複数のアプリやMCPホストからブランド画像・動画を生成、調整、書き出し、挿入する作業を支援します。ライブラリ選択や修正依頼も扱います。
- スライド用のブランド画像を作りたいとき
- 既存素材をフィードバックに合わせて直したいとき
- 動画素材を生成して書き出したいとき
ブランドライブラリやテンプレートの参照を踏まえ、用途に合う画像を生成・調整するスキルです。構図や色、文字の扱いを整理し、候補画像を比較しながら仕上げられます。
Generate and refine brand-consistent images from libraries, references, and prior candidates. Use before calling image-generation or refinement actions.
実行スコア 未検証
インストール方法を見るブランドライブラリやテンプレート、既存の参照画像を手がかりに、用途に合う画像の生成と調整を進めます。画像の役割、被写体、構図、媒体、照明、配色、文字の扱い、変えてはいけない要素を整理し、候補を作成します。テンプレートがある場合は保存済みの形式やスタイル、参照ボードを使い、指定された文章は画像内テキストとして扱います。候補は追加の方向性を試したり、既存画像を部分編集したり、被写体を保ってライブラリのスタイルに寄せたりできます。
ブランドの見た目を保ちながら、告知画像や商品画像などを作りたいときに役立ちます。参考画像やブランドキットがあり、どの画像を使うか、何を生成するかを判断しながら進めたい場合にも向いています。テンプレートが複数ある場合は用途に合うものを選び、適切な参照素材を組み合わせます。
画像生成、画像編集などのアクションを呼び出す前に使います。正確なロゴは生成せず、登録済みのロゴ素材を使います。ブランドや商品の事実、顧客の証拠、スクリーンショットを画像生成で補う用途には使いません。
この紹介文は、公開されている SKILL.md をもとに AI(OpenAI Codex)が作成しました。正確な仕様は下の原文を確認してください。
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this skill before calling generate-image, generate-image-batch, or
refine-image.
Treat references as evidence, not decoration. The prompt should explain the asset's job, subject, framing, medium, lighting, palette, exact text policy, and what must not change. Keep the active library or preset's style brief and the caller's design-system constraints authoritative; Impeccable-style composition guidance may improve the result but must not replace them.
Before generating, decide whether the slot needs a produced image, a direct reuse of an approved asset, or a semantic UI/icon/diagram owned by the caller. Do not use image generation to paper over missing product facts, customer evidence, screenshots, or exact logos.
@ mentions when the user tags generation inputs.
brand-kit references map to libraryId, preset references map to
presetId, and media-type references choose image generation versus video
generation. Call view-screen when the user says "this library" or "this
image" and you need fresh IDs. The image model may default from the composer
image-model picker.@preset (presetId) owns aspectRatio, imageSize, model,
tier, and category. When a preset is set, do NOT pass those args yourself —
leave them out so the preset's saved values are used. You cannot see the
preset's settings from the presetId, so passing your own guess silently
overrides the preset (this is the usual cause of a preset's aspect ratio being
ignored). Pass one of these args alongside a preset ONLY when the user
explicitly asks for a value that differs from the preset. When there is no
preset, set them explicitly or rely on the action schema defaults.hero; diagrams
should prefer diagram; product imagery should include product and logo
references.status: "reference" are valid generation
inputs. Use their returned asset IDs in preset reference fills or reference
boards the same way you would use uploaded reference assets.assetLibraries.settings.canonicalStyleAssetIds and assets marked
assets.metadata.isStyleAnchor before sampling other relevant references.generate-image-batch with stable slotIds so the shared generation tray
can show live slots.generate-image and generate-image-batch are synchronous for images. One
batch call should produce the requested candidates and return compact asset
summaries with IDs and URLs; do not follow it with get-generation-run,
refresh-generation-run, or more generation unless the user asks for another
direction or the returned slot has ok: false.get-asset when full asset details are needed. Use get-audit-run or
list-audit-runs for the prompt, compiled prompt, references, and generation
settings.template @mention as templateId or
call list-templates when choosing one. Pass the template through
generate-image, generate-image-batch, refine-image, or
rerun-generation-run.sessionId and call
update-generation-session after each new candidate so the active asset,
feedback, and run lineage stay resumable./asset/<assetId>/embed; from another
app, preserve the returned preview/download URLs exactly.refine-image --assetId. Use edit-image for targeted edits
and restyle-image when the user wants to preserve a subject image while
applying library style. Pass subjectAssetId, styleStrength, and tier
when they matter.tier values intentionally: fast for exploration, best for
final/high-value output, and auto when there is no clear preference.source: "a2a" and callerAppId to
generate-image-batch / refine-image. The design team uses the audit log
to review quality by app, library, model, prompt, and lineage.@ mentions are the source of generation inputs. Map brand-kit
references to libraryId, template references to templateId, and media-type
references to choosing image (generate-image / generate-image-batch) or
video (generate-video) generation.imageGenerationModel, which image generation actions may use
when model is omitted.template is tagged, the server embeds that template's aesthetics and
creative philosophy (brand style brief, prompt template, text/logo policy,
output format) into your message inside a <tagged-templates> block.
