Skill UI

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

Provides a structured reference covering the 100 most critical web vulnerabilities, including definitions, root causes, impacts, and mitigation guidance aligned with OWASP categories to support assessments and remediation planning.

Provides a structured reference of the 100 most critical web application vulnerabilities organized by category, helping security teams identify attack vectors, assess impacts, and follow remediation guidance during audits or penetration tests.

Automates generating TorchServe deployment configurations with guided best practices for model serving, MLOps pipelines, inference, and production monitoring so ML deployment workflows stay consistent and reliable.

Automates the Tpscheck toolkit via Composio's Rube MCP by discovering tools, verifying connections, and running workflows while respecting dynamic schemas, session reuse, and memory requirements.

This skill provides comprehensive guidance for the entire lifecycle of Conductor tracks—the logical work units used to organize features, bugs, and refactors. It assists users in creating, reviewing, updating specifications (spec.md), developing plans (plan.md), and managing the status and metadata of tasks from initial conception through final completion, ensuring structured and traceable development processes.

Tracks CPU, memory, disk/network I/O, DB connections, and threads via the resource-usage-tracker plugin to reveal performance bottlenecks, recommend right-sizing, and guide cost-saving actions.

Defines and monitors SLAs, SLIs, and SLOs so teams can set performance targets, track metrics like error budgets and availability, and react before issues impact customers.

TransformerLens is the standard library for performing mechanistic interpretability research on large language models. It provides clean interfaces to inspect and manipulate transformer internals—such as attention patterns, residual streams, and MLP outputs—via activation caching and HookPoints. This tool is essential for reverse-engineering model algorithms, performing causal tracing, and studying the internal circuits responsible for model behavior.

Guide for orchestrating Triggercmd actions through Rube MCP and Composio, covering prerequisites, discovery steps, connection checks, execution calls, and best practices for schema compliance when automating flows.

Orchestrates TripAdvisor API tasks via Rube MCP, guiding tool discovery, connection checks, execution sequences, and memory-aware workflows so schema-driven automation stays consistent.

Guides Claude to automatically tune machine learning model hyperparameters via grid, random, or Bayesian search, generating runnable Python code, executing validations, and reporting best configurations plus performance metrics for accuracy or other goals.