Data analysis, machine learning, LLM apps, and prompt design (9K skills)
Mantém coerência visual entre peças geradas por IA — seed, modelo fixo, referência de estilo e biblioteca de prompts da marca. Use quando o usuário disser "as peças não parecem da mesma campanha", "manter o estilo", "consistência", "cada imagem saiu diferente", "padronizar o visual gerado". É o problema mais difícil de geração por IA em escala.
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Consultas, inspeção de schema e mutações seguras (DML/DDL com dry-run) no PostgreSQL via MCP (@soarescbm/postgres-mcp). Use quando o usuário disser "postgres", "postgresql", "banco de dados", "tabela", "query", "SELECT", "índice", "schema", "constraint", "foreign key", "coluna", "DDL", "insert", "update", "delete", "criar tabela", ou pedir para consultar/alterar dados de um banco Postgres.
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Diagnóstico e tuning de PostgreSQL — EXPLAIN ANALYZE, índice, VACUUM, bloat, lock, replicação, WAL, PITR e particionamento. Use quando o usuário disser "postgres", "query lenta", "índice", "EXPLAIN", "vacuum", "lock", "deadlock", "replicação", "conexões", "banco lento".
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Quality gates determinísticos para PostgreSQL. Use quando o usuário pedir "quality gate PostgreSQL", "quality gate Postgres", migrations ou schema drift PostgreSQL, constraints ou índices PostgreSQL, EXPLAIN, plano de consulta ou baseline para PostgreSQL.
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Segundo cérebro persistente em Markdown — LLM Wiki no padrão Karpathy mantida como bundle Open Knowledge Format v0.2, com fontes imutáveis em raw/ e conceitos citados em wiki/. Use quando o usuário disser "segundo cérebro", "second brain", "adiciona isso ao meu brain", "salva na minha wiki", "ingere esse artigo/PDF/link", "o que minha wiki diz sobre", "o que meu brain sabe sobre", "consulta meu conhecimento", "lint da wiki", ou pedir para acumular conhecimento de fontes de forma durável. NÃO use para notas do Obsidian (pwdev-obsidian), planejamento de features (pwdev-feat) nem PRDs (pwdev-prd).
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Plan and execute database migrations, data transformations, and system migrations safely with rollback strategies and data integrity validation. Use when migrating databases, transforming data schemas, moving between database systems, implementing versioned migrations, handling data transformations, ensuring data integrity, or planning zero-downtime migrations.
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Create interactive, self-contained artifacts like mini-apps, visualizations, calculators, and tools that can be embedded or used standalone. Use when building interactive calculators, creating data visualizations, making mini web apps, building embeddable widgets, creating interactive demos, making tools for specific tasks, or generating standalone HTML/JS applications.
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Implement real-time features using Convex reactive queries that automatically update when data changes, enabling live collaboration, instant updates, and reactive UIs without manual polling. Use when building live dashboards, implementing collaborative editing, creating chat applications, showing real-time notifications, building activity feeds, implementing presence indicators, creating reactive search, or any feature requiring instant data synchronization across clients.
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Trace bugs backward through call stacks and execution flows to identify the original source of errors, invalid data, or incorrect behavior. Use when debugging complex issues, tracing error origins, investigating data corruption, following execution paths, identifying where invalid data enters the system, or finding root causes of cascading failures.
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Guide for adding new OAuth-based integrations to Fabric. Use when implementing OAuth providers like Microsoft Teams, GitHub, Google Drive, Slack, or similar services that need OAuth 2.0 authentication for user data access.
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When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do I measure this," "track conversions," "attribution," "Mixpanel," "Segment," "are my events firing," or "analytics isn't working." Use this whenever someone asks how to know if something is working or wants to measure marketing results. For A/B test measurement, see ab-test-setup.
