claude-plugin-marketplace

Use for Clerk backend request auth. PROACTIVELY activate for Express, Fastify, Node, Go, Ruby/Rails, Python/FastAPI/Django/Flask, Java/Spring, C#/.NET, PHP/Laravel, custom APIs, @clerk/express clerkMiddleware()/requireAuth()/getAuth(req), backend request state, cookies, Authorization Bearer tokens, cross-origin frontend-to-API calls, M2M/OAuth/API-key/session token validation, clerkClient Backend API users/orgs calls, proxies/CDNs/CORS/X-Forwarded headers, and 401/404 debugging. Provides middleware patterns, token verification flow, API client guardrails, and language-portable diagnostics.

Use for Clerk JWT and session token clock-skew issues. PROACTIVELY activate for "token-not-active-yet", "JWT cannot be used prior to not before", token-expired, nbf/iat/exp claim failures, clockSkewInMs in verifyToken/authenticateRequest, local dev container/WSL2/Docker Desktop time drift, NTP misconfiguration, paused VM clock, serverless cold-start clock issues, custom JWT template verification leeway across Node/Python/Go/Ruby/Java/.NET/PHP, debugging iat vs server-time delta, and separating real expiry from clock skew. Provides nbf/iat/exp semantics, Clerk and library clock-tolerance defaults, container time fix recipes, multi-language leeway patterns, diagnostics, and gotchas.

Use for Clerk dev/prod readiness, deployment, and multi-language implementation planning. PROACTIVELY activate for environment variables, pk_test/sk_test vs pk_live/sk_live, local dev, preview/staging/prod instances, domains/DNS, redirects, OAuth credentials, custom domains/proxy, authorizedParties, CSP, CORS/cookies, webhooks/tunnels, Vercel/Netlify/Cloudflare/API gateways, monitoring/troubleshooting, and backends in Node/Express/Fastify, Python/FastAPI/Django/Flask, Go, Ruby/Rails, Java/Spring, .NET, PHP/Laravel. Provides checklists, rollout plans, and language-portable patterns.

Use for Clerk frontend auth flows. PROACTIVELY activate for React, JavaScript, Vue, Nuxt, Astro, Expo, React Router, TanStack React Start, or SPA setup; ClerkProvider and publishable-key wiring; SignIn/SignUp/UserButton/UserProfile/OrganizationSwitcher; custom useUser/useAuth/useClerk/useSignIn/useSignUp/useSession/useOrganization flows; multi-session UX; cross-origin getToken() fetches; loading states, redirects, routing, CORS/cookies, or hydration bugs. Provides SDK selection, UI patterns, token-fetch templates, and frontend gotchas.

Use for Clerk in Next.js. PROACTIVELY activate for @clerk/nextjs setup, App Router auth()/currentUser(), clerkMiddleware(), proxy.ts/middleware.ts, createRouteMatcher(), protected pages/layouts/Route Handlers/Server Actions/API routes/tRPC, auth.protect() role/permission/token checks, ClerkProvider placement, server-only clerkClient, Link prefetch, redirects, 401/404 auth failures, custom domains, __clerk proxy paths, and deployment gotchas. Provides file patterns, server/client boundary rules, matcher templates, and production checks.

Use for Clerk sessions, tokens, webhooks, orgs, and security. PROACTIVELY activate for session tokens, JWT templates, getToken(), custom claims, pending sessions, multi-session UX, organizations, roles, permissions, system vs custom permissions, features/plans, MFA/passkeys/password policy/bot protection, Clerk webhooks, Svix signatures, verifyWebhook(), user/org sync, retries/replays, environment variables, custom domains, secret rotation, logs, and auth security reviews. Provides token semantics, webhook idempotency, authorization defaults, and hardening checklist.

