Claude Skills by yanacuti1121

Build production Next.js apps — App Router conventions, React Server Components, server actions, data fetching strategies, caching layers, route handlers, middleware, metadata API, and parallel/intercepting routes. Use when asked about "Next.js App Router", "React Server Components", "RSC", "server actions", "use server", "use client", "Next.js caching", "fetch cache", "revalidatePath", "route handler", "Next.js middleware", "streaming UI", "Suspense boundary", "parallel routes", "Next.js met...

Alternative mã nguồn mở cho Claude Design — filesystem of design skills, 150 brand-grade DESIGN.md systems, 261 plugins. Chạy trên 21+ agent CLI qua MCP server.

NLP text normalization for cleaner LLM context. Stemming, lemmatization, stop-word removal, entity extraction, tokenization, and sentence boundary detection — all without calling an LLM. Sources: naturalnode/natural.

NVIDIA Cosmos — open platform world models cho Physical AI: robotics, autonomous vehicles. Cosmos3-Nano (16B) + Super (64B). Text/video/action I/O. Diffusers/vLLM/NIM. OpenMDW-1.1.

Instrument services for observability — structured logging, distributed tracing, metrics naming conventions, and the three-pillar model. Use when asked about "add logging", "structured logs", "distributed tracing", "trace ID", "metrics", "Prometheus", "OpenTelemetry", "why can't I debug production issues", or "correlate logs across services". Do NOT use for: SLO/alerting design — use `slo-design`. Do NOT use for: log storage/infrastructure provisioning.

Ollama local LLM — pull models, generate, chat, embeddings, REST API, Python client, structured output

ONNX Runtime cross-platform model inference. Export PyTorch/HuggingFace models to ONNX, execution provider selection (CUDA/TensorRT/CPU), graph optimization, and Node.js inference pipeline. Sources: microsoft/onnxruntime (MIT).

Talk to LLM với hands-free voice + Live2D avatar chạy local — voice interruption, real-time lip sync, cross-platform. 8.7K stars.

Use when asked to set up a self-hosted NotebookLM, build a private knowledge base from PDFs/videos/web, generate AI podcasts from documents, or run a local research assistant with multiple AI providers. Triggers on: 'open-notebook', 'self-hosted notebooklm', 'private notebooklm', 'local knowledge base', 'research notebook AI', 'AI podcast generator', 'document Q&A self-hosted', 'notebooklm alternative', 'open source notebooklm', 'knowledge base từ PDF', 'tóm tắt tài liệu riêng tư'.

Build AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, or chat applications. Covers Agent class, state management, callable RPC, Workflows integration, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

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Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

Create and review Cloudflare Durable Objects. Use when building stateful coordination (chat rooms, multiplayer games, booking systems), implementing RPC methods, SQLite storage, alarms, WebSockets, or reviewing DO code for best practices. Covers Workers integration, wrangler config, and testing with Vitest. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

Build sandboxed applications for secure code execution. Load when building AI code execution, code interpreters, CI/CD systems, interactive dev environments, or executing untrusted code. Covers Sandbox SDK lifecycle, commands, files, code interpreter, and preview URLs. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (FCP, LCP, TBT, CLS, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge.

Reviews and authors Cloudflare Workers code against production best practices. Load when writing new Workers, reviewing Worker code, configuring wrangler.jsonc, or checking for common Workers anti-patterns (streaming, floating promises, global state, secrets, bindings, observability). Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

Reviews code changes using CodeRabbit AI. Use when user asks for code review, PR feedback, code quality checks, security issues, or requests fix-review cycles.

Use when Codex is already in the attack-path-analysis phase of a security scan or the user explicitly asks to trace a security finding from source to sink and calibrate severity. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.

Use when the user asks for a deep, exhaustive, multi-pass, or variance-reducing repository-wide Codex Security scan. Run repeated independent repository-wide discovery passes with worker-specific threat models, semantically merge candidates, synthesize one canonical validation threat model, then run validation, attack-path analysis, and final reporting once. Repository-wide targets only; do not use for PRs, commits, branch diffs, working-tree diffs, or scoped paths.

Use when Codex is already in the finding-discovery phase of a security scan or the user explicitly asks to discover candidate security findings in a repository or code change. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.

Use when the user explicitly asks to fix and verify a validated or plausible security finding. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.

Use when the user asks for a security review of a pull request, commit, branch diff, working-tree patch, or other Git-backed change set.

Use when the user asks for a repository-wide or scoped-path security scan.

Use when Codex is already in the threat-modeling phase of a security scan, the user explicitly invokes $threat-model, or the user explicitly asks to create, update, or persist a repository threat model. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.

Use when Codex is already in the validation phase of a security scan or the user explicitly asks to determine whether one or more candidate security findings are valid. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.

Address actionable GitHub pull request review feedback. Use when the user wants to inspect unresolved review threads, requested changes, or inline review comments on a PR, then implement selected fixes. Use the GitHub app for PR metadata and flat comment reads, and use the bundled GraphQL script via `gh` whenever thread-level state, resolution status, or inline review context matters.

Use when a user asks to debug or fix failing GitHub PR checks that run in GitHub Actions. Use the GitHub app from this plugin for PR metadata and patch context, and use `gh` for Actions check and log inspection before implementing any approved fix.

Triage and orient GitHub repository, pull request, and issue work through the connected GitHub app. Use when the user asks for general GitHub help, wants PR or issue summaries, or needs repository context before choosing a more specific GitHub workflow.

Publish local changes to GitHub by confirming scope, committing intentionally, pushing the branch, and opening a draft PR through the GitHub app from this plugin, with `gh` used only as a fallback where connector coverage is insufficient.

Manage issues, projects & team workflows in Linear. Use when the user wants to read, create or updates tickets in Linear.

plugin-eval — evaluate-plugin

plugin-eval — evaluate-skill

Turn plugin-eval findings into a concrete rewrite brief for a Codex skill. Use when the user already evaluated a skill and now wants Codex to improve it, especially after asking what to fix first.

Design custom metric packs for plugin-eval so teams can add local evaluation rubrics that emit schema-compatible checks and metrics. Use when the user wants their own evaluation criteria or visualizations.

plugin-eval — plugin-eval

Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.

Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.

Use when doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector).

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

Use when you have a written implementation plan to execute in a separate session with review checkpoints

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

Use when completing tasks, implementing major features, or before merging to verify work meets requirements

Use when executing implementation plans with independent tasks in the current session

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Use when implementing any feature or bugfix, before writing implementation code

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback