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, `${ENV.xxx}` placeholders, `auto_load_env=True`, or connectivity check for a model-powered feature, including local Ollama, dotenv-loaded DeepSeek or other OpenAI-compatible settings, plugin namespace placement, auth, request options, and minimal verification.
npx skillsauth add agentera/agently-skills agently-model-setupInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill for provider wiring and transport setup before request logic is discussed.
Agently.load_settings("yaml_file", path, auto_load_env=True)Agently.set_settings(...) or agent.set_settings(...) for inline mappings or host-owned overrides${ENV.xxx} placeholders for base URL, model, and authOpenAICompatible, prefer plugins.ModelRequester.OpenAICompatible.*auto_load_env=True when the payload may rely on .envreferences/overview.mddevelopment
Use when the user needs Agently Dynamic Task, model-generated or app-submitted DAG planning, TaskDAG validation, DynamicTaskResolver handlers, or TaskDAGExecutor execution through Agently.create_dynamic_task. Dynamic Task is a first-class Agently API that uses TriggerFlow as an 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.
development
Use when the user needs workflow orchestration such as branching, concurrency, approvals, waiting and resume, runtime stream, restart-safe execution, mixed sync/async function or module orchestration, event-driven fan-out, process-clarity refactors that make stages explicit, performance-oriented refactors that collapse split requests, or workflow definitions and chunk-level runtime metadata that must stay visible for debugging and visualization. The user does not need to say TriggerFlow explicitly.