> For clean Markdown content of this page, append .md to this URL. For the complete documentation index, see https://docs.nvidia.com/dynamo/llms.txt. For full content including API reference and SDK examples, see https://docs.nvidia.com/dynamo/llms-full.txt.

# Agent Skills

Dynamo ships **agent skills**: repository-native instruction files that AI coding agents such
as Claude Code, Codex, and Cursor discover and follow on their own. You don't install anything
or name a skill. Clone the repo, point your agent at it, describe what you want in plain
language, and the relevant skills activate.

```text
"Optimize this Dynamo deployment for my target SLO."
"Deploy GLM-5.2 on my 4-GPU node and confirm it serves traffic."
"My time to first token (TTFT) doubled after I raised concurrency. Diagnose it and fix the config."
```

## Deployment skills

Skills for deploying and diagnosing DynamoGraphDeployments: `deploy-dynamo-recipe`,
`troubleshoot-dynamo`, `dynamo-router-starter`, and `dynamo-interconnect-check`. Ask your
agent to deploy a model or investigate a broken deployment and these guide the work. Sources
live under
[`.agents/skills/`](https://github.com/ai-dynamo/dynamo/tree/main/.agents/skills).

## Performance optimization skills

An end-to-end optimization loop for tuning a Dynamo deployment against a stated goal. The
agent captures an immutable baseline and workload contract, deploys and smoke-tests it,
benchmarks with AIPerf, proposes one evidence-backed change at a time, reviews each proposal
before spending GPU time, and iterates within budgets you set (GPU-hours, wall clock, and failed
deploys; the agent asks if you don't state them). The final handoff includes the recommended
configuration, reproduction commands, limitations, and preserved raw evidence.

The workflow spine is documented in
[`agent-docs/guides/optimization/optimize-loop.md`](https://github.com/ai-dynamo/dynamo/blob/3e522d8ef0b4b96ae75126c1f5e7c71ae6923dcf/agent-docs/guides/optimization/optimize-loop.md),
with benchmark-validity and evidence rules under
[`agent-docs/rules/`](https://github.com/ai-dynamo/dynamo/tree/3e522d8ef0b4b96ae75126c1f5e7c71ae6923dcf/agent-docs/rules).
Harnesses that support isolated sub-agents (Codex) run the loop's roles as separate agents;
other harnesses run the same roles in one agent.

## Practical notes

* For long unattended runs, the agent will tell you when to enable your harness's goal or
  budget mode; if you enable it manually, wait until the agent's opening questions are answered.
* Benchmarking and optimization consume real GPU time. State your budgets; the agent tracks
  consumption against them and reports it.
* Never provide secrets to the agent or allow credentials into run artifacts.