Data analysis, machine learning, LLM apps, and prompt design (8.9K skills)
Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.
Store and retrieve knowledge across agent sessions with AgentDB. Organize memories into tiers, search learned patterns, combine related memories into context, and record causal links to support later work.
Plan and implement a unified AgentDB memory backend for agents. Migrate SQLite and Markdown data, add HNSW indexing for similarity search, and support shared memory and SONA learning patterns while preserving compatibility with existing systems.
Coordinate background tasks with specialized agents for research, optimization, audits, and more. Use execution history, quality scores, success rates, and latency to guide agent selection, track performance, and check benchmark compliance.
Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.
Delegate complex work through a tree of Claude Code sub-agents. Each level uses its own working context and returns concise summaries, helping the coordinating agent manage research, verification, and synthesis without retaining every intermediate detail.
Train, deploy, and manage neural networks with Flow Nexus in isolated E2B environments. Use custom architectures or ready-made templates, run predictions, monitor distributed training across multiple environments, and evaluate models before sharing them.
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.
Build agents that record task outcomes and use past experience to choose future approaches. Implement pattern matching, strategy comparisons, persistent learning records, and metrics for checking how recommendations and results change over time.
Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.
Set up a realtime evaluation folder with a suitable shared harness, the framework that runs evaluations. Prepare prompts, tool definitions, realistic starter data, and a README, then validate the setup with evaluation and test runs.
Creates self-contained HTML charts, tables, diagrams, and dashboards for display within a conversation. It keeps figures traceable to calculations and checks readability in light and dark themes, including narrow mobile layouts, before showing the page.
Analyze CSV, Parquet, and JSON files to answer questions through grouping, joins, cleaning, time series, and charts. Chooses DuckDB or Polars and a suitable processing location based on the task and available memory.
Build voice assistants and conversational characters with ElevenLabs. Configure voices, language models, and external tools, then connect conversations to a website widget or use Twilio integration for outbound phone calls.
Build custom interactive charts, network diagrams, and maps with d3.js. Guides data-driven drawing, tooltips, zooming, animation, responsive sizing, and accessibility, with examples that work in plain JavaScript or frameworks such as React, Vue, and Svelte.
Create and update dashboards with charts, metrics, and tables drawn from connected data sources or attached files. Link displayed values to their sources so readers can inspect queries, calculations, and freshness while reviewing business indicators or recurring reports.
Design multi-agent workflows for research, reviews, and migrations. Organize work across different evidence sources, add independent checks, and combine findings while preserving source references, unresolved questions, and limits on what was examined.
Provide Jarvis with a static catalog of system voices through the included voice_source.json file. This skill supplies reference data only; it does not provide a workflow for choosing voices, handling voice requests, or creating widgets.
Create charts and dashboards with a consistent approach to chart selection, color validation, labels, and interaction. Includes a palette checker and accessibility rules for readable visualizations in both light and dark modes.
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.
Initialize a new MemPalace for AI memory through a guided setup process. The skill retrieves setup instructions for a configuration that uses ChromaDB as its data storage backend and follows the returned steps.
Train and run Uni-Mol Tools models to predict molecular properties from CSV data. Organize experiments into projects, compare model performance, rank results, and manage storage through interactive commands or machine-readable JSON output.
Clean messy tabular data through OpenRefine using command-line operations. Import files, inspect rows, apply reusable JSON transformation histories, and export cleaned CSV or TSV data, with session support for undoing and redoing changes.
Send prompts to Novita AI models such as DeepSeek and GLM from the command line. Retrieve answers as readable text or structured JSON, stream responses as they arrive, and check available models and API connectivity.