This skill should be used when the user asks to automate Azure ML and Azure Data Factory production workflows. PROACTIVELY activate for: (1) Azure ML code asset registration, azure-ai-ml SDK, AML code versions, `result.version`, requested-vs-actual versions, (2) ADF to Azure ML orchestration, ADF WebActivity, managed identity blob reads, `connectVia`, managed VNet IR, (3) code asset version pointer blobs, latest.json contracts, training/scoring code version propagation, (4) private storage firewalls, Microsoft-hosted CI agents, temporary network rules, storage data-plane smoke tests, (5) marshmallow<4 pinning, AML SDK import failures, runtime validation for Azure ML infrastructure. Provides: operationally safe Azure ML + ADF automation patterns.

This skill should be used when the user asks to deploy, scale, or cost-optimize ML workloads on cloud platforms. PROACTIVELY activate for: (1) AWS SageMaker Studio, Training, Processing, Pipelines, Endpoints, Model Monitor, Feature Store, Clarify, Ground Truth, EC2 GPUs, EKS, Lambda, Inferentia, Trainium, (2) GCP Vertex AI Training, Pipelines, Endpoints, Feature Store, Model Monitoring, AutoML, Matching Engine, TPU, GKE, Cloud Run, (3) Azure ML workspaces, pipelines, managed endpoints, AutoML, Responsible ML, AKS/ACI, (4) Databricks, Modal, Replicate, RunPod, Lambda Labs, Anyscale. Provides: cloud ML architecture, autoscaling, hardware, security, and cost guidance.

This skill should be used when the user asks to ingest, clean, validate, transform, version, monitor, or serve ML data and features. PROACTIVELY activate for: (1) data ingestion, preprocessing, feature engineering, leakage prevention, train/serving skew, (2) Spark, Dask, Polars, pandas, Ray Data, streaming pipelines, (3) Great Expectations, TFDV, Deequ, data quality and validation, (4) DVC, lakehouse tables, dataset versioning, lineage, reproducibility, (5) Feast, Tecton, Hopsworks feature stores, point-in-time joins, online/offline features. Provides: scalable, reproducible, leakage-safe ML data pipeline design.

This skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Kubeflow, SageMaker Pipelines, Vertex AI Pipelines, Azure ML pipelines, Databricks workflows, (4) CI/CD, continuous training/evaluation, A/B tests, canary/shadow deployments, (5) drift detection, model monitoring, data validation, responsible AI governance. Provides: end-to-end MLOps architecture and operational safeguards.

Kaggle competition notebook workflows and submissions. PROACTIVELY activate for: (1) submitting notebook outputs to competitions, (2) `kaggle competitions submit -k`, (3) downloading competition data, (4) validating submission.csv format, (5) leakage review, (6) cross-validation split design, (7) public leaderboard overfitting concerns, (8) competition rule compliance, (9) reproducible top-to-bottom notebook execution, (10) fold-aware preprocessing for ML pipelines. Provides: submission commands, validation checklist, leakage controls, and competition-ready notebook guidance.

Kaggle datasets, models, sources, and kagglehub workflows. PROACTIVELY activate for: (1) downloading datasets with kagglehub, (2) uploading datasets, (3) downloading or uploading Kaggle models, (4) competition_download, (5) notebook_output_download, (6) choosing Kaggle CLI vs kagglehub, (7) attaching dataset_sources, competition_sources, kernel_sources, or model_sources, (8) model artifact transfer, (9) source dependency cleanup, (10) kagglehub limitations for notebooks. Provides: dataset/model transfer patterns, source attachment guidance, tool selection, and limitation checks.

Kaggle runtime environment, paths, accelerators, and reproducibility. PROACTIVELY activate for: (1) `/kaggle/input` path errors, (2) `/kaggle/working` output placement, (3) local vs Kaggle notebook behavior, (4) GPU/TPU/accelerator selection, (5) internet enablement, (6) package/version pinning, (7) memory cleanup and timeout issues, (8) DEBUG flags for fast runs, (9) Kaggle Secrets usage guidance, (10) quota-consuming runtime settings. Provides: path conventions, runtime checklist, accelerator IDs, reproducibility patterns, and resource safeguards.

