Skill UI

This skill generates a single, full-bleed, looping motion composition using only inline CSS animations. It functions as a high-fidelity poster frame, specifically designed to be captured by keyframe exporters (like HyperFrames or Lottie) to create animated hero videos, title cards, or video posters for marketing campaigns. Ideal for briefs requiring sophisticated 'motion design' or 'animated hero' assets.

A skill designed to generate cinematic, short-form video clips (3-10 seconds) suitable for marketing reveals, product teasers, or ambient loops. It focuses on generating one cohesive shot per request, ensuring high quality and narrative focus for modern video models like Seedance, Kling, and Veo. Ideal for creating impactful, bite-sized visual content.

A systematic, multi-phase guide for tackling hard bugs and performance regressions. It mandates building a robust feedback loop (e.g., failing tests, harnesses), formulating falsifiable hypotheses, targeted instrumentation, and rigorous regression testing. This methodology ensures that debugging efforts are structured, efficient, and highly reproducible, making it ideal for complex software defects.

This CLI alias functions as a command for gracefully canceling an active PUA loop, mirroring the functionality of the Claude Code `/pua:cancel-loop` command. It cleans up loop state and worktree references, ensuring the cancellation event is recorded. It also safely manages configuration files, preserving unknown fields in `~/.pua/config.json`.

This CLI alias executes a comprehensive teardown protocol for PUA agents, designed for development environments. It safely releases all active agent and loop states, ensuring a clean system reset. It automatically manages configuration files like ~/.pua/config.json, preserving unknown fields while providing reliable, evidence-based cleanup.

A specialized skill for building and auditing Kubernetes Operators. It focuses on the core principle of the declarative reconcile loop, helping developers identify and fix common operational bugs such as status drift, missing finalizers, and incorrect RBAC. Includes tools to validate CRD specifications, lint controller code, and audit capability levels against industry best practices.

An evidence-led SEO workflow built around the Find → Leverage → Optimize → Win loop. Use it to surface stage-specific AI prompts for keyword research, backlink strategy, on-page improvements, and conversion-focused SEO analysis.

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

Provides expert guidance on building, optimizing, and analyzing growth-driving programs. This skill covers the fundamental differences between customer referral schemes and paid affiliate models. Learn to identify key trigger moments, structure compelling incentive mechanisms (single/double-sided), and calculate optimal reward sizes to maximize viral loops and scale customer acquisition for sustained business growth.

This tool facilitates an honest and rigorous retrospective on completed strategic decisions. It requires scoring the outcome against pre-committed success and kill criteria, auditing original assumptions, and revisiting dissenting opinions. Used at 90-day checkpoints or quarterly reviews to formalize lessons learned and close the strategic decision loop.

A structured playbook for improving the time and space complexity (Big-O) of existing, slow code. This skill guides developers through systematic refactoring, focusing on identifying bottlenecks like nested loops or N+1 queries. It emphasizes applying transformations one at a time, maintaining test coverage, and measuring performance gains to ensure correct and efficient code.

Invariant-Guard enforces a correctness-first approach to algorithm design. Instead of writing code and hoping it works, this skill mandates defining the function contract, loop invariants, termination arguments, and all potential edge cases *before* implementation. It is essential for complex algorithms where subtle bugs (like off-by-one errors or missed post-conditions) are common, ensuring mathematically rigorous and verifiable code.