claude-team
Orchestrate multiple Claude Code workers via iTerm2 using the claude-team MCP server. Spawn workers with git worktrees, assign beads issues, monitor progress, and coordinate parallel development work.
Orchestrate multiple Claude Code workers via iTerm2 using the claude-team MCP server. Spawn workers with git worktrees, assign beads issues, monitor progress, and coordinate parallel development work.
Use when creating or developing anything, before writing code or implementation plans - refines rough ideas into fully-formed designs through structured Socratic questioning, alternative exploration, and incremental validation. Optimized for git worktree workflows and Claude CLI agent patterns.
Suite of tools for creating elaborate, multi-component chat-embedded HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
Verify code, documents, or implementation against requirements using LLM Council multi-model deliberation. Use when you need multi-model consensus on correctness, completeness, or quality. Keywords: verify, check, validate, review, approve, pass/fail, consensus, multi-model
Creates multiple git worktrees and launches parallel Claude sessions for simultaneous task execution. Maximum 3 tasks.
Synchronize and update Claude Code and GitHub Copilot development tool configurations to work similarly. Use when asked to update Claude Code setup, update Copilot setup, sync AI dev tools, add new skills/prompts/agents across both platforms, or ensure Claude and Copilot configurations are aligned. Covers skills, prompts, agents, instructions, workflows, and chat modes.
Save TabularPredictor artifacts with TabularPredictor.save, detailing all arguments and persistence behavior; depends on autogluon-tabularpredictor-fit and pairs with autogluon-tabularpredictor-load.
Set the default prediction model with TabularPredictor.set_model_best, detailing all arguments and effects; depends on autogluon-tabularpredictor-fit and is often guided by autogluon-tabularpredictor-fit-summary or leaderboard outputs.
Load saved predictors with TabularPredictor.load, detailing all arguments and security/version implications; depends on autogluon-tabularpredictor-save and assumes a prior autogluon-tabularpredictor-fit.
Decode intermediate layer predictions using the Logit Lens technique. Use when analyzing what a model predicts at each layer, understanding information flow, or visualizing layer-wise processing.
Explain and configure AutoGluon’s TabularPredictor constructor, including all init arguments and their effects for tabular classification/regression; prerequisite for autogluon-tabularpredictor-fit, predict-proba, save/load, fit-summary, calibrate-decision-threshold, set-decision-threshold, and set-model-best.
Build a Through-the-Door training set with reject inference using fuzzy augmentation, including PD-based sample weights; pairs with autogluon-tabularpredictor-fit for modeling the augmented data.
Define and apply monotonic constraints in AutoGluon using a constraints dictionary and a feature-ordered list for boosting models; depends on autogluon-tabularpredictor-fit for passing hyperparameters.
Build scikit-learn compatible custom estimators by following the official “rolling your own estimator” rules for __init__, fit/predict, validation, learned attributes, tags, and estimator checks; prerequisite for autogluon-sklearn-wrapper or any sklearn-facing wrappers.
Build a scikit-learn compatible wrapper for AutoGluon TabularPredictor with feature name checks, sample_weight support, and predict/predict_proba methods; depends on custom-sklearn-estimator for sklearn API rules and autogluon-tabularpredictor-class/fit for predictor usage.
Personal development philosophy emphasizing experiment-driven, fail-fast approach. Activate when planning implementations, reviewing code architecture, making design decisions, or when user asks to apply development principles. Guides against over-engineering and towards solving real problems with simple solutions. (project, gitignored)
Repository management strategies including branch strategies (Git Flow, GitHub Flow, trunk-based), monorepo patterns, submodules, and repository organization. Use when user needs guidance on repository structure or branching strategies.
Authentication library for Next.js applications (NextAuth.js v5). Use when building Next.js 14+ apps that need OAuth providers (GitHub, Google, etc.), credentials login, or session management. Provides adapters for Prisma, Drizzle, and other databases. Choose Auth.js over Passport.js for Next.js App Router projects.
Type-safe API client generator from OpenAPI schemas with full ecosystem support. TRIGGER WHEN: - Setting up API client with OpenAPI schema (@devup-api/fetch) - Making typed API requests (GET, POST, PUT, PATCH, DELETE) - Using React Query hooks (@devup-api/react-query) - Using Zod validation schemas (@devup-api/zod) - Building forms with react-hook-form (@devup-api/hookform) - Creating CRUD interfaces (@devup-api/ui) - Configuring Vite/Next.js/Webpack/Rsbuild plugins - Implementing authentication middleware - Using DevupObject for type references
Git workflow and commit guidelines. Trigger keywords: git, commit, push, .git, version control. MUST be activated before ANY git commit, push, or version control operation. Includes security scanning for secrets (API keys, tokens, .env files), commit message formatting with HEREDOC, logical commit grouping (docs, test, feat, fix, refactor, chore, build, deps), push behavior rules, safety rules for hooks and force pushes, and CRITICAL safeguards for destructive operations (filter-branch, gc --prune, reset --hard). Activate when user requests committing changes, pushing code, creating commits, rewriting history, or performing any git operations including analyzing uncommitted changes.