dataflow-operator-builder

name	dataflow-operator-builder
description	Build production-grade DataFlow operator scaffolds (generate/filter/refine/eval) for Codex and coding agents. Trigger when users ask to create/new/scaffold operators, add OPERATOR_REGISTRY registration, generate DataFlowStorage-based CLI wrappers, or generate operator unit/registry/smoke tests.

DataFlow Operator Builder

Build production-ready DataFlow operator artifacts with either interactive interview mode or direct spec mode.

ZH: 通过“交互采访模式”或“直接 spec 模式”快速生成生产可用的 DataFlow Operator。

Usage

/dataflow-operator-builder
/dataflow-operator-builder --spec path/to/spec.json --output-root path/to/repo
/dataflow-operator-builder --dry-run --spec path/to/spec.json --output-root path/to/repo

Script Directory

Agent execution instructions:

1. Resolve this SKILL.md directory as SKILL_DIR.
2. Use ${SKILL_DIR}/scripts/build_operator_artifacts.py.

ZH:

1. 将当前 SKILL.md 所在目录作为 SKILL_DIR。
2. 使用 ${SKILL_DIR}/scripts/build_operator_artifacts.py。

Script	Purpose
scripts/build_operator_artifacts.py	Generate operator + CLI + tests from spec
scripts/example_spec.json	Example input spec with defaults

Scope

This skill targets:

• DataFlow-style operator implementation (DataFlowStorage + dataframe flow)
• @OPERATOR_REGISTRY.register() registration
• Separate CLI wrapper under cli/
• Minimal but production-grade tests (unit/registry/smoke)

ZH:

• 面向 DataFlow 风格的 operator 实现（DataFlowStorage + dataframe 流程）
• 自动包含 @OPERATOR_REGISTRY.register() 注册
• CLI 与 operator 逻辑分离
• 生成最小但可用的测试骨架（unit/registry/smoke）

Default families:

• generate
• filter
• refine
• eval

Two Working Modes

Mode A: Interactive Interview Mode

Use AskUserQuestion in batch mode with exactly two rounds:

• Round 1: structure fields
• Round 2: implementation fields

Important:

• In each question block, include recommended option + short reason.
• Ask follow-up questions only when high-impact fields are missing or contradictory.
• Do not ask one-by-one when the same round can be asked in one batch.

ZH:

• 使用 AskUserQuestion 且每轮“批量提问”，固定两轮。
• 每个问题块给出“推荐选项 + 简短理由”。
• 仅在高影响字段缺失或冲突时追问。

Interview schema:

• references/askuserquestion-rounds.md

Mode B: Direct Spec Mode

When user already provides --spec, skip interview and run directly.

ZH: 用户已提供 --spec 时，直接执行，不再采访。

Required Workflow

Operator Builder Progress:
- [ ] Step 1: Load references
- [ ] Step 2: Choose mode (Interactive or Spec)
- [ ] Step 3: Build/validate spec JSON
- [ ] Step 4: Dry-run file plan
- [ ] Step 5: Confirm overwrite policy (light guardrail)
- [ ] Step 6: Generate files
- [ ] Step 7: Run validation (none/basic/full)
- [ ] Step 8: Write runtime log events
- [ ] Step 9: Report generated artifacts + validation results

Step 1: Load References

Read:

• references/operator-contract.md
• references/registration-rules.md
• references/cli-shell-guidelines.md
• references/gotchas.md
• references/acceptance-checklist.md

Step 2: Interview or Direct Spec

• Interactive mode: follow references/askuserquestion-rounds.md strictly.
• Spec mode: parse user-provided spec directly.

Step 3: Build Spec JSON

Use:

• scripts/example_spec.json

Required spec fields:

• package_name
• operator_type
• operator_class_name
• operator_module_name
• input_key
• output_key
• uses_llm

Optional spec fields (with defaults):

• cli_module_name (default: <operator_module_name>_cli)
• test_file_prefix (default: <operator_module_name>)
• overwrite_strategy (default: ask-each)
• validation_level (default: full)

Step 4: Dry-Run

python "${SKILL_DIR}/scripts/build_operator_artifacts.py" \
  --spec <spec.json> \
  --output-root <repo-root> \
  --dry-run

Step 5: Light Guardrail (Required)

Before writing files, show:

• full create/update file list
• existing files
• selected overwrite strategy

Then ask for explicit confirmation: y/N.

Step 6: Generate

python "${SKILL_DIR}/scripts/build_operator_artifacts.py" \
  --spec <spec.json> \
  --output-root <repo-root>

Useful flags:

• --overwrite {ask-each,overwrite-all,skip-existing} (override spec)
• --validation-level {none,basic,full}
• --log-dir <path>
• --no-log

Step 7: Validation

• none: skip validation
• basic: import + registry + test file existence
• full: basic + runtime smoke execution

Step 8: Runtime Logs (Light Memory)

Default log root:

• ${CLAUDE_PLUGIN_DATA}/dataflow-operator-builder/ if env exists
• else %USERPROFILE%/.codex_plugin_data/dataflow-operator-builder/

JSONL events:

• dry_run
• generate_start
• generate_done
• validate_done
• cancelled
• error

Step 9: Output Summary

Report:

• operator class
• generated/updated paths
• overwrite behavior
• validation level and pass/fail details
• suggested next test command(s)

File Layout Produced

<output-root>/
├── <package_name>/
│   ├── __init__.py
│   ├── cli/
│   │   └── <cli_module_name>.py
│   └── operators/
│       ├── __init__.py
│       └── <operator_type>/
│           ├── __init__.py
│           └── <operator_module_name>.py
└── test/
    ├── test_<prefix>_unit.py
    ├── test_<prefix>_registry.py
    └── test_<prefix>_smoke.py

Notes

• Keep behavior-level customization after scaffold generation.
• Keep operator description contract bilingual (get_desc(lang='zh'/'en')).
• Runtime messages should remain clear English.

ZH:

• 建议先生成骨架，再做行为细化。
• get_desc 需保持中英文双语契约。
• 终端输出信息应以英文为主，清晰可读。