Tools

Implements the Ralph Wiggum loop with a Stop hook: feeds Claude the same prompt repeatedly so it keeps iterating on its previous file changes until it outputs a completion marker or hits the iteration limit. Official Anthropic plugin.

Connects Claude Code to a TypeScript/JavaScript language server for go-to-definition, find references and live type error checking. Install typescript-language-server globally first. Official Anthropic plugin.

Official Anthropic plugin that solves competition math such as IMO and Putnam problems, with adversarial checks by an independent verifier that only sees the clean proof. Gives up explicitly rather than bluffing when unsure, and outputs LaTeX/PDF once verified.

Three layers of security checks on agent-written code: rule-based warnings on about 25 risky patterns during edits, a model review of the diff at turn end, and an agent tracing cross-file bugs like IDOR and SSRF at commit. Official Anthropic plugin; needs Python 3.8+ and model API access.

Official Anthropic plugin that reads local session logs in ~/.claude/projects and generates an interactive HTML usage report covering tokens, cache hit rate, subagents, skills and the most expensive prompts, with optimization tips. Needs Node.js.

Official Anthropic plugin that builds a shareable status page for big multi-track projects such as migrations, launches or research: goals, workstream order, next steps and risks; say 'refresh' to update the same link. Needs a claude.ai login; Artifacts are a beta for Team and Enterprise plans.

Designs and builds MCP servers: first picks a deployment model among remote HTTP, MCPB bundles and local stdio, then guides tool design, auth and in-chat interactive components. Maintained by Anthropic.

Universal document converter between dozens of formats including Markdown, Word, HTML, LaTeX and EPUB, with templates, citations and Lua filters. PDF output uses LaTeX by default, which you install separately.

Turns common prompts such as summarizing, extracting insights and rewriting into reusable patterns you pipe text, web pages or YouTube transcripts through, using many model providers or local Ollama. Can also run as a REST API. Run fabric --setup first to configure models.

Lightweight multi-protocol command-line downloader that fetches in parallel segments from multiple sources over HTTP(S), FTP, SFTP, BitTorrent and Metalink. Resumes downloads, reads URLs in bulk from text files and offers a JSON-RPC interface for scripts or agents.

Usage rules for daisyUI 5: combine component, part and modifier classes, add Tailwind utilities when needed and the ! override only as a last resort, use the default variant unless asked, make layouts responsive, and follow Refactoring UI for design choices. Part of the official daisyUI skill.

Configuration reference for daisyUI 5: how to set enabled themes, the default and prefers-dark theme, root selector, included or excluded modules, class prefix and console logs in the CSS plugin block, with full examples. Part of the official daisyUI skill.

Installation guide for daisyUI 5: requires Tailwind CSS 4 without tailwind.config.js, installs via npm and adds the plugin in CSS, covers the CDN setup, and links framework guides for Next.js, Vue, SvelteKit, Laravel and more. Part of the official daisyUI skill.

Official daisyUI skill: the agent uses daisyUI 5 component classes, semantic colors and themes whenever it writes HTML or JSX, picking components by intended function, reading their guides and following their exact structure. Includes install, usage, config and color guides. Built on Tailwind CSS 4.

Color rules for daisyUI 5: explains semantic names such as primary, base-100 and error with their -content foregrounds, says to prefer them over fixed Tailwind colors and never add dark: to them, and shows how to enable, create and override themes. Part of the official daisyUI skill.

Business analytics plugin from the wshobson/agents marketplace: KPI dashboard design and data storytelling skills plus a business analyst subagent to choose metrics, plan dashboard layouts and turn data into reports executives or investors understand. Methods and templates only; no data connections.

Quant trading plugin from the wshobson/agents marketplace: quant analyst and risk manager subagents plus backtesting and risk metrics skills for strategy backtests, portfolio optimization and risk measures like VaR, Sharpe ratio and max drawdown. Needs local Python; not investment advice.

Accessibility review plugin with an audit command, a UI validation subagent, and WCAG 2.2 and screen reader testing skills. Before design reviews or launch, the agent checks contrast, keyboard use and screen reader support and suggests fixes. For Claude Code; automated checks need Node.js.

Data engineering plugin from the wshobson/agents marketplace: a data engineer subagent, a data pipeline command, and skills for Airflow DAGs, dbt transformations, Spark tuning and data quality, to help build ETL and data warehouses.

Engine for math and explainer animations in Python (Community Edition, ManimCE): write scenes and render them to video with the manim command, letting agents create teaching and science animations. On macOS install cairo and pkg-config first; formulas need LaTeX.

Context-driven development plugin: first captures product vision, tech stack and workflow docs, then generates a spec and phased plan for each feature, executes with TDD and supports per-task rollback. Ported from Google's Conductor for Gemini CLI.

Orchestrates multi-agent teams using Claude Code's experimental Agent Teams feature: parallel multi-angle code review, competing-hypothesis debugging and feature work split by file ownership. Enable CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS first.

JS/TS development plugin with javascript-pro and typescript-pro subagents, a TypeScript project scaffolding command, and 4 skills on modern JavaScript, advanced TypeScript types, Node.js backends and JS testing. Part of the wshobson/agents marketplace.

MLOps plugin from the wshobson/agents marketplace: data scientist, ML engineer and MLOps engineer subagents and a /ml-pipeline command that runs the pipeline in phases from data analysis and training to deployment and monitoring, plus ML pipeline and recommender system skills.