Agent Skills

by anthropic

Zoom Virtual Agent SDK for web embeds. Use for campaign or entry ID chat launch, event-driven controls, user context updates, and CSP-safe deployment.

zoom-video-sdk-android

by anthropic

Zoom Video SDK for Android native apps. Use when building custom Android video experiences with full UI control, session tokens, raw media options, and event-driven participant state.

zoom-video-sdk-ios

by anthropic

Zoom Video SDK for iOS native apps. Use when building custom iOS video sessions with full UI control, token-based session auth, and event-driven media/participant flows.

zoom-video-sdk-macos

by anthropic

Zoom Video SDK for macOS native desktop apps. Use when building custom macOS video sessions with native UI control, tokenized join, and desktop-oriented media/device workflows.

zoom-video-sdk-react-native

by anthropic

Zoom Video SDK for React Native. Use when building custom mobile video session experiences with @zoom/react-native-videosdk, event listeners, helper-based APIs, and backend JWT token flows.

debugging-difficult-bugs

by mastra-ai

Use early when debugging a medium or hard bug, especially when tests alone may not reveal the real runtime failure. Trigger this before extended TDD iteration when a bug involves runtime state, ordering, persistence, streaming, concurrency, UI/manual reproduction, external services, or when a red or newly passing test may not model the real issue. Skip only when the root cause is already directly proven by a stack trace or deterministic test that exercises the real runtime path.

agent-prompt-quality-bar

by mastra-ai

Universal quality bar and final audit rubric for any agent system prompt. Activate this whenever you are unsure which archetype skill applies, or as a final review pass before writing the system prompt. It defines the required run contract, completion criteria, fallback paths, response format, and anti-patterns every produced agent prompt must satisfy.

code-standards

by mastra-ai

Code quality standards and style guide for reviewing pull requests

coding-agent

by mastra-ai

Authoring playbook for building agents that write, edit, review, or refactor code. Use this when the user asks for an agent that writes scripts, generates code, reviews pull requests, refactors a codebase, fixes bugs, implements features, writes tests, or works with programming languages such as Python, TypeScript, JavaScript, Go, Rust, SQL, or shell.

content-writer-agent

by mastra-ai

Authoring playbook for building agents that draft written content — blog posts, marketing copy, social media posts, newsletters, product descriptions, landing pages, or ad copy. Use this when the user wants an agent that writes, drafts, or rewrites text for publication or marketing.

customer-support-agent

by mastra-ai

Authoring playbook for building agents that triage and reply to customer messages — support tickets, email inquiries, chat questions, refund requests, or product issues. Use this when the user wants an agent that handles inbound customer questions, drafts replies, escalates hard cases, summarizes tickets, or follows a support playbook.

general-tasks

by mastra-ai

Default process for handling open-ended tasks that touch files, the shell, or the web.

generic-assistant

by mastra-ai

Fallback authoring playbook for building general-purpose personal assistant agents that do not fit a more specific archetype. Use this only after checking the other archetype skills (coding, spreadsheet, research, customer-support, content-writer, ops-automation). Examples include summarizing emails, drafting short answers, capturing notes, or generic personal-helper agents.

ops-automation-agent

by mastra-ai

Authoring playbook for building agents that automate recurring internal tasks — running scheduled workflows, syncing data between systems, posting notifications, processing inbound events, or executing operational runbooks. Use this when the user wants an agent that runs on a schedule, reacts to events, automates a process, syncs between tools, or handles ops/internal infrastructure.

performance-review

by mastra-ai

Performance-focused code review for identifying bottlenecks and optimization opportunities

pr-explainer

by mastra-ai

Use when creating an approachable, self-contained HTML review aid for a pull request; explaining what changed, why it matters, how it works, and how it fits into the broader system; turning PR diffs, commits, tests, and architecture context into a local `.pr-review/` HTML page for reviewers; or helping reviewers understand complex code changes without dumping the full diff.

pr-splitter

by mastra-ai

Use when breaking a large, complex, messy, or hard-to-review pull request into multiple smaller PRs; planning stacked PRs; extracting independent changes from a branch; splitting mixed refactor and behavior changes; managing drift after review feedback; rebasing follow-up PRs as earlier PRs change; or preserving original branch intent while shipping incrementally.

research-agent

by mastra-ai

Authoring playbook for building agents that search, read, and synthesize information into a report. Use this when the user wants an agent to research a topic, summarize sources, compare options, do competitive analysis, monitor news, generate briefs, or pull together a citation-backed report from the web or internal documents.

research-tasks

by mastra-ai

Process for answering research questions using the web and saving findings into the workspace.

security-review

by mastra-ai

Security-focused code review checklist for identifying vulnerabilities

spreadsheet-agent

by mastra-ai

Authoring playbook for building agents that read or write tabular data — Google Sheets, Microsoft Excel, CSV, Airtable, Notion databases, or any spreadsheet. Use this when the user wants an agent that updates rows, reads cells, computes totals, generates reports from sheets, syncs data between spreadsheets, or automates anything involving rows, columns, ranges, or worksheets.

tailwind-v4

by mastra-ai

Tailwind CSS v4 usage guide and v3-to-v4 differences. This skill should be used when writing, reviewing, or refactoring any Tailwind CSS code in this repo. Triggers on tasks involving Tailwind classes, @theme blocks, CSS-first configuration, or cleanup of v3-era syntax.

ralph-plan

by mastra-ai

Interactive planning assistant that helps create focused, well-structured ralph-loop commands through collaborative conversation

react-best-practices

by mastra-ai

React performance optimization guidelines from Mastra Engineering. This skill should be used when writing, reviewing, or refactoring React code to ensure optimal performance patterns. Triggers on tasks involving React components, data fetching, bundle optimization, or performance improvements.

commit

✓★ 25,801

by openai

Create a well-formed git commit from current changes using session history for rationale and summary; use when asked to commit, prepare a commit message, or finalize staged work.

pull

✓★ 25,801

by openai

Pull latest origin/main into the current local branch and resolve merge conflicts (aka update-branch). Use when Codex needs to sync a feature branch with origin, perform a merge-based update (not rebase), and guide conflict resolution best practices.

arxiv-search

by langchain-ai

Searches arXiv for preprints and academic papers, retrieves abstracts, and filters by topic. Use when the user asks to find research papers, search arXiv, look up preprints, find academic articles in physics, math, CS, biology, statistics, or related fields.

blog-post

by langchain-ai

Writes and structures long-form blog posts, creates tutorial outlines, and optimizes content for SEO with cover image generation. Use when the user asks to write a blog post, article, how-to guide, tutorial, technical writeup, thought leadership piece, or long-form content.

code-review

by langchain-ai

Perform a structured code review of changes, checking for correctness, style, tests, and potential issues.

coding-prefs

by langchain-ai

Read the user's coding preferences from /memory/coding-prefs.md before making non-trivial style decisions, and append new preferences when the user gives durable feedback.