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Agently-Skills
Agently-Skills contient 6 skills collectées depuis AgentEra, avec une couverture métier par dépôt et des pages de détail sur le site.
Skills dans ce dépôt
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.
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.
Use when the user needs Agently TaskDAG / Dynamic Task, model-generated or app-submitted DAG planning, TaskDAG validation, TaskDAGResolver 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.
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.
Use when the user wants Agently runtime extension capabilities: Action Runtime, built-in action packages, legacy tool compatibility, MCP access, ExecutionResource lifecycle, FastAPIHelper or streaming API exposure, auto-function helpers, KeyWaiter, or optional agently-devtools observation, evaluation, and playground integration.
Use when the user wants to migrate an existing LangChain, LangGraph, LlamaIndex, CrewAI, or similar system into Agently, including choosing whether the source belongs to request/agent-side Agently behavior or TriggerFlow orchestration.