84 lines
2.9 KiB
Markdown
84 lines
2.9 KiB
Markdown
---
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name: agent-quality-review
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description: 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.
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---
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# Agent Quality Review
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Before `test_agent_in_sandbox` and before deploy, review the generated project
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as source code, not just as text.
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## Required Files
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Every generated project needs:
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- `agent.py`
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- `a2a.yaml`
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- `requirements.txt`
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- Optional `skills/<skill-name>/SKILL.md` bundles for nontrivial DeepAgents
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behavior
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Run `list_agent_files` and read the files that matter. If the project was
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deployed before, use `sync_agent_workspace_from_repo` before editing when the
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builder needs to continue from the current managed repo source.
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## Agent.py Review
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Check these items:
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- Public `@skill` methods are async and have `RunContext[...]` plus typed JSON
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arguments.
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- The public schema is small and stable. No unbounded command strings, hidden
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prompt fragments, or provider credentials as public inputs.
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- The implementation reads `ctx.llm` when `llm_provisioning` is
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`CALLER_PROVIDED`.
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- Any use of DeepAgents file tools passes `backend=ctx.workspace_backend()`.
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- Any project skills are seeded into the backend and passed with
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`skills=skill_sources or None`.
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- Custom subagents that need project skills include their own `skills` field.
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- Deterministic tools do exact work; no fake canned-response tools.
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- File-producing skills call `ctx.write_artifact` and `ctx.emit_artifact`.
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- Commands that create downloadable outputs run through `ctx.workspace_shell`
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or `ctx.workspace_python` and write under `/workspace/outputs/...`. Plain
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subprocesses in the agent container are not durable workspace writes.
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- External calls, secrets, workspace access, pricing, and resources are
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declared on the class.
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## A2A YAML Review
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Mirror deployment-only details in `a2a.yaml` because the platform reads it
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before importing user code:
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```yaml
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name: research-agent
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version: 0.1.0
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entrypoint: agent:ResearchAgent
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expose:
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public: true
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runtime:
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apt_packages: [ffmpeg]
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resources:
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cpu: "2"
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memory: 2Gi
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max_runtime_seconds: 900
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```
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Use `runtime.apt_packages` only for Debian system binaries. Python packages
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belong in `requirements.txt`.
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## Sandbox And Live Card
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Always run `test_agent_in_sandbox(name)` before deploy. Treat nonzero exit as
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source failure, then read stderr, edit, and rerun.
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After `cp_deploy_tarball`, compare `live_skills[].input_schema` with the
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actual public `@skill` signatures. If a live schema is missing an argument or
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shows an old shape, refresh or redeploy until the live Agent Card matches the
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source.
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## Minimal Good Result
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For most generated agents, a good result is one public A2A `@skill`, one
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workspace-backed DeepAgent, one or two internal DeepAgents skills, and a few
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small deterministic tools. Resist broad endpoint sets unless the user asked
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for a multi-operation agent.
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