> ## Documentation Index > Fetch the complete documentation index at: https://agno-v2-codex-homepage-20260719-015142.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Build with Coding Agents > Use bundled skills to set up, build, evaluate, and maintain agents inside an AgentOS Starter. Run the setup skill inside a freshly cloned [Starter](/deploy/introduction): ```text theme={null} /setup-platform ``` The coding agent configures the local environment, starts AgentOS with Docker, verifies the MCP endpoint, builds your first agent, and helps you connect the AgentOS UI. Each Starter gives coding agents access to the project, live AgentOS API, evals, traces in PostgreSQL, and container logs. They can use that context to diagnose behavior, edit the implementation, and verify the result. ## Skills The skills live in `.agents/skills/`. Claude Code discovers them through the committed `.claude/skills` symlink. Codex and Cursor can use the same folder. Invoke a skill by name or describe the task and let the coding agent select it. | Skill | What it does | Use when | | --------------------- | ------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------- | | `/setup-platform` | Configures a fresh clone, starts AgentOS locally, verifies MCP, and builds the first agent. | Starting with a new Starter | | `/create-new-agent` | Scaffolds an agent, registers it in `app/main.py`, and smoke-tests it live. | Adding a new agent | | `/extend-agent` | Adds a tool or capability, refines instructions, or fixes a known bug. | The required change is already clear | | `/improve-agent` | Derives probes from the agent's `INSTRUCTIONS`, judges responses, makes focused edits, and reruns affected probes. | Improving behavior through simulations and probes | | `/eval-and-improve` | Runs the eval suite, classifies failures, proposes scoped fixes, and reruns affected cases. | Resolving an eval regression | | `/review-and-improve` | Checks documentation, code, and configuration together and fixes straightforward inconsistencies. | Maintaining the project after changes | ## Connect the live platform Coding agents can edit the project and call its live agents over MCP. Register the local AgentOS with supported clients: ```bash theme={null} uvx agno connect ``` The command detects Claude Code, Claude Desktop, Codex, and Cursor and registers `http://localhost:8000/mcp`. Your coding agent can then call `run_agent`, `run_team`, and `run_workflow` against the running platform. For a deployed platform, pass its URL: ```bash theme={null} uvx agno connect --url https:// ``` | AgentOS configuration | What `agno connect` does | | ------------------------------------------------ | ---------------------------------------------------------- | | MCP without authentication | Writes a tokenless client entry | | Token-protected AgentOS with an admin credential | Mints a scoped service-account token for each client | | MCP OAuth | Writes the connection and prints the client sign-in step | | Headless client using MCP OAuth | Use `--pat` with an admin credential or existing admin PAT | See [`agno connect`](/cli/connect) for client configuration, credential requirements, and conflict handling. ## Evals The eval suite lives in `evals/`. Set up the host environment once: ```bash theme={null} ./scripts/venv_setup.sh source .venv/bin/activate ``` Run the suite by scope: ```bash theme={null} python -m evals --tag smoke # fast checks of the self-driving surfaces python -m evals --tag release # broader pre-release confidence python -m evals --name # one case while iterating python -m evals -v # stream the full run with rich panels ``` Cases use [AgentAsJudgeEval](/evals/agent-as-judge/overview) for model-judged behavior and [ReliabilityEval](/evals/reliability/overview) for tool-call assertions. Results are stored in PostgreSQL and appear in the [AgentOS UI](https://os.agno.com) beside sessions and traces. If a case fails, run `/eval-and-improve`. ## Agent patterns The bundled agents demonstrate three patterns: | Pattern | Example | When to use | | ---------------- | ---------------------------- | --------------------------------------------------------------------------------------------- | | Direct tools | `agents/web_search.py` | The agent needs fine-grained control over each tool call. | | Context provider | `agents/platform_manager.py` | Several tools can sit behind one `query_` interface managed by a sub-agent. | | Studio builder | `agents/agent_builder.py` | Users create and refine components from chat with confirmation gates for destructive actions. | ## Teams and workflows | Pattern | When to use | | -------------------------------- | ----------------------------------------------------------------------- | | [Teams](/teams/overview) | Route to specialists, coordinate parallel work, and synthesize results. | | [Workflows](/workflows/overview) | Run a process with defined, repeatable control flow. | Use agents for open questions, teams for routing, and workflows for processes. ## Scheduled tasks The scheduler is enabled by default. Two reference workflows are registered: | Workflow | What it does | Default | | ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------- | | Deployment Check | Runs a daily readiness report for the database, authentication, scheduler, MCP, and Slack configuration. It uses fixed checks with no model calls. | On. Disable with `ENABLE_DEPLOY_CHECK=False`. | | Run Evals | Runs the smoke-tag eval suite daily and uses model calls. | Off. Enable with `ENABLE_SCHEDULED_EVALS=True`. | See [Scheduler](/agent-os/scheduler/overview) for the cron API.