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Found 304 Skills
CrewAI architecture decisions and project scaffolding. Use when starting a new crewAI project, choosing between LLM.call() vs Agent.kickoff() vs Crew.kickoff() vs Flow, scaffolding with 'crewai create flow', setting up YAML config (agents.yaml, tasks.yaml), wiring @CrewBase crew.py, writing Flow main.py with @start/@listen, or using {variable} interpolation.
Fetch a GitHub issue body and its messages as YAML for the current repository.
Use when creating, editing, validating, or troubleshooting a Zeabur template YAML. Use when converting docker-compose to Zeabur template. Do NOT use for deploying templates (use zeabur-template-deploy instead).
Grafana Alerting, Incident Response Management (IRM), and SLOs. Covers Grafana-managed and data source-managed alert rules, notification policies, contact points (Slack/PagerDuty/email/webhook), silences, muting, on-call scheduling, incident management workflows, and SLO configuration with burn-rate alerts. Use when configuring alerts, debugging notification routing, setting up on-call rotations, managing incidents, defining SLOs, or provisioning alerting via YAML/API.
**Opt-in DSL path** for NocoBase app building. Use ONLY when the user explicitly asks for YAML / DSL / committed-to-git / `cli push` / spec files — e.g. "use the DSL reconciler", "I want YAML I can commit", "build this as a workspaces/ project". For any other UI authoring request (new page, new block, tweak an existing screen), default to `nocobase-ui-builder` instead — this reconciler is still in active development and has rough edges that the live-UI path avoids. When the user opts in: produces/changes files under `workspaces/<project>/`, supports new pages, menus, modules, whole systems, collections, tables, sub-tables, popups, dashboards, approval workflows, recordActions, and deploys them via `cli push`.
Configure Steedos Server via environment variables and YAML settings files. Covers required env vars (MONGO_URL, ROOT_URL, B6_TRANSPORTER, B6_CACHER), steedos-config.yml project settings, default.steedos.settings.yml template with env interpolation, datasources, tenant settings, CFS file storage (local, aliyun, aws, steedosCloud), SSO/OIDC, email, SMS, push notifications, and frontend asset URLs.
MSW `.map` / `.ui` / `.gamelogic` / `.model` 에셋과 `world.yaml` 을 버전 고정된 `@choigawoon/msw-vfs-cli`(npx) 로 읽고·탐색·편집·변환하는 스킬. '맵 구조 확인', '맵 엔티티 목록', 'UI 계층', 'HP바/텍스트 조사', 'entity 값 수정', '컴포넌트 추가/삭제', '.model 값 편집', 'YAML export/import', 'world 빌드', '.map/.ui/.gamelogic/.model 파일 분석' 요청 시 사용. L1(경로 기반 VFS) + L2(entity 단위) + .model + YAML/World 모두 지원.
Genera código YAML pasteable en Power Apps Studio usando el schema pa.yaml v3. Incluye controles modernos, patrones de caché, Gallery con colecciones, y todas las lecciones aprendidas para vibe codear sin errores. Trigger: Cuando el usuario pida crear pantallas, controles o código para Power Apps en formato YAML.
Generate Harness Environment YAML for deployment targets and create via MCP. Supports PreProduction and Production types with environment variables, manifest overrides, and multi-environment setup (dev, staging, prod). Use when asked to create an environment, set up staging, configure production, define deployment targets, or manage environment overrides. Trigger phrases: create environment, deployment environment, setup dev, setup staging, setup production, environment variables, environment overrides.
This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.
Deploy prompt-based Azure AI agents from YAML definitions to Azure AI Foundry projects. Use when users want to (1) create and deploy Azure AI agents, (2) set up Azure AI infrastructure, (3) deploy AI models to Azure, or (4) test deployed agents interactively. Handles authentication, RBAC, quotas, and deployment complexities automatically.
Cross-session learning system that extracts insights from session transcripts and injects relevant past learnings at session start. Uses simple keyword matching for relevance. Complements DISCOVERIES.md/PATTERNS.md with structured YAML storage.