Hainrixz

Audit a database schema, ORM model, or migrations for design and performance — produces two independent 0-100 scores (Design & Integrity + Performance & Scale) plus a prioritized, evidence-backed report. Read-only; never writes or migrates. Use when the user asks to audit, analyze, review, check, or score a database, schema, data model, migrations, indexes, keys, referential integrity, types, constraints, query patterns, partitioning, or connection pooling — for Postgres, MySQL, Mongo, DynamoDB, Cassandra, pgvector, ClickHouse, Neo4j, and friends.

Render a production-readiness GO/NO-GO grid for a database — runs a read-only audit, then maps the findings onto a fixed launch checklist (PITR/backups, RLS on tenant tables, FK indexes, money-as-numeric, timestamptz/UTC, connection pooling sized, migration reversibility, secrets-not-in-schema, charset/utf8mb4). Each row is PASS/WARN/FAIL/NEEDS-LIVE with the finding id, topped by a plain-language verdict. Read-only. Use when the user asks if a database is production-ready, ready to launch/ship, a go/no-go, a pre-launch or readiness checklist, or what to fix before going live.

Unified cross-paradigm anti-pattern catalog (M19) — relational and NoSQL smells that no single structural module owns cleanly: EAV / generic key-value tables, the "god table" with hundreds of columns, comma-separated lists in a column, polymorphic associations without FK, boolean-flag soup, Mongo unbounded array growth, deeply nested documents, the document fan-out / "join in app code" trap, Redis as a relied-on durable primary store. Each finding is re-homed to inherit the natural module's category. Feeds the Design & Integrity AND Performance & Scale scores depending on the smell.

Audit concurrency correctness — transaction isolation level, lost-update / read-modify-write races, queue-worker contention (SKIP LOCKED), and idempotency for key-value / document / wide-column writes. Module M14. Feeds the Performance & Scale score.

Audit connection management — serverless functions opening direct Postgres connections, transaction-mode pooler misuse (PgBouncer / Supabase / prepared statements), and pool sizing against backend max_connections. Module M15. Feeds the Performance & Scale score.

Audit data-integrity constraints — missing NOT NULL on required columns, absent CHECK constraints for domain rules, missing UNIQUE on natural keys, and the over-nullable UNIQUE trap where NULLs silently permit duplicates. Module M5. Feeds the Design & Integrity score (Constraints category).

Audit column defaults and generated columns — application-side timestamps that should be DB defaults, non-deterministic or wrong defaults, derived values that should be GENERATED ALWAYS / computed columns instead of drift-prone duplicated data, and identity/sequence defaults. Module M6. Feeds the Design & Integrity score (Tipos category, shared with M4).

Engine selection (M0) — at design/start time, recommends a database paradigm and engine for a described workload (access patterns, consistency needs, scale, team, platform), and names the runner-up with the trade-off you're accepting. M0 is a RECOMMENDATION, not a scored audit module — it emits no findings and never contributes to the Design or Performance score. Walks the decision tree in references/engine-selection-tree.md and is honest about lock-in and operability instead of fabricating benchmarks.

Audit a website or web codebase for SEO and AI-search (GEO/AEO) — produces two independent 0-100 scores (Search SEO + AI Visibility) plus a prioritized, evidence-backed report. Read-only; never writes files. Use when the user asks to audit, analyze, check, or score a site's SEO, structured data/schema, meta tags, Core Web Vitals, robots.txt, sitemaps, or llms.txt, or how it ranks in Google and is cited by AI engines (ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude).

Opt-in fixer (the /claude-seo-ai:fix command). Applies the safe, deterministic SEO/AI-search fixes from an audit to the user's code — meta viewport/charset/lang, JSON-LD, robots.txt AI directives, hreflang, sitemaps, OG/Twitter cards, image dimensions, canonical, llms.txt. Dry-run preview by default; writes only after explicit per-change confirmation. Runs only when the user invokes it — never auto-triggered.

Analyze and score only a page's AI-search visibility (GEO/AEO) — answer extractability, fact density, AI-crawler access, entity linking, and llms.txt — and report an AI Visibility score with a citability breakdown. Read-only. Use for "will AI engines cite this?", GEO/AEO, or ChatGPT/Perplexity/Google AI Overviews/Gemini optimization.

Recompute and display the two scores (Search SEO + AI Visibility) from the most recent audit's findings, without re-crawling. Use to re-show or refresh the scores after an audit, or to score a saved findings JSON file.

Audit AI crawler access and citability for a page — confirm retrieval/citation bots (OAI-SearchBot, Claude-SearchBot, PerplexityBot) are allowed and the Googlebot vs Google-Extended split is correct, classify training vs search/retrieval vs user-fetch user-agents, check the page is server-rendered enough for non-JS AI crawlers, validate llms.txt / llms-full.txt (also covers M21), and generate a choice-gated robots.txt preset. Module M14. Feeds the AI Visibility score.

