Skill UI

Guides breaking multifaceted work into short, specific tasks with clear verification steps, logical ordering, and project-aware scripts so teams can plan new projects, features, or bug fixes without bloated templates.

Systematically identify failing tests, group similar errors, and fix them in priority order so CI/CD runs stabilize after refactors or implementation work. Focused verification, dependency-aware ordering, and repeatable workflows ensure a clean `make test` pass.

Submit and manage autonomous lab protocols on Ginkgo Cloud Lab, leveraging RACs for cell-free protein expression validation/optimization, fluorescent pixel art, EstiMate AI assessments, and guided ordering of sample inputs through the web interface.

Guides Odoo users through configuring FIFO/AVCO valuation, reordering rules, putaway strategies, warehouse routes and multi-step flows to ensure accurate stock levels and traceable receipts/shipments.

Best practices for building reusable Robius/Moly-inspired Makepad widgets, covering modal overlays, collapsible sections, drag-drop reordering, avatar text/image toggles, and dynamic styling utilities.

Structured red team engagement planning that blends MITRE ATT&CK kill-chain ordering, technique scoring, choke point and OPSEC analysis, and crown-jewel targeting into authorized offensive simulations with tooling like the engagement planner.

Analyzes ARIS usage logs to spot harness inefficiencies and recommend tweaks to SKILL.md prompts, defaults, and workflow ordering, guided by Meta-Harness principles for better outer-loop orchestration.

Checklist for securing Solidity AMMs, detailing reentrancy defenses, CEI ordering, donation manipulation guards, oracle hardening, slippage checks, reserve math safety, and audit tooling for liquidity pools and swap flows.

This skill safely randomizes the order of repetitive JSON objects within an array without corrupting the data or breaking the JSON syntax. It acts as a data engineer, rigorously validating the input schema (checking for property consistency and lack of nesting) before performing the shuffle. It ensures data integrity is maintained while reordering records, making it ideal for testing, simulation, or data preparation workflows.