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Cross-model benchmark for gstack skills. Runs the same prompt through Claude, GPT (via Codex CLI), and Gemini side-by-side — compares latency, tokens, cost, and optionally quality via LLM judge. Answers "which model is actually best for this skill?" with data instead of vibes. Separate from /benchmark, which measures web page performance. Use when: "benchmark models", "compare models", "which model is best for X", "cross-model comparison", "model shootout". (gstack) Voice triggers (speech-to-text aliases): "compare models", "model shootout", "which model is best".
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Self-tuning question sensitivity + developer psychographic for gstack (v1: observational). Review which AskUserQuestion prompts fire across gstack skills, set per-question preferences (never-ask / always-ask / ask-only-for-one-way), inspect the dual-track profile (what you declared vs what your behavior suggests), and enable/disable question tuning. Conversational interface — no CLI syntax required. Use when asked to "tune questions", "stop asking me that", "too many questions", "show my profile", "what questions have I been asked", "show my vibe", "developer profile", or "turn off question tuning". (gstack) Proactively suggest when the user says the same gstack question has come up before, or when they explicitly override a recommendation for the Nth time.
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Pull data from a web page. First call on a new intent prototypes the flow via $B primitives and returns JSON. Subsequent calls on a matching intent route to a codified browser-skill and return in ~200ms. Read-only — for mutating flows (form fills, clicks, submissions), use /automate. Use when asked to "scrape", "get data from", "pull", "extract from", or "what's on" a page. (gstack)
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Pre-landing PR review. Analyzes diff against the base branch for SQL safety, LLM trust boundary violations, conditional side effects, and other structural issues. Use when asked to "review this PR", "code review", "pre-landing review", or "check my diff". Proactively suggest when the user is about to merge or land code changes. (gstack)
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Build generative UI apps with OpenUI and OpenUI Lang — the token-efficient open standard for LLM-generated interfaces. Use when mentioning OpenUI, @openuidev, generative UI, streaming UI from LLMs, component libraries for AI, or replacing json-render/A2UI. Covers scaffolding, defineComponent, system prompts, the Renderer, and debugging OpenUI Lang output.
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Orchestrate the migrate-prep cascade — extract --prep → direct --prep → prototype --prep → assets prep — with confirmation gates between phases. Builds the data structure migrate consumes.
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Generates a polished, leadership-ready performance briefing with KPI tiles, executive narrative bullets, and a driver analysis — all as a print-ready HTML document. Always use this skill when someone asks for an executive summary, performance briefing, leadership readout, stakeholder update, or business review — even if they don't say "executive" explicitly. Trigger phrases include: "write a summary of last week's performance," "create a briefing for our leadership team," "produce a monthly business review," "what should I tell executives about our metrics," "generate a performance narrative," "QBR summary," "weekly business review," "board update," "stakeholder briefing," "how did we do last week," or "give me a performance snapshot." When in doubt, use this skill — it is always better to produce a polished briefing than a raw data dump.
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Comprehensive dimension analysis and reporting for CJA. Use this skill whenever the user wants to analyze one or more dimensions — including cardinality, distribution/skew, trends, anomalies, data quality errors, comparisons, and forecasting. Also trigger when someone asks "what are the top values for...", "dimension health", "explore this dimension", "dimension dashboard", "dimension statistics", "data quality check on a dimension", "dimension cardinality", "dimension trends", "dimension skew", "dimension anomalies", "compare dimensions", or any similar request to understand what's inside a CJA dimension. Produces an interactive HTML dashboard or a markdown report. Works with the CJA MCP server.
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Generate Mermaid diagrams from code, architecture, or concepts. Use when you need to visualize code flow, system architecture, data relationships, state machines, sequences, class structures, or any other diagrammable concept.
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Use when implementing or debugging ANY network request, API call, or data fetching. Covers fetch API, React Query, SWR, error handling, caching, offline support, and Expo Router data loaders (`useLoaderData`).
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Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
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Build a throwaway prototype to flesh out a design before committing to it. Routes between two branches — a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route. Use when the user wants to prototype, sanity-check a data model or state machine, mock up a UI, explore design options, or says "prototype this", "let me play with it", "try a few designs".
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Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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