Kaggle `kernel-metadata.json` setup and repair. PROACTIVELY activate for: (1) creating kernel-metadata.json, (2) fixing metadata validation errors, (3) setting `id`, `title`, `code_file`, language, or kernel type, (4) configuring private/public, internet, GPU, or accelerator behavior, (5) attaching dataset_sources, competition_sources, kernel_sources, or model_sources, (6) preparing metadata before `kaggle kernels push`, (7) converting script/notebook metadata. Provides: schema checklist, valid fields, source arrays, defaults, and push-readiness review.

Kaggle notebook/kernel lifecycle operations. PROACTIVELY activate for: (1) creating Kaggle notebooks or kernels, (2) `kaggle kernels init`, (3) pushing or running notebooks with `kaggle kernels push`, (4) pulling notebooks with metadata, (5) checking kernel status, files, logs, or outputs, (6) downloading output artifacts with file patterns, (7) deleting kernels, (8) Python API kernel operations, (9) auth for notebook CLI workflows. Provides: safe CLI/Python lifecycle commands, run monitoring, output retrieval, destructive-action guardrails.

This skill should be used when the user asks to evaluate, validate, compare, explain, or debug model performance. PROACTIVELY activate for: (1) metrics selection for classification, regression, ranking, NLP, CV, recommender, forecasting, and generative AI, (2) train/validation/test splits, cross-validation, grouped or time-series validation, (3) confusion matrices, ROC/PR curves, calibration, thresholds, error analysis, (4) ablation studies, statistical significance, confidence intervals, bootstrap tests, (5) bias, fairness, explainability, robustness, leakage detection. Provides: rigorous evaluation methodology and production-readiness checks.

This skill should be used when the user asks to adapt pretrained, foundation, language, vision, multimodal, or embedding models. PROACTIVELY activate for: (1) transfer learning, full fine-tuning, frozen backbones, adapters, LoRA, QLoRA, AdaLoRA, PEFT, (2) Hugging Face Transformers, Diffusers, Accelerate, TRL, RLHF, DPO, preference tuning, alignment, (3) dataset preparation, instruction tuning, chat templates, tokenization, packing, catastrophic forgetting, (4) RAG, vector databases, embedding optimization, reranking, (5) multimodal and edge fine-tuning. Provides: safe, efficient fine-tuning and adaptation guidance.

This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.

This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.

This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.

This skill should be used when the user asks for implementation recipes, examples, migration guidance, troubleshooting, or best practices for specific TUI frameworks and libraries: Rust Ratatui/Crossterm/Termion/Cursive, Python Textual/Rich/prompt_toolkit/curses/Urwid/asciimatics, Go Bubble Tea/Lip Gloss/Bubbles/tview/tcell/termui/gocui, Node Ink/Blessed/Inquirer/Prompts, .NET Terminal.Gui/Spectre.Console, C/C++ ncurses/notcurses/FTXUI, Zig vaxis, and SwiftTUI. PROACTIVELY activate for: framework-specific app structure, cleanup, testing, widgets, migration, library comparison, framework troubleshooting, API gotchas, and porting between TUI frameworks. Provides: framework defaults, gotchas, testing hooks, and selection recipes.

This skill should be used when the user asks about TUI input handling, raw mode, cbreak mode, terminal keyboard parsing, Esc vs Alt ambiguity, Ctrl-C, shortcuts, focus management, mouse events, scroll wheel, bracketed paste, paste safety, command palettes, forms, text editing, resize events, or keybinding design. PROACTIVELY activate for: raw mode, keyboard input, keybindings, mouse support, paste handling, focus model, terminal forms, text input, vim/emacs-style shortcuts, input conflicts, and cross-platform Windows/macOS/Linux input behavior. Provides: input architecture, shortcut rules, focus patterns, paste/mouse safety, and failure-mode triage.

