Plugins for mathematical computing, scientific research, and developer productivity
npx claudepluginhub james-traina/science-pluginsGives Claude a real math engine. Ask a math or science question and Claude translates it to Wolfram Language, runs it through wolframscript, and hands back the exact answer — symbolic algebra, calculus, plotting, statistics, and more. No special syntax needed.
AI research assistant for quantitative social science. Ambient hooks detect research context and route to 10 specialized agents covering structural econometrics, causal inference, game theory, identification, Monte Carlo studies, and reproducible pipelines.
Routes mechanical coding tasks — test writing, documentation, formatting, and code generation — to OpenAI Codex instead of Claude, cutting token costs on work that doesn't need deep reasoning.
Port of ralph-orchestrator to Claude Code's official plugin system. Runs your prompt in a loop until a verification command passes. Solo mode for one session, team mode for parallel agents. Logs telemetry for post-session QA.
Directory of popular Claude Code extensions including development tools, productivity plugins, and MCP integrations
Curated collection of 129 specialized Claude Code subagents organized into 10 focused categories
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A personal Claude Code plugin marketplace for mathematical computing, scientific research, and developer productivity. Add the marketplace once, then install any plugin with a single command.
Claude Code can be extended with plugins — bundles of agents, commands, skills, and hooks that add new capabilities to your sessions. This repository is a marketplace catalog: it tells Claude Code where to find plugins and what they contain. Point Claude Code at this catalog once, and from then on you can install any listed plugin with /plugin install.
The plugins here are focused on quantitative research and developer productivity, and they're designed to work together. wolfram-hart handles symbolic math. compound-science handles econometric workflows. claude-codex routes mechanical tasks to a cheaper model. ralpha-team drives long-running jobs to completion without babysitting.
Claude Code plugins have four types of components:
/estimate, /eval). They run a structured prompt with defined tools and argument handling.Most real-world behavior comes from combining these. A hook detects context, a skill loads additional instructions, a command handles the interface for the heavy lifting.
/plugin marketplace add James-Traina/science-plugins
This downloads the catalog and registers it locally. You don't need to reinstall the marketplace when plugins are updated.
Gives Claude a real computer algebra system. Without this, Claude approximates symbolic math: it reasons about it rather than computing it. This plugin connects Claude to wolframscript, the command-line interface to Wolfram Language (the engine behind Mathematica and Wolfram Alpha).
When a math or science question comes up, the included skill triggers. Claude translates the problem to Wolfram Language, runs it through wolframscript, and shows you the computed result — the actual output, not a reconstruction from training data. Plots are saved as PNGs and shown inline. You never need to write any Wolfram Language yourself.
Coverage includes symbolic algebra, calculus, linear algebra, statistics, differential equations, number theory, combinatorics, and plotting. The three commands give you direct control: /eval runs Wolfram Language code you supply; /check verifies a mathematical claim step-by-step; /patterns lists the problem types the skill handles. The wolfram-reviewer agent audits a derivation or proof, running each step through the engine to check it independently.
Components: 1 skill · 3 commands (/eval, /check, /patterns) · 1 agent (wolfram-reviewer)
Prerequisites: A free Wolfram Engine (local, ~1 GB) or wolframscript for cloud evaluation. Requires a free Wolfram ID.
/plugin install wolfram-hart@science-plugins
A research workflow toolkit for structural econometrics, causal inference, game theory, applied micro, identification arguments, Monte Carlo studies, and reproducible pipelines. Inspired by compound-engineering: the same plan → work → review → compound loop, adapted for quantitative social science.
The organizing idea is that solved problems should stay solved. Every methodological fix — a convergence workaround, an identification argument that held up, a numerical issue and how it was handled — gets documented and made searchable in docs/solutions/. The next project starts where the last one left off. Over time you build a searchable record of hard-won solutions, and Claude can draw on it when facing a similar problem.