Audit Core Web Vitals & page performance — measure LCP, INP, and CLS against p75 field thresholds, diagnose render-blocking resources, unoptimized images, and layout-shift sources, and produce prioritized, advisory-only remediation guidance. Module M15. Feeds the Search SEO score (heavily) and the AI Visibility score (minimally).

Fetch a page (or local files) and build the shared PageSnapshot every audit module reads — raw HTML, rendered DOM when a render MCP is available, response headers, status/redirect chain, and site artifacts (robots.txt, sitemaps, llms.txt). Decides whether JavaScript rendering is needed and records the data tier.

Audit and generate robots.txt and general crawl access for a page — verify robots.txt reachability and syntax, detect Disallow rules that block CSS/JS or important content, sanity-check crawl-delay, confirm a Sitemap directive, and assert overall crawl access for Googlebot/Bingbot. Module M1. Feeds the Search SEO score.

Continuous coach. Reads the most recent <platform>-audit-results.json files (cwd + ~/.claude-ads/history/), ranks Quick Wins and Critical Issues by impact × ease using the user's saved spend mix, surfaces the top 3 next actions, and optionally walks the user through fixing the #1 right now. Detects regressions vs the previous audit (score drops, new failures) and flags them as Priority 0. Use when user says next, what should I do, recommend, fix, suggest, what's next, qué sigue, recomienda, próximo paso.

Free-first paid advertising audit and optimization skill, focused on the 3 platforms where 95% of advertiser spend lives: Meta, Google, TikTok. Guided onboarding (/ads start) saves context once and step-by-step OAuth walkthroughs; continuous coach (/ads next) ranks Quick Wins after each audit. ~158 weighted checks with scoring, parallel agents, industry templates, and AI creative generation.

Guided first-run wizard. Walks the user from cold install to a working audit pipeline: business-context capture, per-platform connection choice (MCP / direct API / manual) with step-by-step OAuth verification, optional Zernio + Meta Developers account walkthroughs, profile persistence to ~/.claude-ads/profile.json. Re-runnable to edit settings (`/ads start edit`), reset (`/ads start reset`), or check status (`/ads start status`). Use when user says start, setup, init, onboard, first time, get started, walkthrough, connect my accounts, conectar mis cuentas, empezar.

Full multi-platform paid advertising audit with parallel subagent delegation. Analyzes Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads, and Microsoft Ads accounts. Generates health score per platform and aggregate score. Use when user says audit, full ad check, analyze my ads, account health check, or PPC audit.

Budget allocation and bidding strategy review across all ad platforms. Evaluates spend distribution, bidding strategy appropriateness, scaling readiness, and identifies campaigns to kill or scale. Uses 70/20/10 rule, 3x Kill Rule, and 20% scaling rule. Use when user says budget allocation, bidding strategy, ad spend, ROAS target, media budget, or scaling.

Competitor ad intelligence analysis across Google, Meta, LinkedIn, TikTok, Microsoft, and Apple Ads. Analyzes competitor ad copy, creative strategy, keyword targeting, estimated spend, and identifies competitive gaps and opportunities. Use when user says competitor ads, ad spy, competitive analysis, competitor PPC, or ad intelligence.

Cross-platform creative quality audit covering ad copy, video, image, and format diversity across all platforms. Detects creative fatigue, evaluates platform-native compliance, and provides production priorities. Use when user says creative audit, ad creative, creative fatigue, ad copy, ad design, or creative review.

AI image generation for paid ad creatives. Reads campaign-brief.md and brand-profile.json to produce platform-sized ad images using banana-claude. Requires banana-claude (v1.4.1+) with nanobanana-mcp configured. Triggers on: generate ads, create images, make ad creatives, generate visuals, create ad images, generate campaign images, make the images, generate from brief.

UI/UX design intelligence for web and mobile. Includes 50+ styles, 161 color palettes, 57 font pairings, 161 product types, 99 UX guidelines, and 25 chart types across 10 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui, and HTML/CSS). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, and check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, and mobile app. Elements: button, modal, navbar, sidebar, card, table, form, and chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, and flat design. Topics: color systems, accessibility, animation, layout, typography, font pairing, spacing, interaction states, shadow, and gradient. Integrations: shadcn/ui MCP for component search and examples.

