Skill UI

This skill optimizes multi-agent systems through coordinated profiling, workload distribution, and cost-aware orchestration. It helps improve agent performance, throughput, and reliability by identifying bottlenecks, managing context windows, and reducing latency. Suitable for complex workflows requiring intelligent coordination and efficiency tracking across multiple AI agents.

This skill enables debugging and patching failed Agent QA runs using MCP evidence, artifacts, logs, and local code. It ensures defects are not hidden by rewriting tests. The process involves collecting evidence, classifying failures, inspecting local files, applying minimal changes, and verifying fixes through reruns. It supports self-healing of stale definitions while maintaining safety boundaries.

This skill focuses on designing tools for AI agents to interact with the world effectively. It covers schema design, JSON Schema best practices, error handling, and description writing to optimize LLM performance. Key aspects include validation, the emerging MCP standard, and understanding that tool descriptions are crucial for agent functionality.

AgentFlow transforms your existing Kanban board (Asana, GitHub, Linear) into a self-managing, autonomous AI development pipeline. It orchestrates multiple AI agents through defined stages (Backlog to Done), enforcing deterministic quality gates, performing adversarial reviews, and tracking costs—all visible within your existing project management UI. It provides crash-proof execution and full observability for complex software development lifecycles.

A comprehensive design specification detailing 98 rules for building Command Line Interface (CLI) tools that are safe, reliable, and easily consumable by AI agents. It covers structured JSON output formats, standardized error handling (to stderr), robust input contracts, safety guardrails (e.g., preventing path traversal), and tiered compliance levels (Agent-Friendly, Agent-Ready, Agent-Native) for seamless integration into AI workflows.

This skill guides the development of AI-powered products, focusing on LLM integration, RAG architecture, scalable prompt engineering, trustworthy AI UX, and cost optimization. It ensures products are production-ready rather than failing demos.

Converts requirements into precise, dependency-aware implementation plans. Creates detailed checklists, defines milestones, and maps critical paths for development teams. Ensures tasks are atomic and verifiable before coding begins, bridging the gap between requirements and execution.

This skill provides authorized security techniques for analyzing protected binaries and malware. It is designed for educational purposes, penetration testing, and CTF competitions. Users must confirm written authorization before bypassing protections or extracting data. It focuses on understanding anti-debugging and obfuscation for defensive security research.

Autonomous Principal Design Technologist. Transforms generic, AI-generated UI into bespoke, human-grade, production-ready software. Cures vibe-coded frontend slop using curated token archetypes and 7-axis quality gating.

A comprehensive guide and skill for building robust, production-ready REST API endpoints. This skill covers full lifecycle development, including input validation, centralized error handling, authentication/authorization checks, CRUD operations, pagination, and generating proper API documentation. It ensures that back-end services are secure, scalable, and adhere to industry best practices.

Implement bounded, idempotency-aware API throttling, backoff, and retry handling for 429 and transient 5xx responses. Includes token bucket rate limiting, Retry-After parsing, and terminal error classification.

Convert existing software into reusable serverless applications for the Apify platform, using the Apify SDK to wrap code as Actors that accept JSON input and produce structured output.