645 skills · sorted by relevance
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.