Skill UI

Guides GTM measurement for AI products by outlining revenue, efficiency, pipeline, retention, and funnel metrics, aligning dashboards with attribution models and weekly reviews so founders and revenue leaders monitor ARR, CAC, TTFV, NRR, and cost signals without implementation detail.

This skill provides a comprehensive framework for tracking vulnerability aging and enforcing Service Level Agreements (SLAs). It calculates time-to-remediation against severity-based deadlines (e.g., 14 days for critical), incorporating adaptive modifiers and automated escalation paths. It is essential for establishing compliance policies, measuring remediation efficiency, and managing organizational risk.

Executes a structured VC valuation workflow that ingests SaaS or startup metrics, runs DCF, comparable revenue multiples, and SaaS health models via Python, and synthesizes a defensible valuation, runway, and capital-efficiency narrative for users planning fundraising or benchmarking.

This skill provides guidance on implementing common architectural patterns and best practices within the PyTorch framework. It is designed for developers building sophisticated deep learning models, helping to improve structure, efficiency, and code maintainability when working with neural networks.

This skill reviews code differences (diffs) to identify opportunities for improvement in clarity, reusability, efficiency, and overall quality. It employs multiple specialized sub-agents (e.g., Code Reuse, Efficiency, Quality) to audit changes. It can provide high-confidence, behavior-preserving fixes when requested, ensuring the code adheres to best practices without introducing regressions. Best used when refactoring, cleaning up, or auditing a specific code scope.

A comprehensive tool for establishing performance baselines across multiple dimensions. It measures Core Web Vitals (LCP, CLS, INP), API latency (p50, p95, p99), and development build efficiency. Essential for detecting performance regressions before merging code, ensuring stability in CI/CD pipelines, and comparing alternative stack implementations.

Tmux is a powerful terminal multiplexer that enables users to manage multiple sessions, windows, and panes within a single terminal connection. It is essential for maintaining persistent connections on remote servers (surviving SSH disconnects), running long-term background jobs, and automating complex, multi-pane workflows directly from shell scripts, significantly enhancing remote development efficiency.

This skill interactively monitors AI training sessions by periodically checking WandB or fallback logs for anomalies like NaN, divergence, or plateaus. It analyzes loss trends and evaluation metrics to determine whether to continue, wait, or stop the training run, ensuring training quality and resource efficiency.

Provides a read-only report showing how much the context-mode feature has preserved session data, including token consumption, savings ratio, and tool-by-tool breakdown for evaluating context efficiency.

A specialized audit skill designed to identify and quantify resource leakage, excessive costs, and billing vulnerabilities within complex development tool workflows (e.g., GitHub Apps). It traces webhook events, queue processing, and model calls to pinpoint where token burn or unauthorized resource usage is occurring, ensuring cost efficiency and clean billing practices.

A comprehensive guide and skill set for optimizing Go (Golang) application performance. It establishes a core philosophy of profiling before optimizing, focusing on reducing allocations, improving CPU efficiency, and addressing structural anti-patterns. Provides systematic modes for deep review (hot path, architecture review) and follows an iterative methodology (Benchmark -> Diagnose -> Improve -> Compare) to ensure measurable and stable performance gains.

This utility compresses natural language documents (notes, preferences, memory files) into a highly condensed, 'caveman-speak' format. Its primary function is drastically reducing input tokens for efficiency while meticulously preserving all technical substance, including code blocks, URLs, file paths, and markdown structure. Ideal for optimizing memory consumption when interacting with AI models.