Skill UI

A concise collection of idiomatic Python best practices—from readability and explicit configuration to type hints, error handling, and context managers—aimed at building robust, maintainable applications.

Master advanced error capture and context enrichment using Sentry SDKs for both TypeScript and Python. This skill teaches how to reliably capture exceptions, track non-exception events (messages), and enrich reports with structured data like user context, tags, breadcrumbs, and custom fingerprints. It is essential for dramatically improving the ability to diagnose complex production issues quickly.

This skill provides a comprehensive framework for the continuous self-improvement of makepad-skills. It outlines automatic skill triggering via hooks, context-aware skill routing, and a structured process for capturing new patterns, error solutions, and best practices. It ensures the entire skill library remains robust and up-to-date throughout the development lifecycle.

This tool allows users to explicitly save critical, project-specific facts, conventions, or debugging insights to a dedicated auto-memory file. Use it when the knowledge is too important or specialized to rely on the AI's automatic context capture. It ensures persistent recall of architectural decisions, tool gotchas, or team preferences across multiple sessions and projects.

This agent ensures seamless continuity across multiple work sessions. It automatically captures, compresses, and stores critical project information—including summarized discussions, architectural decisions, pending tasks, modified files, and technical discoveries. When a new session starts, it loads a comprehensive briefing, allowing users to quickly resume work without losing track of the project's current state.

This utility converts a single, high-level objective into a comprehensive, step-by-step build plan suitable for complex, multi-agent, or multi-session engineering projects. It automatically handles dependency mapping, detects parallel steps, and structures each step with self-contained context briefs, ensuring any new agent can execute it without prior knowledge. Ideal for large-scale refactoring or feature development spanning multiple PRs.

Allows users to resume an interrupted or paused autoresearch experiment. It automatically loads the full context, including configuration, strategy files, complete results history, and recent code changes. The system then summarizes the current best metrics and patterns, prompting the user to continue with a single iteration, set up an automated loop, or manually review the results.

This skill guides the creation of automated workflows for enriching raw Indicators of Compromise (IOCs) with multi-source threat intelligence context. It is designed for integration into SOAR platforms (e.g., Cortex XSOAR, Splunk SOAR) or Python pipelines. By automatically querying multiple sources like VirusTotal, AbuseIPDB, and MISP, it significantly reduces analyst mean time to triage (MTTT) and standardizes the resulting threat context and confidence scoring.

This skill details the architecture and implementation of an automated Indicator of Compromise (IOC) enrichment pipeline using OpenCTI. It demonstrates how to connect OpenCTI to various external threat intelligence services (like VirusTotal, Shodan, AbuseIPDB) to automatically gather context, correlate indicators, and assign confidence scores. This is crucial for enhancing security monitoring and prioritizing threats for cyber analysts.

This guide outlines how to implement Google Cloud Identity-Aware Proxy (IAP) to enforce zero-trust, identity-based access control across various Google Cloud services (App Engine, Cloud Run, GKE, Compute Engine). It details configuring per-request authentication, context-aware policies (like device posture and location), and programmatic access management for highly secure internal applications.

This skill helps developers structure and record critical architectural decisions (ADRs) made during coding sessions. It automatically detects moments of decision, capturing the context, alternatives considered, and the rationale (trade-offs and consequences). This maintains a living log that explains 'why' the code base was built the way it is, preventing knowledge loss and improving project maintainability.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.