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Found 67 Skills
Define list views for displaying Steedos object records in table format. List views are .listview.yml files in objects/{name}/listviews/. Covers column configuration, filter operators and conditions, date-based filters, searchable fields, sorting, mobile columns, view types (grid, calendar, kanban), crud_mode, shared settings, and filter scope.
Create, modify, and maintain Taskfiles following Task (https://taskfile.dev) best practices. Use when: (1) Creating new tasks or Taskfiles, (2) Modifying existing task definitions, (3) Adding new task includes, (4) Debugging task execution issues, (5) Questions about Taskfile syntax or patterns, (6) Running or understanding "task" commands, (7) Questions about available tasks or task namespaces. Triggers: "taskfile", "Taskfile.yaml", "task command", "task:", "create task", "add task", "task --list", "task tg:", "task inv:", "task wt:", ".taskfiles/", "how to run", "available tasks", "task syntax", "taskfile.dev" This skill covers the repository's specific conventions in .taskfiles/ and the root Taskfile.yaml.
Use when the user is shaping how one model request or request family should be instructed or templated, including prompt slots, input/instruct/info layering, mappings, recursive placeholder injection, prompt config, YAML or config-file-driven prompt behavior, and reusable prompt structure.
Create or update a Coral source spec YAML for a custom HTTP API or local dataset. Use when authoring a standalone source for `coral source add --file`, or when adapting that spec into a bundled source in the Coral repo.
Check whether AutoDeploy YAML configs were actually applied by analyzing server logs and optionally graph dumps (AD_DUMP_GRAPHS_DIR). Use when the user wants to verify config application, debug config issues, or check if AutoDeploy transforms (piecewise CUDA graph, multi-stream, sharding, fusion, etc.) were applied or fell back. Triggers on: "check config", "verify config", "ad-conf-check", "were my configs applied", "config not working", "check if piecewise is enabled", "check log for config", or any request to compare AD YAML settings against runtime behavior.
Generate Harness Infrastructure Definition YAML for deployment targets and create via MCP. Use when user says "create infrastructure", "infrastructure definition", "k8s cluster config", "deployment target", or wants to configure where workloads run.
Performance benchmarking for a deployed NVIDIA RAG Blueprint server: profiling pass + aiperf load test driven by a single YAML config. Not for accuracy / RAGAS scoring (use rag-eval) or for deploying / repairing services (use rag-blueprint).
ast-grep rule writing and usage best practices. This skill should be used when writing, reviewing, or debugging ast-grep rules for code search, linting, and transformation. Triggers on tasks involving YAML rules, pattern syntax, meta variables, constraints, or code rewriting.
Utiliza esta habilidad cuando el usuario quiera crear, modificar o analizar una nueva "Skill" (habilidad) para Antigravity. Proporciona instrucciones sobre estructura de carpetas, YAML y Markdown.
Create and use brand.yml files for consistent branding across Shiny apps and Quarto documents. Use when working with brand styling, colors, fonts, logos, or corporate identity in Shiny or Quarto projects. Covers: (1) Creating new _brand.yml files from brand guidelines, (2) Applying brand.yml to Shiny for R apps with bslib, (3) Applying brand.yml to Shiny for Python apps with ui.Theme, (4) Using brand.yml in Quarto documents, presentations, dashboards, and PDFs, (5) Modifying existing brand.yml files, (6) Troubleshooting brand integration issues. Includes complete specifications and framework-specific integration guides.
Deploys and monitors TrueFoundry batch jobs, scheduled cron jobs, and one-time tasks. Uses YAML manifests with `tfy apply`. Use when deploying jobs, scheduling cron tasks, checking job run status, or viewing execution history. For listing job applications, use `applications` skill.
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.