Skill UI

NeuroKit2 is a Python biosignal processing toolkit for ECG, EEG, EDA, RSP, EMG, and EOG data that automates cleaning, feature extraction, and HRV/complexity analysis for psychophysiology, clinical, and HCI studies.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This advanced orchestration layer acts as the central point between founders and C-suite advisors. It analyzes complex strategic questions, scores decision complexity, and automatically routes queries to the necessary expert roles (e.g., CFO, CTO). It coordinates multi-agent 'board meetings' and synthesizes conflicting expert opinions into structured, actionable decision logs for major business decisions.

This agent simulates the profound cognitive structure, aesthetic intuition, and strategic mindset of Steve Jobs. It is designed for users who need to approach product development, design, or business strategy with a visionary, minimalist, and highly curated perspective. Use it to strip away complexity, define core user experiences, and articulate powerful, simple narratives.

Helps founders and go-to-market teams select between PLG, hybrid, and sales-led motions, design value-before-purchase flows, and optimize time-to-first-value based on price, complexity, and buyer signals.

This skill intercepts the response flow before generation, allowing the user to explicitly control the desired depth, length, and token usage of the AI's answer. It estimates the complexity of the input prompt and presents multiple options (e.g., 25%, 50%, 75%, 100%), ensuring the output matches the user's need, whether they require a brief summary or a comprehensive, detailed analysis.

A comprehensive guide to achieving high code quality in Go projects using `golangci-lint`. This skill covers the entire development lifecycle, including setting up the essential `.golangci.yml` configuration, running automated linters (style, security, complexity), automatically fixing issues, interpreting detailed lint output, and correctly suppressing false positives using `//nolint` directives. Ideal for enforcing best practices in CI/CD pipelines and maintaining legacy codebases.

Reads `.agent.md` or `.prompt.md` files, assesses task complexity, tooling, and requirements, then recommends the most cost-efficient GitHub Copilot model(s) (e.g., GPT-5, Claude Sonnet) with rationale for users across different subscription tiers and priorities.

Refactors a specified method into smaller helper routines so the cognitive complexity stays below a given threshold while keeping validation, error handling, and behavior intact.

Python library for medicinal chemistry filtering of compound libraries. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alerts (PAINS, NIBR, ChEMBL), complexity metrics, chemical group detection, and a query language for large-scale molecular triage in drug discovery.

Build or audit reproducible research workflows with NeuroKit2 for physiological time-series: preprocessing, event/interval analysis, multimodal alignment, variability and complexity on ECG, RSP and EDA signals.

This guide details advanced strategies for optimizing Claude API usage to reduce latency and cost. Techniques covered include prompt caching (significant cost reduction), strategic model selection (Haiku, Sonnet, Opus) based on task complexity, implementing streaming for perceived speed, and managing token limits. Use this guide when building high-performance, cost-efficient LLM applications that interact with large language models.