// Use when the user wants Action Runtime or 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.
| name | agently-agent-extensions |
| description | Use when the user wants Action Runtime or 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. |
Use this skill when the problem is agent-side extension rather than prompt shape, output contract, or workflow control.
auto_func, KeyWaiter, or agently-devtoolsAgently.execution_environment as an advanced framework/plugin surface, not the default app-development APIagent.enable_* component helpers before exposing core manager/provider conceptsagent.enable_python(...), agent.enable_shell(...), agent.enable_workspace(...), agent.enable_nodejs(...), and agent.enable_sqlite(...) for common Python, shell, workspace, Node.js, and SQLite accessenable_* helper desc= values as optional extra guidance by default; use desc_mode="override" only when the app intentionally replaces the default capability descriptionLiteral for finite options such as desc_mode@agent.action_func and agent.use_actions(...) as the primary action APIs; tool_func and use_tool remain compatibility aliasesfrom agently.builtins.actions import Search, Browse and agent.use_actions(Search(...)) / agent.use_actions(Browse(...)); do not invent enable_search(...) or ActionToolsagently-devtools as an optional companion package installed from PyPI, not as a required source checkoutAgently.event_centeragently-model-response or agently-triggerflow only when the scenario needs those layersAgently.execution_environment can own the dependencypip install agently-devtools is the supported public pathObservationBridgereferences/overview.mdreferences/devtools.mdUse 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.
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 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 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.
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 embeddings, vector indexing, retrieval, or retrieval-backed answers, including embedding-agent setup, Chroma-backed collections, collection add/query, and KB-to-answer flows.