Study and internalize that brief before you generate — let it drive
composition, mood, lighting, and subject — then pass the templateId to
generate-image / generate-image-batch so the saved format/model/tier/logo
apply automatically. Do not restate those as ad-hoc args.templateId instead of
ad-hoc settings. Only generate without a template when nothing plausibly matches.list-templates and scan titles/descriptions/categories for a use-case match.
A template encodes the designer's format, model, layout, and
reference board; using it is always better than improvising.templateId; do not restate its saved
aspect ratio/size/model/tier. If several plausibly match: pick the best and
state which one you used; do not ask the user to choose.settings.presetReferences. Fill required
variable entries via presetReferenceFills: search the library for assets of
those people first; ask the user for photos only when none exist. Never skip a
required entry.embeddedText per the existing text rules; keep the creative direction in
prompt.embeddedText and
optional textPlacement to generate-image or each generate-image-batch
slot. Keep the general prompt for creative direction; the structured text
fields are what allow the pipeline to render copy instead of suppressing it.*-generation-preset actions remain aliases for existing agent threads.referenceAssetIds are preserved.presetReferenceFills; each fill REPLACES that entry's pinned images rather
than appending. Required variable entries block generation until you provide
at least one image.presetReferenceFills: [{ referenceId, assetIds }] to generate-image or
generate-image-batch. Board images are additive to brand style references.
User-uploaded per-event people/photos should be uploaded as content images
(subject intent/role), not as reusable brand style references.analyze-collection-style
and use its vision brand analysis for palette, composition, lighting, subject
treatment, typography policy, and constraints.originalPrompt.After generation, reply with asset IDs and previews. Ask whether to save, iterate, or produce another direction.
When the user says a designer should pick up the work, create a generation
session with create-generation-session, including the active assetId,
relevant runIds, presetId, and the feedback summary. Use
prepare-generation-session-continuation to open a new chat with the handoff
context preloaded.
Every generation is audit logged automatically. When a reviewer asks how images
are performing, use navigate --view audit, list-audit-runs, or
get-audit-run.
Use rerun-generation-run to rerun the original prompt and settings from an
older generation against the latest library style brief, custom instructions,
collection data, and deterministic references.
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複数のアプリやMCPホストからブランド画像・動画を生成、調整、書き出し、挿入する作業を支援します。ライブラリ選択や修正依頼も扱います。
特定の顧客企業について、契約と製品利用の数値を照らし合わせ、根拠や不足情報を明記した健全性レポートをまとめます。
アプリの操作をエージェントと画面の両方から呼び出せるアクションとして実装し、API連携や画面のデータ取得を一貫した形で追加するスキルです。
新機能をUI・エージェント用アクション・説明資料・画面状態の4領域でそろえ、利用者とエージェントのどちらからも一貫して使えるようにするチェックリストです。
複数のデータソースを照合して、傾向や指標、根拠をまとめた分析結果を作成します。必要に応じて、再実行できるダッシュボードとして保存できます。
Analyticsの管理画面で、機能フラグの状態確認と切り替え、ダッシュボード利用状況の調査、接続先アプリのデータベース管理を行います。