without a public endpoint

Debug local web and Electron apps through a persistent Playwright session in js_repl, an interactive JavaScript environment. Reuse browser connections while checking user interactions, visual states, screen sizes, and screenshots after changes.

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• Rechecking an interface after code changes
• Testing buttons and complete user flows
• Inspecting layouts at desktop and mobile widths

Track work across sessions with persistent issues, dependencies, and progress notes. Use the bd command-line tool to find unblocked work, claim issues, record completion, and recover task context after conversation compaction.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Track feature work across multiple sessions
• Find work that has no unresolved blockers
• Recover task context after conversation compaction

Organize multi-step agent tasks with persistent Markdown files for plans, findings, and progress. Records decisions, errors, and test results, then reloads the selected task's planning files to resume work after an interruption.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning research that involves many tool calls
• Resuming work after an interruption
• Recording errors and test results

Audits Google Cloud Filestore instances for missing or outdated backups, overly broad file share access, and zone isolation requirements. Produces a health scorecard, prioritized findings, an instance inventory, and proposed backup remediation.

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• Finding file shares without recent backups
• Auditing NFS access ranges and root privileges
• Checking physical zone isolation and separation

Inspect Google Cloud Filestore usage and free space, then propose capacity increases or decreases based on configured thresholds and storage tier limits. Apply resizing after confirmation to address low space or reduce excess capacity.

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• Check free space across Filestore instances
• Increase capacity when storage is running low
• Reduce excess capacity on eligible storage tiers

Diagnose Google Cloud Filestore mount failures by checking client access rules, network firewalls, and audit logs. Distinguish permission errors from connection timeouts, then provide a structured report and remediation commands that require confirmation before execution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating a Filestore mount that hangs or fails
• Diagnosing access-denied errors from a GKE workload
• Checking client permissions and VPC firewall rules

Configure Backup for GKE to protect Kubernetes configurations and persistent data. Set backup schedules, retention periods, and customer-managed encryption keys, then prepare restore plans with explicit policies for handling existing resources.

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• Setting scheduled backups and retention periods
• Backing up persistent data and Secrets
• Encrypting backups with customer-managed keys

Build and operate websites, internal tools, APIs, and databases on Kortix Apps using its CLI or SDK. Covers deployment, sign-in, scaling, persistent storage that survives updates, snapshots, and restoring earlier application releases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Publish a static website or single-page app
• Build an internal CRM with team sign-in
• Create an API or database backend