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[Feature] Agent-oriented VChart spec authoring workflow

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評価

難易度
5/5
見積もり時間
1週間以上
初心者へのやさしさ
35/100
issue の種類
機能追加
明瞭さ
おおむね明確
活発さ
活発
技術スタック
typescript
領域
ai, tooling

調査の方向性

まず既存の vchart-development-assistant workflow と vchart-mcp-server の出力形式を確認し、そのうえで、これを新しい skill にするのか拡張にするのかを明確にします。target、context、result、test を軸にエージェント向けの workflow を定義します。完了の条件は、データマッピングと設定が検証された再利用可能な VChart spec を生成でき、必要に応じて実際の renderer で検証できることです。

索引モデルが issue の本文から書いたものです。

説明

What problem does this feature solve?

VChart already provides vchart-development-assistant, which is useful for developers building, modifying, or debugging VChart applications.

I think there is a related but different workflow that may be worth supporting explicitly: an autonomous/data agent producing a VChart visualization as part of its own work.

In this case, the agent is not necessarily helping a developer write a VChart application. It may have already analyzed some data and simply needs to decide how to visualize the result, author a valid VChart spec, and pass that spec to a renderer.

For example:

User
↓
"Show these data with an appropriate VChart visualization."
↓
Agent

  • understands the visualization goal
  • inspects the data and runtime constraints
  • chooses an appropriate chart
  • authors a VChart spec
  • validates the result
    ↓
    VChart spec
    ↓
    host / renderer

The VChart spec itself is the useful artifact here. It may be rendered in a browser, embedded into another application, consumed by another tool, or converted to image/HTML later.

I ran a small prototype of this workflow with Feishu Card as the host renderer.

After the host/output constraints had been provided in the earlier conversation context, the later user request was simply natural language plus data:

下面的数据
周一 12
周二 25
周三 18
周四 32

用合适的 vchart 图表展示

The agent chose a bar chart, generated the VChart payload, and the host rendered it directly as a native chart.

Image

In this prototype, Feishu is only the renderer and the transport layer passes the generated payload through unchanged. The VChart authoring itself is done by the agent.

What feels missing is a reusable way to provide that working knowledge. Currently, the prototype relies on conversation history to teach the agent the relevant VChart and renderer constraints. A VChart skill could make this knowledge explicit and reusable, instead of requiring low-level instructions to be repeated in each conversation.

The existing vchart-development-assistant is primarily developer-oriented and currently treats runnable application output such as HTML as the final result. For autonomous/data-agent workflows, I think it may also be useful to treat a reusable VChart spec as a first-class result.

This also seems consistent with vchart-mcp-server, where spec, image, and html are already separate output forms.

What does the proposed API look like?

This may not require a new VChart runtime API.

I think the useful addition is an agent-facing authoring workflow, either as a new skill or as an additional workflow in the existing vchart-development-assistant.

A possible working model is:

Target

  • What should the visualization communicate?

Context

  • What data is available?
  • What VChart/runtime/renderer constraints apply?

Result

  • Produce an appropriate, reusable VChart spec.

Test

  • Check data/field mappings and required configuration.
  • When possible, verify the spec with a real VChart renderer.

The intention is not to turn visualization into a rigid step-by-step pipeline. The agent should still be able to make decisions based on the actual data and task; the skill would provide the domain knowledge, constraints, references, and validation methods needed to do that reliably.

I am not strongly attached to whether this should be a separate skill or an extension of vchart-development-assistant. I would prefer to align on that boundary before implementing it.

If this use case fits the direction of VChart's skill ecosystem, I would be happy to work on the implementation.

主要言語
TypeScript
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フォーク
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平均マージ
1日 17時間
マージ済み PR（30日）
20

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