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Found 281 Skills
This skill should be used when users encounter cspell unknown word warnings, spelling errors from cspell diagnostics, or CI/linting failures on unrecognized words. Also applies when users ask to add words to the cspell dictionary, suppress or ignore cspell warnings, choose between cspell:words and cspell:ignore directives, or bootstrap cspell config in a new project
Creates and scaffolds a new Spring Boot project (3.x or 4.x) by downloading from Spring Initializr, generating package structure (DDD or Layered architecture), configuring JPA, SpringDoc OpenAPI, and Docker Compose services (PostgreSQL, Redis, MongoDB). Use when creating a new Java Spring Boot project from scratch, bootstrapping a microservice, or initializing a backend application.
Scaffolds a production-ready Next.js turborepo with TypeScript, Tailwind CSS, shadcn CLI, Blode UI components from ui.blode.co, blode-icons-react, Biome, Ultracite, and Vercel deployment. Use when creating a new Next.js app, bootstrapping a turborepo, scaffolding a web project, starting a new website, or asking "create a Next.js project."
Agent onboarding automation for AIBTC first-hour setup. Use when a new or existing agent needs a structured bootstrap flow: wallet readiness, AIBTC registration check, heartbeat health checks/check-in, safe skill-pack installs, and a one-command doctor summary with next actions.
Community registry of agent configurations for the AIBTC platform — browse reference configs for arc0btc, spark0btc, iris0btc, loom0btc, and forge0btc, or copy the template to bootstrap a new agent.
Personal wiki at ~/.ultrabrain/ that accumulates knowledge across sessions using an LLM-maintained-wiki pattern. Use when the user asks factual, technical, or decision-oriented questions that may have been previously captured (check index.md before answering), or explicitly asks to capture/記下來/save session content, ingest/整合 raw entries into the wiki, lint/檢查 the vault, or bootstrap a new vault. Skip for small talk, current-file questions, or code-execution requests.
Adopt Prisma Next into a new project, onto an existing database, or as the first move after a bootstrap tool dropped you into a scaffold. Use for "what can I do with Prisma Next", "what can I do next with Prisma", "where do I start", "what should I do first", "just ran createprisma", "createprisma", "npx createprisma", "npx create-prisma", "first steps", "first query", "I have a scaffolded Prisma Next project what now"; for `pnpm dlx prisma-next init` greenfield setup; and for `prisma-next contract infer` + `db sign` against an existing database. Also covers the connect-write-read first-arc orientation, the day-to-day commands (`contract emit`, `db init`, `db update`, `migration plan`, `migrate`, `db schema`, `db verify`), and routing to `prisma-next-contract` / `prisma-next-queries` / `prisma-next-runtime` for the next move. Flags: --target, --authoring, --schema-path, --probe-db, --output.
Bootstrap a custom carrier board by forking carrier files and scaffolding a DT overlay from the reference devkit. Use after jetson-init-source; not for module-level or kernel-DTB changes.
Initialize Spec-Driven Development context in any project. Detects stack, conventions, and bootstraps the active persistence backend. Trigger: When user wants to initialize SDD in a project, or says "sdd init", "iniciar sdd", "openspec init".
Set up `release-please` for automated releases in a repository. Use this skill when the user mentions release-please, `googleapis/release-please-action`, release PRs, conventional commits, `release-please-config.json`, `.release-please-manifest.json`, GitHub Actions release automation, or wants to bootstrap or debug release-please in a new or existing repo.
Guide developers through creating MCP apps. Covers the full lifecycle: brainstorming ideas against UX guidelines, bootstrapping projects, implementing tools/widgets, debugging, running dev servers, deploying and connecting apps to ChatGPT. Use when a user wants to create or update a MCP app, MCP server or use the Skybridge framework.
Fine-tune models on your data to maximize quality and cut costs. Use when prompt optimization hit a ceiling, you need domain specialization, you want cheaper models to match expensive ones, you heard "fine-tuning will make us AI-native", you have 500+ training examples, or you need to train on proprietary data. Covers DSPy BootstrapFinetune, BetterTogether, model distillation, and when to fine-tune vs optimize prompts.