---

name: agent-authoring description: "Author and debug Agent Lightning agents with rollout decorators, LitAgent classes, resource injection, return contracts, and single-rollout smoke checks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Agent authoring

Use this sub-skill when the user wants to write, wrap, migrate, or debug an Agent Lightning agent.

Route by task

Request	Read/run
Create a function-based trainable agent	references/authoring-workflows.md
Fix @rollout, @llm_rollout, or @prompt_rollout errors	references/troubleshooting.md and references/api-reference.md
Write a class-based LitAgent	references/authoring-workflows.md
Validate one rollout without external services	python scripts/agent_rollout_smoke.py
Debug resources, prompt templates, or returned reward spans	references/troubleshooting.md, then route to ../tracing-and-instrumentation/SKILL.md if spans are involved

Key rules

• Every agent consumes one task input and some tunable resources.
• Function decorators support known signatures, not arbitrary callables. The most common patterns are def agent(task, prompt_template) -> float and def agent(task, llm) -> float.
• Returning a float is the simplest final reward path. Returning None is valid only when traces and rewards are emitted explicitly.
• Class-based agents subclass LitAgent[T] and implement rollout(self, task, resources, rollout) or async/validation variants.
• For local debugging, prefer OtelTracer, InMemoryLightningStore, and LitAgentRunner.step before using multi-process trainer flows.

Minimal authoring pattern

import agentlightning as agl

@agl.rollout
def my_agent(task: dict, prompt_template: agl.PromptTemplate) -> float:
    prompt = prompt_template.format(**task)
    # call tools or an LLM here
    return 1.0

Run the bundled smoke script when you need an assertion-backed minimal example:

python scripts/agent_rollout_smoke.py

Boundary

This sub-skill covers agent objects and resource injection. For store lifecycle, algorithms, and Trainer, use runner-store-training. For emitters, adapters, and trace analysis, use tracing-and-instrumentation. For CLI services and LLM proxy endpoint checks, use cli-and-services.