legacy/v1/skills/agently-model-setup/SKILL.md
Use when the request is already narrowed to wiring a model endpoint, env vars, settings-file-based model config, `${ENV.xxx}` placeholders, `auto_load_env=True`, or connectivity check for a model-powered feature, including local Ollama, Anthropic, dotenv-loaded DeepSeek or other OpenAI-compatible settings, plugin namespace placement, auth, request options, and minimal verification.
npx skillsauth add agentera/agently-skills agently-model-setupInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill for provider wiring and transport setup before request logic is discussed.
Agently.load_settings("yaml_file", path, auto_load_env=True)Agently.set_settings(...) or agent.set_settings(...) for inline mappings or host-owned overrides${ENV.xxx} placeholders for base URL, model, and authOpenAICompatible, prefer plugins.ModelRequester.OpenAICompatible.*; for AnthropicCompatible, prefer plugins.ModelRequester.AnthropicCompatible.*auto_load_env=True when the payload may rely on .envreferences/overview.mdtools
Use when the user wants to build, initialize, validate, optimize, or refactor a model-powered assistant, internal tool, automation, evaluator, or workflow from a business scenario or common problem statement, including project-structure refactors or starter skeletons that may separate model setup, prompt config, and orchestration, even if the request also mentions a UI, app shell, or local model service such as Ollama, and it is still unclear whether the solution should stay a single request, add supporting capabilities, or become orchestration. The user does not need to mention Agently explicitly.
data-ai
Use when the user needs Agently TaskDAG / Dynamic Task, model-generated or app-submitted DAG planning, TaskDAG validation, DynamicTaskResolver handlers, TaskDAGExecutor execution, or the Agently.create_dynamic_task compatibility facade. TaskDAG is the DAG foundation capability; Dynamic Task is the convenience facade over it and uses TriggerFlow as the execution substrate.
tools
Use when the user wants Agently runtime extension capabilities: Action Runtime, built-in action packages, legacy tool compatibility, MCP access, Execution Environment lifecycle, FastAPIHelper or streaming API exposure, auto-function helpers, KeyWaiter, or optional agently-devtools observation, evaluation, and playground integration.
development
Use when the user is shaping Agently request-side behavior: model setup, settings files, prompt management, structured output, response reuse, streaming consumption, session memory, embeddings, knowledge-base indexing, retrieval, or retrieval-backed answers within one request family.