This skill should be used when the user asks about terminal emulator compatibility, operating-system differences, tmux/screen/Zellij, SSH/remote terminals, Windows ConPTY/classic console/Windows Terminal, Unix PTYs, macOS Terminal/iTerm2, Kitty/Alacritty/WezTerm/foot/GNOME Terminal/Konsole, terminal feature matrices, escape-sequence passthrough, latency, resize propagation, or graceful degradation. PROACTIVELY activate for: compatibility audits, emulator bugs, remote TUI performance, multiplexer passthrough. Provides: support matrices, platform caveats, and fallback rules.

This skill should be used when the user asks to implement, optimize, debug, or review TUI rendering, layout, repainting, flicker, frame buffers, diff rendering, terminal resize behavior, alternate screen lifecycle, scrollback, viewports, tables, dashboards, progress displays, or performance over SSH/tmux. PROACTIVELY activate for: rendering performance, layout bugs, flicker, alternate screen cleanup, resize handling, terminal dashboards, frame pacing, virtual DOM/cell buffers, double buffering, dirty regions, scroll regions, long-running tasks, and high-frequency updates. Provides: render-loop rules, layout patterns, performance budgets, lifecycle cleanup checks, and failure triage.

This skill should be used when the user asks about ANSI, VT100, VT220, ECMA-48, ISO 6429, xterm control sequences, terminal capability detection, terminfo, environment variables, alternate screen protocols, SGR colors, OSC, CSI, DCS, terminal graphics, Windows virtual terminal processing, ConPTY, tmux/screen, SSH, or escape-sequence compatibility. PROACTIVELY activate for: escape sequences, terminal standards, capability negotiation, raw ANSI output, xterm protocols, alternate screen, color support, OSC 8 links, OSC 52 clipboard, Kitty/iTerm2 protocols, sixel, and cross-terminal fallback design. Provides: safe protocol rules, negotiation checklists, compatibility warnings, and reference tables.

This skill should be used when the user asks to design, create, choose, modernize, or review a terminal UI, text user interface, curses app, dashboard, wizard, REPL shell, prompt flow, or CLI with interactive screens. PROACTIVELY activate for: TUI architecture, terminal-ui design, framework selection, curses vs modern framework, full-screen vs inline UI, app state modeling, widget composition, command palette, keyboard-first workflow, terminal dashboard, interactive CLI UX, and cross-platform terminal product planning. Provides: architecture patterns, framework decision rules, UX guardrails, scope boundaries, and production readiness checklists.

This skill should be used when the user asks to test, debug, snapshot, validate, reproduce, or automate terminal UIs, curses apps, raw ANSI output, PTY-driven interactions, virtual terminal emulators, ConPTY behavior, resize tests, keyboard/mouse/paste tests, or flaky terminal snapshots. PROACTIVELY activate for: TUI testing, snapshot tests, golden files, pty integration tests, pseudo-terminal automation, ConPTY, Pexpect, node-pty, vt emulators, Textual Pilot, Ratatui snapshots, Bubble Tea tests, terminal debug logs, and CI failures. Provides: layered test strategy, deterministic snapshot rules, PTY/ConPTY patterns, debugging playbooks, and flake prevention.

Use this skill when a terminal UI freezes, hangs, shows a blank screen, corrupts terminal state, leaves raw mode stuck, stops responding to input, garbles output, breaks colors/cursor state, fails over SSH/tmux/screen, behaves differently on Windows/ConPTY, or needs deep root-cause analysis for subtle terminal failure modes. PROACTIVELY activate for: TUI freeze, terminal hang, blank TUI, raw mode stuck, stdin paused, no input events, alternate screen stuck, mouse mode stuck, bracketed paste markers, garbled terminal output, corrupted cursor/colors, partial renders, resize races, terminal state cleanup bugs, npm run tui froze, and debugging terminal gotchas. Provides: freeze/hang diagnosis, terminal recovery, startup and cleanup lifecycle checks, platform triage, and fix patterns.

