A hardware-aware Codex/WorkBuddy skill that automates local MiniMax H3 video generation through ComfyUI, handling model selection, installation, and low-VRAM configuration.

EasyDB is a lightweight desktop app built with Tauri + Rust, powered by Apache DataFusion. Query local CSV, TSV, Text, NdJson, Excel, Parquet files and MySQL databases directly with SQL — no external database required. Handles datasets from hundreds of MB to several GB with ease. 让所有数据说同一种“语言”

A local-first visual prompt archive with installable agent skills for generating art direction and retrieving traceable prompt-image references.

test on Windows, enterprise CA, corporate certificate, GPO cert, TLS fetch failed, Windows sandbox, daytona windows, self-hosted cert. Use when validating iPolloWork Windows enterprise TLS/OS-trust fixes in a Daytona Windows sandbox.

🤖 Provide 70+ ready-to-use, platform-agnostic AI agent skills for improving tasks like code review, security, and data analysis.

Streamline FPGA development with 8 Vivado/Vitis skills for HLS, RTL, synthesis, constraints, timing, and debug workflows

Set up and run a public Portal relay on any Linux host with a public IP — Docker Compose deployment, embedded authoritative DNS with one-time NS delegation, optional TCP/UDP lease ports for game hosting, and registration in the public relay pool. Use when the user asks to run their own relay, contribute a relay to the Portal network, self-host a relay instead of using public ones, or open a relay with game-server support. Do not use for exposing a local service (portal-expose) or for accessing a CLI agent remotely.

A music player library for the PSG audio channels of the GB, GBC and GBA.

Provisions and manages Neo4j Aura instances via CLI (aura-cli v1.7+) or REST API.

Created a continuous, homogeneous, and structured 10 GB dataset from self obtained collections of unstructured intraday financial data. Generated features from indicators, statistics, and recent factors. Used multi-disciplined analysis to find feature importance. Attached labels of trends and stop/hold positions for machine learning. Used machine learning to significant features.

Investigate and reduce SigNoz telemetry ingestion cost and metric cardinality across metrics, logs, and traces. Find what drives SigNoz spend (via the Cost Meter), which metrics have runaway or unbounded label cardinality, and safe, dashboard-, alert-, and Infra-page-aware ways to cut volume. Make sure to use this skill whenever the user asks "why is my SigNoz bill so high", "what's driving my ingestion cost", "reduce telemetry volume", "which metrics cost the most", "cardinality health check", or "what can I safely drop" — or otherwise asks about telemetry spend, ingestion volume, or metric cardinality, even if they don't say "cost" or "optimize" explicitly.