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Found 268 Skills
CrewAI agent design and configuration. Use when creating, configuring, or debugging crewAI agents — choosing role/goal/backstory, selecting LLMs, assigning tools, tuning max_iter/max_rpm/max_execution_time, enabling planning/code execution/delegation, setting up knowledge sources, using guardrails, or configuring agents in YAML vs code.
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.
Guide for creating properly structured YAML configuration files for MassGen. This skill should be used when agents need to create new configs for examples, case studies, testing, or demonstrating features.
Defines Steedos object data models using YAML. Objects represent database tables with fields, permissions, list views, and behaviors. Use this skill to create and configure objects, define fields, set up relationships, configure feature flags, and establish naming conventions. Modern format uses separate .field.yml, .listview.yml, .permission.yml, .button.yml files in subfolders.
Create analytics question files (.question.yml) in Steedos projects. Questions are report/chart definitions stored as YAML seed data files, based on the @steedos-labs/analytics package (Metabase engine). Covers file format, dataset_query structure (MBQL), display types, visualization_settings, result_metadata, and file naming conventions.
Score and compare images using vision LLMs as judges. YAML-defined criteria presets for 11 use cases (text-to-image, photorealism, document OCR, charts, UI, portrait, product, scientific, invoice, alt-text, artistic style). Supports OpenAI, Anthropic, Gemini, Mistral, and OpenRouter as judge providers. Keys auto-decrypted via SOPS + age.
Generate a source-backed starting `trtllm-serve --config` YAML for basic aggregate single-node PyTorch serving, aligned with checked-in TensorRT-LLM configs and deployment docs. Preserves explicit latency / balanced / throughput objectives. Excludes disaggregated, multi-node, and non-MTP speculative configs.
Generates wiring verification YAML for loom plans. Helps agents prove that features are properly integrated — commands registered, endpoints mounted, modules exported, components rendered. Use when writing truths/artifacts/wiring fields for loom plan stages.
Generates custom Claude Code subagents with specialized expertise. Activates when user wants to create a subagent, specialized agent, or task-specific AI assistant. Creates properly formatted .md files with YAML frontmatter, suggests tool restrictions and model selection, generates effective system prompts. Use when user mentions "create subagent", "new agent", "specialized agent", "task-specific agent", or wants isolated context for domain-specific work.
Automated fix skill that reads review reports and applies fixes to SPEC (Specification) documents - handles broken links, YAML structure issues, missing files, and iterative improvement
Automated SPEC generation from REQ/CTR - generates implementation-ready YAML specifications with TASKS-Ready scoring
Teaches AI assistants how to develop FlutterFlow apps using MCP tools. Use this skill when working with FlutterFlow projects, editing FF YAML, creating or inspecting pages and components, reading project configuration, or navigating FlutterFlow widget trees. It covers all 25 MCP tools for discovery, reading, editing, and settings. Triggers on: FlutterFlow, FF YAML, FF page, FF component, FF widget, FF theme, FF project.