Skill UI

A powerful tool for designing and writing deterministic, multi-agent workflow scripts in JavaScript. It allows users to orchestrate complex, repeatable, multi-step tasks using advanced patterns like fan-out, pipelines, loops, and judge-panels. Ideal for automating sophisticated business processes that require multiple specialized sub-agents and robust context management.

Comprehensive tool for finding and fixing WCAG 2.2 accessibility violations across codebases or live web pages. Operates in two modes: Report mode generates a prioritized, detailed, non-editing audit report (ideal for code reviews), while Fix mode performs an audit-edit-verify loop directly on the target to apply necessary fixes.

A meta-skill designed to generate a comprehensive daily operating brief. It intelligently combines structured data from pasted context (calendars, tasks, emails), user memory (priorities, open loops), and external factors like weather, providing a holistic and actionable plan for the day. Ideal for users needing a single, high-level summary of their commitments and priorities.

Arbor executes an Autonomous Optimization (AO) loop, allowing users to iteratively improve complex artifacts—such as models, agents, or data pipelines—against a measurable objective. It implements Hypothesis Tree Refinement (HTR), structuring research into a persistent tree to track hypotheses, insights, and evidence. This process is crucial for long-horizon optimization tasks (e.g., Kaggle-style benchmarking) and utilizes a held-out test set to prevent overfitting and ensure cumulative, auditable progress.

This skill guides the process of routing a complex goal through an AI's scored-memory loop using the `acc_act(runtime="solve")` function. It provides a structured workflow for deliberation, handling intermediate `brain_frame` results, and submitting continuous proposals. It emphasizes systematic reasoning and requires careful resolution of all generated frames to ensure a robust and verifiable solution.

A comprehensive reference guide detailing best practices for writing robust and efficient Bash shell scripts. It covers critical topics such as proper quoting (`"$var"`), safe conditional testing (`[[ ]]`), reliable loops, advanced piping techniques, and essential error handling using `set -euo pipefail`. Use this guide to elevate your scripting skills and write highly portable, production-ready shell code.

A human-in-the-loop studio for iteratively tuning AI outputs or reviewing complex media/prose. It allows users to directly manipulate visual parameters (color, typography, layout) or annotate documents in real-time, moving beyond static comparison grids or vague prompts. Perfect for designers and content creators who need hands-on, visual feedback.

A sophisticated automated loop that uses vision or video models to evaluate subjective aesthetic quality (e.g., timing, color grade, layout). Instead of relying on numeric metrics, the system renders multiple parameterized variants into one artifact, asks the AI to rate them, and suggests optimal values, repeating the process until convergence. Ideal for design iteration where human judgment is the primary success criteria.

This comprehensive guide details the idiomatic and efficient way to write Scala code. It covers crucial modern practices, such as utilizing functional transforms over traditional loops, embracing pattern matching, using Option/Either for robust error handling instead of null checks or exceptions, and managing concurrency safely. Essential reading for any Scala developer aiming for high-quality, maintainable, and performant codebases.

This skill outlines a systematic, multi-phase workflow for diagnosing hard bugs and performance regressions in complex codebases. It emphasizes the critical necessity of building a tight, deterministic feedback loop (Phase 1) before moving to hypothesis generation. The process guides users through reproducing the failure, minimizing the scope of the issue, and forming actionable, ranked hypotheses.

This skill assists users in defining, adapting, and designing bounded, structured feedback loops for AI agents. Whether you need to find a suitable existing workflow, fine-tune a published loop, or design a new process from scratch, this tool guides you through a safe, iterative methodology. It emphasizes setting clear stopping rules, guardrails, and verifiable, measurable steps to ensure the automation remains controllable and focused.

PyRIT (Python Risk Identification Tool) is an open-source automation framework designed for advanced, multi-turn adversarial red-teaming of conversational LLMs. It simulates real-world attack scenarios by orchestrating an attacker model and a scorer model in a feedback loop. Features include Crescendo (gradual escalation) and Tree-of-Attacks with Pruning (TAP) techniques to detect complex vulnerabilities like prompt injection and jailbreaks in stateful dialogues.