Vision-driven browser automation using Midscene Bridge mode. Operates entirely from screenshots — no DOM or accessibility labels required. Can interact with all visible elements on screen regardless of technology stack. This mode connects to the user's desktop Chrome browser via the Midscene Chrome Extension, preserving cookies, sessions, and login state. Use this skill when the user wants to: - Browse, navigate, or open web pages in the user's own Chrome browser - Interact with pages that require login sessions, cookies, or existing browser state - Scrape, extract, or collect data from websites using the user's real browser - Fill out forms, click buttons, or interact with web elements - Verify, validate, or test frontend UI behavior - Take screenshots of web pages - Automate multi-step web workflows - Check website content or appearance Powered by Midscene.js (https://midscenejs.com)

Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema-markup. For AI search optimization, see ai-seo.

shadcn/ui component library for React with Tailwind CSS - copy-paste accessible components with full code ownership

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

Live browser interaction via Playwright MCP — navigate pages, click buttons, fill forms, take screenshots.

Professional React animation skill for creating Awwwards/FWA-level animations using GSAP (useGSAP), Motion (Framer Motion), Anime.js, and Lenis. Use when building premium scroll experiences, custom cursors, page transitions, text animations, parallax effects, micro-interactions, or any animation that needs to be 60fps and award-worthy. Triggers on requests for smooth scroll, ScrollTrigger, magnetic effects, reveal animations, horizontal scroll, pin sections, stagger effects, useScroll, useTransform, integration with Three.js/WebGL, algorithmic art, mathematical art, generative art, fractals, L-systems, flow fields, strange attractors, sacred geometry, geometric puzzles, Dudeney dissections, tangram, tessellations, Penrose tiles, kinetic typography, glitch effects, text explosion, morphing text, circular text, brutalist design, minimalist animation, neo-brutalism, or design philosophy mixing. React-first approach with proper cleanup and hooks.

Explainer video production guide: scripting, voiceover, visuals, and assembly. Covers script formulas, pacing rules, scene planning, and multi-tool pipelines. Use for: product demos, how-it-works videos, onboarding videos, social explainers. Triggers: explainer video, how to make explainer, product video, demo video, video production, video script, animated explainer, product demo video, tutorial video, onboarding video, walkthrough video, video pipeline

Advanced motion designer with decades of After Effects and motion graphics experience, specialized in creating engaging video specifications for Remotion. Use when creating video specs, planning motion graphics, designing animations, or when asked to "create a video", "design motion graphics", "plan video content", or "spec out a video". Produces detailed scene-by-scene specifications with timing, audio, sound effects, and animation descriptions.

Render videos from React/Remotion component code via inference.sh. Pass TSX code, get MP4. Supports all Remotion APIs: useCurrentFrame, useVideoConfig, spring, interpolate, AbsoluteFill, Sequence. Configurable resolution, FPS, duration, codec. Use for: programmatic video generation, animated graphics, motion design, data-driven videos, React animations to video. Triggers: remotion, render video from code, tsx to video, react video, programmatic video, remotion render, code to video, animated video, motion graphics code, react animation video

Recommends the single best payment provider for the user's project after a short interactive wizard. Used when the user wants to integrate Stripe, Mercado Pago, Wompi, Lemon Squeezy, or compare payment options based on country, currency, recurrence, local payment methods (PIX, OXXO, PSE, Bizum), and product type. Triggered by /pagokit:start. Computes fees in real money for a typical transaction; never exposes numeric scores; always discloses last_verified_at and applicable Phase-1 limitations. Bilingual ES/EN/PT, language inferred from the user's first prompt.

Audits an existing PagoKit integration in the current project. Checks .gitignore covers .env, that env vars are present and use test-key prefixes, that the webhook secret looks valid, that the webhook handler verifies signatures, that the minimum events for the integrated provider are routed, and that PAGOKIT_INTEGRATION.md exists. Used by /pagokit:doctor. Read-only — never writes files.

Composes a full vertical payment integration from canonical templates. Called by integration-specialist subagent after the user selects a provider. Loads templates by (provider, stack, ORM, deploy target, billing_mode, frontend_style, use_cases) and emits a coherent file plan that gets written to the user's project. Always cites webhook-verifier and SECURITY_RULES. Phase 1 supports stripe, mercadopago, wompi, lemonsqueezy on Next.js App Router and Express; Phase 2 adds more.

Detects a project's technology stack, framework, deploy target, ORM, primary language, and active payment use cases by reading package.json/pyproject.toml/composer.json/Gemfile, schema files, route files, and deploy configs. Use this skill at the start of any PagoKit flow to ground recommendations in the real project context. Emits a structured detection result for payment-advisor and integration-specialist to consume.

Reference for cryptographic verification of payment webhooks. Cited by integration-specialist whenever it generates a webhook handler. Documents per-provider signature algorithms, timestamp tolerances, replay-protection strategies, raw-body capture per stack, and the minimum set of events each handler must route. Always use this skill (instead of recalling from training data) — webhook verification is where the integration goes silently wrong.

