agent-builder/agent_builder/skills/agent-quality-review/SKILL.md

name, description

name	description
agent-quality-review	Review a generated a2a-pack agent before sandbox testing or deploy. Use to catch broken cards, missing schemas, fake tools, unwired DeepAgents skills, missing runtime mirrors, unsafe IO, and stale live Agent Card schemas.

Agent Quality Review

Before test_agent_in_sandbox and before deploy, review the generated project as source code, not just as text.

Required Files

Every generated project needs:

• agent.py
• a2a.yaml
• requirements.txt
• Optional skills/<skill-name>/SKILL.md bundles for nontrivial DeepAgents behavior
• Optional frontend/ source when the agent ships a packed app

Run list_agent_files and read the files that matter. If the project was deployed before, use sync_agent_workspace_from_repo before editing when the builder needs to continue from the current managed repo source.

Agent.py Review

Check these items:

• Public @skill methods are async and have RunContext[...] plus typed JSON arguments.
• The public schema is small and stable. No unbounded command strings, hidden prompt fragments, or provider credentials as public inputs.
• The implementation reads ctx.llm for both CALLER_PROVIDED and PLATFORM LLM provisioning. PLATFORM_OR_CALLER_PROVIDED is acceptable only for trusted platform/meta agents and must also read ctx.llm.
• Generated/user agents should default to llm_provisioning = LLMProvisioning.CALLER_PROVIDED and Pricing(..., caller_pays_llm=True, ...). If the user explicitly wants platform-paid LLM usage, LLMProvisioning.PLATFORM is acceptable only if the code still uses ctx.llm and never reads LiteLLM/provider keys directly.
• Any use of DeepAgents file tools passes backend=ctx.workspace_backend().
• Any project skills are seeded into the backend and passed with skills=skill_sources or None.
• Custom subagents that need project skills include their own skills field.
• Deterministic tools do exact work; no fake canned-response tools.
• File-producing skills call ctx.write_artifact and ctx.emit_artifact.
• Commands that create downloadable outputs run through ctx.workspace_shell or ctx.workspace_python; /workspace writes persist directly and other changed sandbox rootfs files are mirrored under outputs/rootfs-captures/.... Plain subprocesses in the agent container are not durable workspace writes.
• External calls, secrets, workspace access, pricing, and resources are declared on the class.

A2A YAML Review

Mirror deployment-only details in a2a.yaml because the platform reads it before importing user code:

name: research-agent
version: 0.1.0
entrypoint: agent:ResearchAgent
expose:
  public: true
runtime:
  apt_packages: [ffmpeg]
  resources:
    cpu: "2"
    memory: 2Gi
    max_runtime_seconds: 900

Use runtime.apt_packages only for Debian system binaries. Python packages belong in requirements.txt.

If a packed frontend is declared, verify the manifest and files line up:

frontend:
  path: frontend
  build: npm run build
  dist: dist
  mount: /app
  auth: inherit

For React/Vite frontends, expect frontend/package.json, frontend/vite.config.js, frontend/src/App.jsx, and frontend/src/a2a.js. For static frontends, expect frontend/dist/index.html. The browser code must not contain platform secrets, provider keys, LiteLLM keys, hard-coded deployment URLs, or private stack details. It should load /app/config.json, use /app/a2a-client.js or generated config endpoints, and call only the public @skill schemas.

Sandbox And Live Card

Always run test_agent_in_sandbox(name) before deploy. Treat nonzero exit as source failure, then read stderr, edit, and rerun.

When a frontend is present, inspect the sandbox output's a2a frontend info. For React apps, make sure frontend/package.json defines a build script and the deployed build process can produce frontend/dist/index.html.

After cp_deploy_tarball, compare live_skills[].input_schema with the actual public @skill signatures. If a live schema is missing an argument or shows an old shape, refresh or redeploy until the live Agent Card matches the source.

Minimal Good Result

For most generated agents, a good result is one public A2A @skill, one workspace-backed DeepAgent, one or two internal DeepAgents skills, and a few small deterministic tools. Resist broad endpoint sets unless the user asked for a multi-operation agent.