agently-playbook
Agently Playbook
Use this skill first when the request still starts from business goals, refactor goals, product behavior, or broad model-app language.
The user does not need to say Agently, TriggerFlow, or any other framework term. Generic asks such as "build an assistant", "help me design an internal tool", or "create a validator for common problems" should still start here when the owner layer is unresolved.
Requests that also mention a UI, a web page, a desktop shell, or a local model service such as Ollama should still start here when the request is fundamentally about shaping a model-powered tool rather than only wiring one narrow capability.
Workflow
- Reduce the request into scenario and atomic goals.
- If the request is a project initialization or structure refactor, choose the owner layers, async boundary, and repo skeleton first.
- Choose the narrowest native Agently capability path.
- Name the concrete operations or primitives that should be used.
- Name the validation rule that proves the design stayed native-first.
Native-First Rules
- default to async-first guidance for service code, streaming, TriggerFlow, and any path that may overlap work or benefit from cancellation
More from agentera/agently-skills
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Use when the user is shaping how one model request or request family should be instructed or templated, including prompt slots, input/instruct/info layering, mappings, recursive placeholder injection, prompt config, YAML or config-file-driven prompt behavior, and reusable prompt structure.
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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.
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Use when a migration is already known to stay on the LangChain agent side, including agent setup, tools, structured output, retrieval, and short-term memory.
39agently-triggerflow
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.
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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.
39agently-agent-extensions
Use when the user wants tool use, MCP access, HTTP or streaming API exposure, auto-function helpers, wait-for-key behavior, or optional `agently-devtools` observation, evaluation, and playground integration through Agently-native extension surfaces rather than custom wrappers first.
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