skills/agently-model-response/SKILL.md
Use when the user wants to reuse one model result, read text/data/meta without re-requesting, or stream partial updates, including `get_response()`, async getters, `delta`, `instant`, and `specific`.
npx skillsauth add agentera/agently-skills agently-model-responseInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when the output contract is already chosen and the remaining issue is how the response instance should be consumed or reused.
The user does not need to say get_response(). Requests to reuse one result as text, parsed data, metadata, or progressive updates should route here.
get_response() when one request result must be consumed more than oncedelta, instant, specific, or all instead of custom stream splitting logicinstant or streaming_parse already fitsreferences/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.