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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(...) and load_json_prompt(...)
• get_yaml_prompt(...) and get_json_prompt(...)
• .agent, .request, and .alias
• top-level $key agent shorthand and top-level request-key shorthand
• mappings for placeholder substitution in config keys and values
• prompt_key_path for 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

Do not use this skill for:

• regular prompt composition written directly in Python code
• session export / restore, session ids, or session-backed memory
• TriggerFlow flow config, blueprint export, or execution-state restore
• provider setup, auth, proxy, or request transport configuration
• .output(...) response parsing or streaming-consumption decisions

Workflow

1. If the task is about how a prompt config file is structured, read references/structure-and-routing.md.
2. If the task is about file-vs-string loading, mappings, JSON5 behavior, or prompt_key_path, read references/loading-mappings-and-key-paths.md.
3. If the task is about .alias, exporting config, or roundtripping prompt state, read references/alias-and-roundtrip.md.
4. If the behavior still looks wrong, use references/troubleshooting.md.

Core Mental Model

Prompt config in Agently is a serialized form of prompt state, not a new prompt system.

• .agent maps to persistent agent prompt state
• .request maps to per-request prompt state
• top-level $key is shorthand for writing into agent prompt state
• top-level ordinary keys are shorthand for writing into request prompt state
• .alias is an imperative hook that executes agent methods during load

That means this feature is best used when prompt structure should live outside business code, be versioned as data, or be shared across services and environments.

Selection Rules

• prompt template should live in YAML or JSON instead of Python -> use prompt config files
• one config file contains multiple reusable prompt packs -> use prompt_key_path
• placeholders should be filled at load time -> use mappings
• prompt state should be exported and restored elsewhere -> use get_yaml_prompt() / get_json_prompt()
• config should drive simple prompt-building methods -> use .alias
• long-lived conversation memory or state snapshot -> use agently-session-memo, not this skill
• flow-definition export -> use agently-triggerflow-config, not this skill
• TriggerFlow runtime restore -> use agently-triggerflow-execution-state, not this skill

Key Limits

• mappings are applied when prompt keys and values are written into prompt state
• .alias arguments are executed as written; they do not receive the same load-time ${...} mapping substitution
• exported prompt config serializes current .agent and .request state
• export does not preserve .alias, comments, prompt_key_path, or the original unresolved template text

References

• references/source-map.md
• references/structure-and-routing.md
• references/loading-mappings-and-key-paths.md
• references/alias-and-roundtrip.md
• references/troubleshooting.md

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1

Repository

agentera/agently-skills

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1 day ago

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