ES — Critica a fondo una idea, plan, pitch o proyecto entero sin validar ni adular: asume que va a fracasar y lo demuestra por ocho ángulos (premisas, mercado, competencia, viabilidad, números, ejecución, pre-mortem y punto ciego), investiga fracasos reales cuando hay herramientas, y entrega un veredicto franco con la lista priorizada de qué arreglar. Activar con /abogado-del-diablo, "hazme pedazos esto", "critica mi idea sin filtros", "dime por qué va a fallar", "destruye este plan", "segunda opinión brutal", "pre-mortem". EN — Harshly critiques an idea, plan, pitch, or whole project without validating; assumes it will fail and proves it across eight angles, then returns a blunt verdict with a prioritized fix list. Trigger on "devil's advocate", "red team this", "tear this apart", "why will this fail", "brutal second opinion", "pre-mortem".

ES — Critica a fondo una idea, plan, pitch o proyecto entero sin validar ni adular: asume que va a fracasar y lo demuestra por ocho ángulos (premisas, mercado, competencia, viabilidad, números, ejecución, pre-mortem y punto ciego), investiga fracasos reales cuando hay herramientas, y entrega un veredicto franco con la lista priorizada de qué arreglar. Activar con /abogado-del-diablo, "hazme pedazos esto", "critica mi idea sin filtros", "dime por qué va a fallar", "destruye este plan", "segunda opinión brutal", "pre-mortem". EN — Harshly critiques an idea, plan, pitch, or whole project without validating; assumes it will fail and proves it across eight angles, then returns a blunt verdict with a prioritized fix list. Trigger on "devil's advocate", "red team this", "tear this apart", "why will this fail", "brutal second opinion", "pre-mortem".

EN — Review the current conversation and surface reusable learnings across four categories (memory, lesson, skill, project-doc). Generate a numbered candidate list first; only write to disk after the user confirms. Trigger when the user types /aprende, /learn, "reflect on this", "save what we learned", "remember this for next time", or after correcting the agent on a recurring mistake. ES — Revisa la conversación actual y extrae aprendizajes reusables en cuatro categorías (memoria, lección, skill, project-doc). Genera primero una lista numerada de candidatos; solo escribe a disco después de la confirmación del usuario. Activa cuando el usuario escriba /aprende, /learn, "reflexiona sobre esto", "guarda lo que aprendimos", "recordar esto para la próxima", o después de corregir al agente sobre un error recurrente.

English alias for /aprende. Runs the same five-pass workflow to surface reusable learnings from the current conversation across memory, lesson, skill, and project-doc categories — with confirmation before any write. Alias en inglés para /aprende.

Comprehensive cybersecurity analysis for any local project. Scans for dependency vulnerabilities (SCA), code security patterns (SAST), leaked secrets, authentication/authorization flaws, cryptographic weaknesses, misconfigurations, supply chain risks, and CI/CD security. Covers all OWASP 2025 Top 10 and CWE Top 25. Generates a prioritized report with remediation guidance. Use when the user says "security audit", "vulnerability scan", "check for security issues", "find vulnerabilities", "security review", "pentest", "security check", or invokes /cyber-neo.

Multi-agent investment research and analysis system by Tododeia. Use when the user wants market analysis, investment research, or a summary of current opportunities across crypto, stocks, forex, and commodities. Spawns 5 specialized research agents (4 sector + 1 strategy), adapts to user risk profile, tracks historical accuracy, and generates a branded interactive HTML report served locally. Educational analysis only — not financial advice. Trigger phrases: "investment analysis", "market research", "analyze markets", "investment opportunities", "what should I invest in", "market report", "tododeia", "investment advice", "portfolio recommendations", "run tododeia", "daily market analysis", "weekly report", "análisis de inversión", "analizar mercados", "reporte de mercado", "oportunidades de inversión".

Detects and eliminates 40 AI writing tells across vocabulary, structure, formatting, content inflation, and communication artifacts. Includes personality injection, 6-dimension scoring, and a self-audit loop. Use when editing or reviewing any text to make it sound unmistakably human.

Deploys any web app, API, or agent to the internet with a strict pre-deploy audit and a preview → health-check → prod flow. Detects Next, Vite, Astro, Remix, Nuxt, SvelteKit, FastAPI, Flask, Express, MCP server, Claude Agent SDK script, Python worker, Dockerized, and static projects; routes to Vercel, Railway, Docker+SSH VPS, or cloudflared tunnel. Also supports a first-class "run locally" mode. At the start of each cloud deploy the skill asks the user whether to run full-auto (audit → preview → prod in one sequence) or step-by-step (confirm each phase); the user can skip that question by saying "auto" or "step by step" up front. Use when the user says "/all-deploy", "/deploy", "/despliega", "deploy this", "ship this", "push to prod", "get this online", "deploy my agent", "deploy my FastAPI", "run it locally", "corre esto localmente", "deploy auto", "deploy step by step", "paso a paso", "how do I put this on the internet", or otherwise asks to publish a project to a URL or run it locally.