Skill UI

Guide to configuring Proofpoint Targeted Attack Protection so suspicious attachments and URLs are detonated in isolated environments, policies are tuned, dashboards monitored, and SIEM workflows triggered for phishing defense.

Guides SOC teams through full phishing email investigations, from header and link analysis to attachment detonation, user impact mapping, and containment using tools such as Splunk, Microsoft Defender, URLScan, and sandboxes.

Analyzes malware to extract file hashes, C2 domains, IPs, URLs, registry hits and behavioral artifacts, blending static PE parsing, sandbox detonations, YARA-based IOC spotting, and STIX 2.1 formatting for CTI sharing during assessments or incident response.

Orchestrates idea discovery, implementation, scaling experiments, and autonomous review loops into a single pipeline that builds and iterates toward submission-ready research.

Turns messy workflows, docs, transcripts, or existing scripts into production-ready cross-platform agent skills by surfacing hidden requirements, generating specs, and validating exports for Claude Code, Copilot CLI, Cursor, and similar runtimes.

Register ICANN domains via a REST API with crypto payments so AI agents can autonomously check availability, pay in USDC/USDT/ETH/BTC, and retrieve DNS management credentials.

AegisOps-AI integrates Gemini 3 Flash to autonomously review Linux kernel patches, Terraform plans, and Kubernetes manifests, enforcing security, cost, and compliance guardrails inside the SDLC pipeline.

Adds x402 payment execution stack to AI agents, giving per-task/session budgets, allowed recipients, rate controls, and non-custodial wallets via MCP tools so agents can autonomously pay for APIs, services, or other agents with no custodial risk.

Trace every user-facing button or touchpoint to ensure the final UI state matches its label by mapping state stores, analyzing handler call sequences, and spotting conflicts like silent undos or race conditions after refactors.

Trackio is a robust library designed for comprehensive monitoring and management of Machine Learning experiments. It allows users to log key training metrics, set structured alerts (e.g., loss divergence, NaN gradients), and visualize results over time. The system supports integration via Python API for logging/alerting, and a powerful CLI for programmatic metric retrieval and dashboard synchronization to Hugging Face Spaces. Ideal for autonomous AI agent workflows and detailed research tracking.

Safety Guard prevents destructive operations on production systems or during autonomous agent runs by intercepting risky commands, restricting write access to specified directories, and combining both protections when needed.

Transforms Claude Code into a continuous autonomous agent with persistent memory, scheduled operations, remote dispatch, computer automation, and task queuing for repeatable workflows and monitoring.