Browse skills

Comprehensive guide for creating software diagrams using Mermaid syntax. Use when users need to create, visualize, or document software through diagrams including class diagrams (domain modeling, object-oriented design), sequence diagrams (application flows, API interactions, code execution), flowcharts (processes, algorithms, user journeys), entity relationship diagrams (database schemas), C4 architecture diagrams (system context, containers, components), state diagrams, git graphs, pie charts, gantt charts, or any other diagram type. Triggers include requests to "diagram", "visualize", "model", "map out", "show the flow", or when explaining system architecture, database design, code structure, or user/application flows.

Capture a website and create a HyperFrames video from it. Use when: (1) a user provides a URL and wants a video, (2) someone says "capture this site", "turn this into a video", "make a promo from my site", (3) the user wants a social ad, product tour, or any video based on an existing website, (4) the user shares a link and asks for any kind of video content. Even if the user just pastes a URL — this is the skill to use.

Execute multi-level exploratory testing of the app covering basic functionality, complex operations, adversarial testing, and cross-cutting scenarios. Deeper than /smoke-test. Use when the user asks to "exploratory test", "test thoroughly", "test all scenarios", "deep test", "test edge cases", "test everything", "break it", or "find bugs by testing".

Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules. Use when: user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more AI-navigable.

Draft, confirm, and post a single conversational reply to GitHub PR conversation comments (issue comments). The reply addresses all tracked items in one natural-prose message. Use when the user asks to "reply to PR conversation", "post PR conversation replies", or "draft PR conversation messages".

Production-ready patterns for integrating frontend applications with backend APIs, including race condition handling, request cancellation, retry strategies, error normalization, and UI state management.

Native agent-to-agent language for compact multi-agent messaging. A shared tongue agents speak directly, not a translation layer. 340+ atoms across 7 domains; 3x smaller than natural language.

AI-powered browser automation — navigate sites, fill forms, extract structured data, log in with stored credentials, and build reusable workflows.

NCAA cross country and track & field athlete data via TFRRS (tfrrs.org) and news via The Stride Report. Fetch athlete profiles including all personal records (PRs), eligibility year, school, full season-by-season results history, and XC/TF news. Zero config, no API keys. Use when: user asks about NCAA cross country, NCAA track and field, college running, TFRRS athlete profiles, personal records, PRs, XC or TF season results, individual athlete performance history, or XC/TF news. Don't use when: user asks about professional track, Diamond League, or other sports — use nfl-data, nba-data, wnba-data, nhl-data, mlb-data, golf-data, cfb-data, cbb-data, tennis-data, fastf1, or volleyball-data. For betting use polymarket or kalshi.

Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.

Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.

Pre-install security scanner for AI agent skills. 7.5% of 14,706 skills are malicious. Audit before you trust.

Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.

Migrate legacy files in .turbo/ to current formats. Covers plans and shells (layout split, frontmatter normalization, prompt-plan index cleanup) and improvements.md (legacy `trivial`/`standard` Type values rewritten to `direct`/`plan`). Use when the user asks to "migrate turbo files", "migrate turbo", "migrate turboplans", "migrate turbo plans", "upgrade plan format", "add frontmatter to plans", "convert old plans", or "migrate improvements".

Retrieves up-to-date documentation, API references, and code examples for any developer technology. Use this skill whenever the user asks about a specific library, framework, SDK, CLI tool, or cloud service -- even for well-known ones like React, Next.js, Prisma, Express, Tailwind, Django, or Spring Boot. Your training data may not reflect recent API changes or version updates. Always use for: API syntax questions, configuration options, version migration issues, "how do I" questions mentioning a library name, debugging that involves library-specific behavior, setup instructions, and CLI tool usage. Use even when you think you know the answer -- do not rely on training data for API details, signatures, or configuration options as they are frequently outdated. Always verify against current docs. Prefer this over web search for library documentation and API details.

Use when writing an OpenCLI adapter for a new site or adding a new command to an existing site. Guides end-to-end from first recon through field decoding, adapter coding, and verify. Replaces opencli-oneshot / opencli-explorer. For ad-hoc browser driving (no adapter), see opencli-browser instead; for a top-level orientation to opencli, see opencli-usage.

Claude Code Command Selection Guide - Automatically recommend and select the right commands, agents, and skills in Claude Code. Use when: (1) user is unsure which command or tool to use, (2) needs to decide which agent/skill best fits the current task, (3) querying usage scenarios for /plan, /tdd, /compact, /loop and other commands, (4) understanding when to invoke planner, code-reviewer, build-error-resolver and other agents, (5) needs command cheat sheet or decision flowchart. Triggers: "which command to use", "which agent", "command selection", "how to use /plan", "when to use /compact", "agent selection guide", "command cheat sheet", "skill recommendation".

Use when the user asks to capture a full-page screenshot, long screenshot, or complete page capture of a web page. Handles SPA scroll containers, lazy-loaded images, and very tall pages via Chrome DevTools Protocol with zero external dependencies.

Master microservices architecture patterns including service boundaries, inter-service communication, data management, and resilience patterns for building distributed systems.

Multi-cluster Kubernetes dashboard with AI-powered operations via MCP server and 10+ built-in agent skills