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Build agent

Skill nanocodana/nanocodana/packages/cli/example-skills/build-agent

Scaffold, write, and test a brand-new NanoCodana agent (a runnable NodeAgent script, plus a skill if useful) from a plain-language descriptionFrom its SKILL.md

Install
npx -y skills add nanocodana/nanocodana --skill build-agent

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.8 KB, 675 tokens by cl100k_base, as published. Nobody here has run it

Build Agent Skill

Turn a description like "an agent that writes commit messages" into a working, tested NanoCodana agent. NanoCodana is a coding-agent framework on the Vercel AI SDK: an agent is created with NodeAgent({ ... }) and run with agent.generate({ prompt }) or agent.stream({ messages }).

Template — a minimal runnable agent

Write a file like this to the current working directory (adapt the name, model, system prompt, and tools to the request). It reads the API key from the environment — no dotenv needed, because it inherits the parent process's env.

import { NodeAgent } from '@nanocodana/nodejs'
import { createAnthropic } from '@ai-sdk/anthropic'

const model = createAnthropic({ apiKey: process.env.ANTHROPIC_API_KEY })(
  'claude-sonnet-4-5',
)

const agent = NodeAgent({
  model,
  workingDirectory: process.cwd(),
  systemPrompt: `<one paragraph describing this agent's single job>`,
  // Optional: gate or restrict tools for safety, e.g.
  //   needsApproval: ['Write', 'Edit', 'Delete'],
})

// The task comes in as the first CLI arg (fallback for a quick smoke test).
const task = process.argv[2] ?? '<a representative default task>'
const result = await agent.generate({ prompt: task })
console.log(result.text)

If OPENAI_API_KEY is set instead of ANTHROPIC_API_KEY, use createOpenAI(...)('gpt-4o') from @ai-sdk/openai.

Steps

  1. Clarify the agent's single purpose, its model, and which tools it needs. Keep the scope to one clear job.
  2. Write the agent script to <name>.mjs in the current working directory.
  3. (Optional) Write a skill at .agents/skills/<name>/SKILL.md if the agent needs reusable, loadable instructions — then have the generated agent load it (omit skills to let it auto-discover from its working dir).
  4. Test it by running it with the host shell and a representative task:
    node <name>.mjs "a representative task for this agent"
    
    (Use Bash with host: true — running Node needs the real shell.)
  5. Read the output. If the agent misbehaves, fix the script or its system prompt / skill and run it again. Don't stop until it produces a sensible result.
  6. Summarize what you built: the file(s) created, the agent's purpose, and the exact command to run it.

Rules

  • Keep generated agents minimal — only @nanocodana/nodejs plus one AI SDK provider. No extra dependencies.
  • Default to safe tools. Do not enable host Bash or destructive tools in a generated agent unless the user explicitly asks.
  • Always test before declaring done.
  • Do NOT build an agent whose job is to build other agents (no recursion).

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.