Skill UI

This framework enforces high output quality, intellectual honesty, and continuous improvement across all AppSec agents. It mandates structured processes, including self-critique loops, anti-rationalization guards, and self-reflection quality gates (≥8 scoring). Applicable during code review, SAST, SCA, and threat modeling, it ensures rigorous and verifiable security analysis.

This skill automates the migration of existing frontend code (such as those built with React, Vite, or other frameworks) into the proprietary Stitch Design format. It performs a multi-step workflow: first, extracting a self-contained static HTML file; second, analyzing source components and stylesheets to generate a comprehensive design system documentation (DESIGN.md); and finally, uploading both artifacts to the specified Stitch project for continuous iteration and improvement.

This powerful skill leverages learned patterns and the current operational state to predict the most efficient next action. It guides complex workflows by offering immediate, high-confidence predictions. If confidence is low, it intelligently falls back to task priority lists. After every completed task, it utilizes a robust learning loop to update success patterns, ensuring continuous process improvement and cross-session memory recall.

This tool enables the creation and management of Dynamic Agentic Architecture (DAA) agents. Unlike static agents, DAA agents possess the ability to learn, adapt their internal behavior based on real-time performance metrics, and continuously evolve through interactions. It facilitates knowledge sharing among multiple agents, ensuring systemic, continuous improvement in complex AI systems. Ideal for scenarios requiring high adaptability and evolving intelligence.

SkillOpt-Sleep enables an AI agent to undergo a simulated "sleep cycle" for continuous, measurable self-improvement. It harvests past sessions, mines recurring tasks, and re-runs them offline. It then consolidates findings into the agent's memory and skills, ensuring only validated improvements are adopted. This process maximizes long-term competence from short-term interactions.

This skill implements a continuous improvement feedback loop (Part 12). It aggregates signals from market shifts, customer feedback, competitive intelligence, and internal operations. The output provides structured recommendations to refine the brand's product offering, business strategy, and channel mix, ensuring marketing efforts remain relevant and adaptive. Used continuously, especially during Quarterly Business Reviews (QBRs).

Design a comprehensive and systematic framework for optimizing ad creative performance. This tool guides users through defining testable variables (headlines, CTAs, imagery), designing A/B and multivariate tests, calculating required sample sizes, and establishing optimal iteration cadences across major advertising platforms (Meta, Google, etc.) for continuous marketing improvement.

A comprehensive guide for project-managing the demanding lifecycle of submitting a top-tier systems conference paper (e.g., OSDI). Covers critical deadlines, from initial title registration and full submission to artifact preparation and final paper revisions. It provides strategies for navigating review outcomes, retargeting to other venues (SOSP, NSDI), and utilizing the silent window for continuous improvement.

An autonomous system designed to continuously improve code quality by scanning codebase metrics. It functions as an agent, proposing mechanical code changes, executing them, and committing only if defined metrics improve. It supports multiple modes (Scan, Scaffold, Run) for systematic, data-driven codebase refactoring.