skills/agently-output-control/SKILL.md
Use when the user wants stable structured fields, required keys, reliable machine-readable sections, or downstream-consumable output from one model request, including prompt-config-owned output contracts, `.output(...)`, field ordering, `ensure_keys`, and structured streaming.
npx skillsauth add agentera/agently-skills agently-output-controlInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when the question is what shape the model should return and how that shape should stay reliable.
The user does not need to say .output(...) or ensure_keys. Requests for stable JSON-like fields, structured reports, or machine-readable sections should route here.
.request.output when the schema is stable and shared across a request family.output(...) for machine-readable results when the schema is dynamic, exploratory, or easier to keep close to codeensure_keys when required fields must be enforced.output(...) already owns the contractensure_keysreferences/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.