productivity▌

Portfolio risk measurement with VaR, CVaR, Sharpe, Sortino, and drawdown analysis. \n \n Covers 15+ risk metrics across volatility, tail risk, drawdown, and risk-adjusted return categories with parametric, historical, and Cornish-Fisher VaR methods \n Includes rolling window analysis, portfolio-level calculations with marginal risk contribution and risk parity optimization, and stress testing against historical crises or hypothetical shocks \n Supports Monte Carlo simulation with elevated volati

Universal API for orchestrating multiple AI coding agents in sandboxed environments. \n \n Supports four coding agents (Claude Code, Codex, OpenCode, Amp) with a unified schema for responses, events, and session management \n Deploy locally or to cloud sandboxes including E2B, Daytona, Docker, Vercel, and Cloudflare with environment-specific configuration \n Stream agent events, manage session persistence across multiple backends (in-memory, SQLite, Postgres, IndexedDB), and handle human-in-the-

Generate Schema.org JSON-LD markup for search engine rich results and AI understanding. \n \n Supports 10+ schema types including FAQPage, HowTo, Article, Product, LocalBusiness, Organization, BreadcrumbList, Event, and Recipe with validation against Google Rich Results requirements \n Maps content to appropriate schema types based on page purpose (blog, product, FAQ, local business, etc.) and identifies eligible rich result opportunities \n Generates valid JSON-LD with all required and optional

Text-to-speech with dual backends, voice cloning, and timeline-accurate audio synthesis for dubbing and video narration. \n \n Supports two backends: Kokoro (local, offline) for simple speech synthesis, and Noiz (cloud) for voice cloning, emotion control, and precise segment timing \n Simple mode converts text, files, or URLs to audio with optional voice cloning from reference audio; timeline mode aligns speech to SRT subtitles with per-segment voice and emotion control \n Voice maps enable gran

Command-line interface for creating, updating, and querying Linear issues with git and jj integration. \n \n Covers 11+ command categories including issues, teams, projects, cycles, milestones, initiatives, labels, and documents \n Supports file-based flags for markdown content to ensure proper formatting and avoid shell escaping issues \n Includes direct GraphQL API access via the api command for operations not covered by the CLI, with schema inspection tools \n Requires the linear CLI tool to

This skill provides procedural guidance for configuring GitHub secret scanning — detecting leaked credentials, preventing secret pushes, defining custom patterns, and managing alerts.

Slash commands are frequently-used prompts defined as Markdown files that Claude executes during interactive sessions. Understanding command structure, frontmatter options, and dynamic features enables creating powerful, reusable workflows.

Frameworks and best practices for clear, professional communication across emails, team chat, meetings, and technical audiences. \n \n Covers four core areas: the What-Why-How structure for organizing messages, email templates with subject line formulas, team messaging etiquette including the \"no hello\" principle, and strategies for translating technical concepts to non-technical audiences \n Includes audience calibration guidance, jargon-to-plain-language translation examples, and clarity pri

Progressive context refinement loop for multi-agent workflows solving the subagent context problem. \n \n Four-phase cycle (dispatch, evaluate, refine, loop) that iteratively narrows retrieval to high-relevance files, capping at 3 iterations to balance token usage and context quality \n Scores retrieved files on a 0–1 relevance scale and explicitly identifies missing context gaps to drive the next refinement cycle \n Learns codebase terminology and patterns during the first cycle, improving subs

PyMC is a Python library for Bayesian modeling and probabilistic programming. Build, fit, validate, and compare Bayesian models using PyMC's modern API (version 5.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, and model comparison (LOO, WAIC).