agentera
35 verified skills210 total stars
agently-runtime
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.
tools9
agently-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.
development9
agently-request
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.
development9
agently-dynamic-task
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.
development9
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.
tools9
agently-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.
tools8
agently-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.
development8
agently-migration
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.
data-ai8
agently-langgraph-to-triggerflow
Use when a migration is already known to stay on the LangGraph orchestration side, including stages, routing, checkpoints, interrupts, persistence, streaming, and subgraph boundaries.
testing8
agently-model-response
Use when the user wants to reuse one model result, read text/data/meta without re-requesting, or stream partial updates, including `get_response()`, async getters, `delta`, `instant`, and `specific`.
testing8
agently-knowledge-base
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.
data-ai8
agently-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, Anthropic, dotenv-loaded DeepSeek or other OpenAI-compatible settings, plugin namespace placement, auth, request options, and minimal verification.
tools8
agently-session-memory
Use when the user needs conversation continuity, memo, or restore-after-restart behavior for a request family, including session ids, chat history, request-side memory boundaries, and session-backed continuity.
data-ai8
agently-output-control
Use when the user wants stable structured fields, required keys, value-level output validation, reliable machine-readable sections, or downstream-consumable output from one model request, including prompt-config-owned output contracts, `.output(...)`, tuple ensure flags, runtime `ensure_keys`, `.validate(...)`, and structured streaming.
testing8
agently-prompt-management
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.
development8
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.
tools8
agently-migration-playbook
Use when the user wants to migrate an existing LangChain or LangGraph system and the first decision is whether the source problem is agent-side or orchestration-side.
testing8
agently-agent-extensions
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.
tools8
agently-session-memory
Use when the user needs conversation continuity, memo, or restore-after-restart behavior for a request family, including session ids, chat history, request-side memory boundaries, and session-backed continuity.
data-ai4
agently-knowledge-base
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.
data-ai4
agently-langgraph-to-triggerflow
Use when a migration is already known to stay on the LangGraph orchestration side, including stages, routing, checkpoints, interrupts, persistence, streaming, and subgraph boundaries.
testing4
agently-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.
tools4
agently-model-response
Use when the user wants to reuse one model result, read text/data/meta without re-requesting, or stream partial updates, including `get_response()`, async getters, `delta`, `instant`, and `specific`.
testing4
agently-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.
data-ai4
agently-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.
tools4
agently-prompt-management
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.
development4
agently-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.
tools4
agently-migration-playbook
Use when the user wants to migrate an existing LangChain or LangGraph system and the first decision is whether the source problem is agent-side or orchestration-side.
testing4
agently-agents-and-prompts
Agent setup, prompt layering, routing, and info injection patterns.
data-ai3
agently-agent-systems
Build complex agent and intelligent system services with Agently (Model control + TriggerFlow).
development3
agently-streaming-and-react
Streaming outputs, runtime streams, and ReAct-style tool loops.
tools3
agently-fastapi-service
FastAPI service wrappers for Agently Agent + TriggerFlow (POST, SSE, WebSocket).
development3
agently-output-control
Structured output patterns with ensure_keys, ordering, and instant streaming.
data-ai3
agently-triggerflow-orchestration
Event-driven TriggerFlow orchestration patterns and branching.
data-ai3
agently-langchain-langgraph-translation
Translate LangChain/LangGraph patterns into Agently code (model control + TriggerFlow).
development3