This skill should be used when the user asks to make a terminal UI beautiful, polished, readable, premium, modern, branded, or visually distinctive; improve visual hierarchy, spacing, typography, borders, color palettes, charts, progress displays, icons, prompts, forms, dashboards, or framework styling. PROACTIVELY activate for: TUI visual design, aesthetic polish, layout composition, terminal typography, color systems, box drawing, chart styling, animation, glyph/icon usage, prompt styling, design critique, visual anti-patterns, and framework-specific styling guidance. Provides: terminal design principles, polish checklists, visual recipes, and accessibility-aware aesthetics.

This skill should be used when the user asks about Unicode rendering, wcwidth, grapheme clusters, emoji, combining marks, East Asian width, BiDi text, box drawing, Nerd Fonts, color themes, truecolor, 256-color fallback, NO_COLOR, contrast, accessibility, screen-reader behavior, keyboard access, reduced motion, or non-TUI fallbacks. PROACTIVELY activate for: Unicode width bugs, misaligned tables, emoji layout, ANSI-aware string measurement, color accessibility, screen-reader friendly CLI design, plain output mode, ASCII fallback, and terminal theme support. Provides: text measurement rules, color/fallback policy, accessibility checklist, and failure-mode fixes.

This skill should be used when the user asks about TUI widgets, forms, dialogs, tables, trees, charts, command palettes, split panes, scrollbars, validation, state management, Elm/MVU architecture, async message passing, configuration, packaging, distribution, terminal security, escape-sequence injection, paste sanitization, or production hardening. PROACTIVELY activate for: widget design, validation, virtual scrolling, config/keybinding files, XDG paths, packaging, secure terminal output, and release readiness. Provides: widget contracts, state patterns, config, security, and distribution checklists.

This skill should be used when deciding whether a doc should exist, where it belongs, whether something is really an ADR, or whether an architectural decision is missing from the record. PROACTIVELY activate on "should this be an ADR?", "where should I document X?", "is this architecturally significant?", "ADR vs RFC vs design doc vs runbook", "Diátaxis", "audit docs folder", "clean up the decision log", "doc drift", "doc governance", "ADR template selection", "Nygard vs MADR vs Y-statement", "missing ADR", "missing decision record", "undocumented architecture", "implicit decision", "historical documentation clutter", "inherited repo", "large legacy repo", "architecture archaeology", "code archaeology", "scan for decision records", "find decisions not captured in ADRs." Provides: doc placement diagnostic, alternatives catalog, ADR canon, folder audit procedure, and BACKFILL-ADR candidate detection.

This skill should be used to review, lint, or fix Markdown formatting in READMEs, ADRs, runbooks, how-tos, design docs, or any `.md` file. PROACTIVELY activate on "review this markdown", "lint this doc", "lint this README", "style-check this doc", "fix the formatting", "review my README", "is this markdown valid?", "fix the headings", "fix the list indentation", "ATX vs setext", "should I use a TOC?", "markdown style guide", or "Markdown linter." Provides: two-pass syntax/style review with line-referenced findings.

This skill should be used to retroactively record an architectural decision that was made in the past but never written up as an ADR — typically surfaced by a doc audit or by a user realising a long-ago shipped change should have been documented. PROACTIVELY activate on "backfill ADR", "retroactive ADR", "post-hoc ADR", "reconstruct an ADR from history", "write up a past decision", "we never documented this decision", "decision was made but no ADR exists", "we decided years ago", "back when", "before my time", or "the audit surfaced a backfill candidate." Tense rule: `adr-drafting` claims "we decided / we're deciding"; `adr-backfill` claims "we decided years ago / back when / before my time / never wrote it down." Refuses when reconstruction confidence is `low` -- routes to `open-questions.md`. Requires evidence in two independent locations and a verbatim honesty clause in the record. Provides: backfill ADR template, confidence rubric, evidence corroboration, honesty-clause boilerplate.

