agently-model-request-playbook
Agently Model Request Playbook
This skill is the scenario-routing entry point for model-request-side work in Agently. Use it when the request starts from business needs rather than one specific API. It helps choose the right request skill or skill combination. It does not replace the implementation skills themselves.
Prerequisite: Agently >= 4.0.8.5.
Scope
Use this skill for:
- deciding how a standard Agently model request should be built
- deciding how a higher-quality request should be upgraded for structure, streaming, or reuse
- deciding which parts belong to Agently and which parts belong to business logic
- deciding when to add tools, MCP, knowledge-base or RAG retrieval, session continuity, prompt config, or FastAPI exposure
- deciding when the problem has outgrown one request and should escalate to TriggerFlow
Do not use this skill for:
- direct API-level implementation details
More from agentera/agently-skills
agently-playbook
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.
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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.
40agently-model-setup
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.
40agently-langchain-to-agently
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.
38agently-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.
38agently-output-control
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.
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