Loading...
Loading...
Found 6,488 Skills
Pick and install beUI (@beui) animated React components from the shadcn registry. Use when building motion UI, agent/chat interfaces, toasts, docks, bottom sheets, drawers, popovers, sliders, loaders, 404 pages, or any beui.dev component. Maps user intent to exact @beui install slugs instead of inventing custom widgets.
The craft of turning a single quote, line, or stat into a designed typographic graphic — quote cards, pull-quotes, text-on-image, stat cards, typography posts. Use when someone wants a quote card, a text graphic, a pull-quote, a stat card, or to turn a strong line into a shareable image. The design amplifies a strong line; it can't create one. Uses the QUOTE framework. Reads brand-profile + design-and-templates + voice-builder first, and pulls the line from hook-writer, storytelling, or data-and-original-research (a stat). The agent picks the line and specs the card; a design tool renders; the human approves; WoopSocial publishes the image (it does not generate media). NEVER fabricates a quote, attributes an invented line to a real person, or strips context; verifies wording + source; consent + likeness. Distinct from infographic-and-data-viz, design-and-templates, caption-writer, and social-proof-and-testimonials.
Create a visual identity brief for a brand — logo direction, color palette, typography, imagery style, and design system foundations. Use when the user says "visual identity", "brand identity", "logo brief", "logo direction", "design brief", "brand design", "color palette for my brand", "typography for my brand", "visual language", "design system", "brand look and feel", "what should my brand look like", or is briefing a designer or design agency. Also use when the user has a brand strategy and wants to translate it into visual design direction.
Code comment hygiene for AI coding agents: remove generic AI-slop comments, keep the valuable ones, never touch the code.
Simplifies, polishes, and declutters code without changing behavior. Use when asked to simplify, clean up, refactor, declutter, remove dead code or AI slop, or improve readability. For analysis-only reports without code changes, use code-simplicity-reviewer agent.
Migrate browser-use (Python) browser-automation scripts to Stagehand v3 (TypeScript) on Browserbase. Use when the user wants to convert, port, rewrite, or migrate a browser-use Agent script to Stagehand, map browser-use features/APIs to Stagehand primitives (act/extract/observe/agent), or move agentic browser automation onto Browserbase with more determinism. Triggers on "browser-use", "browser_use", or "Agent(task=...)".
Use when the user mentions Firetiger or wants to work with their observability data — setting up Firetiger, instrumenting an app with OpenTelemetry, querying traces/logs/metrics with SQL, investigating an incident, monitoring a PR or deployment, or creating a monitoring agent. Always use this skill when the user says "Firetiger", even for simple asks — it routes to the specialized skill that carries the critical gotchas (Basic-auth ingest, DuckDB SQL over per-service tables, the @firetiger comment flow) that prevent common mistakes.
12 pre-dev workflow skills + 4 research agents organized into Small Track (4 gates, <2 days) and Large Track (9 gates, 2+ days) for systematic feature planning with research-first approach.
Qianfan Deep Research Agent for complex research tasks. Combines information retrieval, multi-source analysis, content synthesis, and report generation. Use when user needs in-depth research, analysis reports, or comprehensive investigation on complex topics.
Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare. Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.
Turn a vague bug report into a VERIFIED minimal reproduction and then a failing regression test, agent-driven end to end. Covers extracting the implicit repro from a thin report (env, build, steps, data), the reproduce-minimize-isolate-capture loop, git bisect to find the introducing commit, building a deterministic minimal repro (fixed seeds, frozen time, stubbed network), writing the failing regression test BEFORE the fix (red) and confirming the fix flips it green, and writing repro evidence back into the ticket. Distinguishes flaky-not-reproducible from environment-specific. Use when: "reproduce this bug," "minimal reproduction," "repro steps," "find the commit that broke it," "git bisect," "make the repro deterministic," "write a failing test for this bug," "regression test for a defect," "can't reproduce this bug." Not for: Classifying/deduplicating/severity-routing existing failures without reproducing them — that is ai-bug-triage. Generating tests from specs rather than from a defect — that is ai-test-generation. Related: ai-bug-triage, ai-test-generation, test-reliability, systematic-debugging, qa-project-context.
Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.