This skill should be used before drafting an ADR when context, components, relationships, deciders, or prior decisions are unclear. PROACTIVELY activate on "I want to write an ADR for X", "gather context for a decision", "discovery for an ADR", "ADR intake", "ADR pre-flight", "prep work before an ADR", "what's the current architecture?", or "set up a decision log." Provides: zero-hallucination Q&A, human-confirmed discovery brief, open-questions register, and five required confirmations before drafting.

Azure DevOps pipeline best practices, patterns, and industry standards. PROACTIVELY activate for: (1) authoring or reviewing Azure DevOps YAML pipelines, (2) multi-stage pipeline patterns (build, test, deploy), (3) reusable templates (steps, jobs, stages), (4) pipeline caching (Cache@2 task), (5) parallel jobs and matrix strategies, (6) deployment strategies (rolling, blue-green, canary), (7) approvals and environments, (8) variable groups, secret variables, Key Vault linkage, (9) service connections (Azure RM, GitHub, container registries), (10) pipeline versioning and pinning task major versions. Provides: YAML pattern catalog, template library structure, caching recipes, deployment strategy templates, and a pipeline-quality checklist.

Docker Compose Watch mode for hot reload during local development (2025 GA). PROACTIVELY activate for: (1) docker compose watch setup, (2) watch action types (sync, rebuild, sync+restart, sync+exec), (3) configuring path filters and ignore patterns, (4) hot-reload for Node/Python/Go/Rust apps in Compose, (5) sync-only vs rebuild trade-offs, (6) using watch with profiles, (7) debugging watch mode (verbose logs), (8) integrating watch with bind mounts, (9) Compose watch vs nodemon/air/cargo-watch. Provides: watch action reference, per-language hot-reload examples, path filter patterns, and troubleshooting steps.

Microsoft .NET Microservices Architecture Guide reference. PROACTIVELY activate for: (1) reading or applying patterns from Microsoft .NET Microservices Architecture for Containerized .NET Applications, (2) eShopOnContainers reference architecture, (3) DDD and bounded contexts, (4) integration events (RabbitMQ, Azure Service Bus), (5) API gateway patterns (Ocelot, YARP), (6) resilience patterns (Polly), (7) data patterns (CQRS, event sourcing), (8) testing strategies in microservices, (9) deployment to Kubernetes/AKS/Container Apps. Provides: chapter-by-chapter summary, code patterns from eShopOnContainers, decision matrices for sync vs async integration, and links into specific guide sections.

Azure Data Factory (ADF) CI/CD, deployment, and pipeline development. PROACTIVELY activate for: (1) ADF CI/CD setup (npm validation, ARM template export), (2) ADF ARM template deployment, (3) ADF npm build validation in CI, (4) PrePostDeploymentScript for trigger and resource cleanup, (5) ADF GitHub Actions workflows, (6) ADF Azure DevOps pipelines, (7) ADF Git integration (collaboration vs publish branch), (8) parameterizing linked services and datasets across environments, (9) ADF triggers (schedule, tumbling window, event), (10) deployment slots and blue-green for ADF. Provides: complete CI/CD YAML for GitHub Actions and Azure DevOps, PrePostDeploymentScript reference, parameterization patterns, and trigger management recipes.

ADF activity nesting rules, limits, and validation gotchas. PROACTIVELY activate for: (1) ADF activity nesting rules (ForEach inside If, Switch, Until), (2) ForEach limitations (no nested ForEach, item limits), (3) pipeline validation errors and ARM-template build failures, (4) linked service authentication issues, (5) Set Variable restrictions in ForEach, (6) Data Flow constraints (transformation limits, source/sink rules), (7) parameter and variable scope, (8) max activities per pipeline, (9) pipeline concurrency limits. Provides: nesting rule matrix, activity limit reference, validation error catalog, and refactoring patterns for unsupported nesting.