Skill UI

Trackio is an essential library for logging, visualizing, and analyzing machine learning training experiments. It provides multi-interface support: a Python API for real-time metric logging and alert firing during training; a Command Line Interface (CLI) for retrieving historical metrics and alerts; and synchronization capabilities to Hugging Face Spaces for persistent dashboards. This tool is crucial for debugging, diagnosing training issues (like loss spikes), and automating ML workflow iteration.

This skill details the deployment of various canary tokens (DNS, HTTP, AWS API Keys) across enterprise network infrastructure. It establishes digital tripwires that detect behavioral anomalies, such as unauthorized access, credential leakage, or lateral movement. Tokens are configured with webhook alerts (Slack, SIEM) for real-time, low-false-positive notification, significantly enhancing traditional IDS/IPS capabilities.

Provides methodology and scripts for proactive threat hunting, IOC sweeps, and statistical anomaly detection that surface attacker activity before automated alerts fire.

Send plain-text messages to a Google Chat space directly from the gws CLI using `gws chat +send` for quick notifications or alerts when you want to automate or script communication.

A comprehensive guide detailing how to configure and manage GitHub's secret scanning feature. This skill covers enabling Secret Protection, setting up push protection to block leaked credentials before they reach the repository, defining custom regex patterns, and providing workflows for triaging alerts and resolving blocked pushes (including using git rebase or bypassing protections). Essential for maintaining secure code repositories and complying with security standards.

Surfaces Monte Carlo data observability insights—health, lineage, alerts, and monitors-as-code—before developers edit dbt models or SQL pipelines, ensuring risk-aware code changes.

This skill enables the proactive, one-way sending of messages to specific users, sessions, or channels (e.g., Feishu, Discord). It is ideal for automated notifications, task completion alerts, or status updates when a reply is not expected. Before sending, the workflow requires querying available sessions to obtain necessary target IDs.

Medchem is a powerful Python library designed for molecular filtering and prioritization in drug discovery. It allows users to apply rigorous, literature-derived drug-likeness rules (e.g., Lipinski, Veber, CNS), detect structural alerts (PAINS, NIBR), calculate complexity metrics, and combine multiple criteria using a custom query language. It is essential for compound library triage and hit-to-lead optimization.

A comprehensive guide and toolkit for optimizing and monitoring consumption-based costs across the Adobe Creative Cloud ecosystem. It provides strategies and code snippets for tracking usage (credits, transactions) for Firefly, PDF Services, Photoshop, and Lightroom APIs. Use this when implementing usage monitoring, managing billing alerts, or reducing operational expenses related to Adobe API calls.

This guide provides comprehensive strategies to significantly reduce spending on the Anthropic Claude API. Key techniques include model routing (selecting the most cost-effective model for the task), prompt caching for repeated prompts, using Message Batches for asynchronous tasks, and implementing real-time spend tracking and budget alerts. Essential for managing large-scale, high-cost AI deployments and optimizing cloud LLM expenditures.

This skill integrates the agent with the Matrix/Element messaging platform. It enables full conversational capabilities, allowing users to receive real-time alerts, search content, and control connected systems (such as cameras) directly from a designated Matrix room. It supports multi-turn interactions, including sending files, reacting, editing, and managing threads.

This skill provides a robust solution for monitoring critical forecast changes within Clari. Since Clari lacks native real-time webhooks for all data, this system implements change detection by polling periodic data exports and comparing current snapshots against historical records. It calculates the percentage change and can trigger immediate alerts via platforms like Slack, ensuring users are always aware of significant shifts in revenue forecasts or sales pipelines.