agently-prompt-config-files
Agently Prompt Config Files
This skill covers YAML and JSON prompt config in Agently. It focuses on prompt templates as data assets: loading prompt config from files or strings, routing config into agent/request prompt layers, applying mappings, selecting nested prompt packs, executing .alias, and exporting current prompt state back to YAML or JSON. It does not cover session serialization, TriggerFlow config/export, provider setup, or response consumption.
Prerequisite: Agently >= 4.0.8.5.
Scope
Use this skill for:
load_yaml_prompt(...)andload_json_prompt(...)get_yaml_prompt(...)andget_json_prompt(...).agent,.request, and.alias- top-level
$keyagent shorthand and top-level request-key shorthand mappingsfor placeholder substitution in config keys and valuesprompt_key_pathfor selecting one prompt config from a larger YAML or JSON document- prompt-template roundtrip between code-built prompt state and exported config text
- externalizing prompt structure out of business code
More from agentera/agently-skills
agently-playbook
Use when the user wants to build, initialize, validate, optimize, or refactor a model-powered assistant, internal tool, automation, evaluator, or workflow from a business scenario or common problem statement, including project-structure refactors or starter skeletons that may separate model setup, prompt config, and orchestration, even if the request also mentions a UI, app shell, or local model service such as Ollama, and it is still unclear whether the solution should stay a single request, add supporting capabilities, or become orchestration. The user does not need to mention Agently explicitly.
40agently-prompt-management
Use when the user is shaping how one model request or request family should be instructed or templated, including prompt slots, input/instruct/info layering, mappings, recursive placeholder injection, prompt config, YAML or config-file-driven prompt behavior, and reusable prompt structure.
40agently-model-setup
Use when the request is already narrowed to wiring a model endpoint, env vars, settings-file-based model config, `${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.
38