1Passwordに保存したパスワードやAPIキーを、コマンド実行や設定テンプレートで使うためのスキル。CLIの導入、認証、秘密情報の取得・受け渡しを案内します。
- 1Password CLIの導入と設定
- 保存済みAPIキーでコマンド実行
- 設定テンプレートへの秘密情報の埋め込み
目標画像と実装した3Dシーンのスクリーンショットを比較し、構図・照明・質感を段階的に改善します。見た目の再現度を採点し、描画速度も確認しながら修正を重ねるスキルです。
Build stunning 3D scenes via a concept-art fidelity loop.
目標となる画像をもとに3Dシーンを実装し、実際の表示を撮影して比較・修正する工程を進めます。構図、照明と色、素材の質感、細部の順に再現度を評価し、採点と具体的な修正指示を記録します。見た目が目標に近づいたら、FPSという描画の滑らかさを示す値も確認し、必要に応じて最適化します。既存の作品は現在のスクリーンショットから改善案の画像を作り、修正の目標にします。
three.jsやWebGLを使うブラウザの3Dシーン、ゲームの見た目を作り込みたいときに向いています。照明や質感を少しずつ調整するだけでは改善しない場合に、素材や構図などの方針を見直す手順も含みます。
image_generate、browser_exec、vision_analyzeなど、画像生成・画面撮影・画像評価のためのツールが必要です。生成した目標画像は実装前に利用者が確認します。対応環境はLinuxとmacOS、ライセンスはMITです。一般的なアプリ機能や2DのUI設計は対象外です。反復作業には多くのトークンを使う場合があります。
この紹介文は、公開されている SKILL.md をもとに AI(Claude Haiku)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。
An autonomous process for building extremely impressive visuals, especially 3D scenes (games, apps, usually browser three.js/WebGL): generate photorealistic concept art of the ideal result, build it, screenshot the live build, have a judge score screenshot vs concept against a gated ladder, and iterate until convergence. Goal: the most visually stunning result at an acceptable frame rate for the target platform (e.g. 60 fps browser, 120 fps modern mobile).
This skill does NOT cover general web-app functionality, 2D UI design, or non-visual quality — only the visual-fidelity loop.
image_generate tool. If unavailable, stop and ask the
user for a concept image (or an image-generation API to connect to).browser_exec — serve the build locally
(python3 -m http.server for static builds), then new_tab(url),
wait_for_load(), capture_screenshot().vision_analyze (see Judge section for the one-image-per-call
workaround).blender-3d-automation
skill. delegate_task for parallel asset work and fresh-context judging.If you don't have the tools needed for the full loop, flag that to the user early and stop.
| Stage | What happens | Artifacts (.dream-loop/) |
|---|---|---|
| 1. Target | Get/confirm the user's description | notes |
| 2. Concept | Generate "in-engine screenshot" concept art | concept.png |
| 3. Budget | Record time budget & start time (if given) | notes |
| 4. Build | Implement the concept as well as possible in one go | source, plans |
| 5. Screenshot | Capture live build at concept resolution | round-N.png |
| 6. Self-check | Rigorous side-by-side audit before judging | assessment log |
| 7. Judge | Ladder-scored comparison, actionable directives | verdict log |
| 8. Iterate/Exit | Address directives or exit per criteria | — |
If the user provided a description of a game, scene, or app, proceed — don't ask for clarification unless it's too vague to generate concept art from. If they didn't, ask for it.
Put working context/files in .dream-loop/ and gitignore it (unless told
otherwise).
The concept is a realistic, high-quality, impressive target: the look of a current AAA game running in real time. Physically plausible materials (wet stone, brushed metal, cloth, glass) with real roughness and normal detail, correct proportions, atmosphere (fog, haze, rain, dust, volumetric light), cinematic lighting with a clear key and rich shadows. It should NOT be stylized or an artistic rendition — it should look like a true screenshot of the ideal result.
Avoid these failure modes when generating with image_generate:
Aim for the middle ground: beautiful surfaces and materials that shaders render well, strong atmosphere and lighting, an interesting palette, and focused hero elements with fine detail that draws the eye (not every element fighting for attention).
Prompt for "in-engine screenshot" more than "concept art" and discourage the
noisy/grainy look. Review the image with vision_analyze; if it hits a
failure mode, pass it back to image_generate in edit mode and ask it to fix
the issue. Save it as .dream-loop/concept.png.
If you generated the art (the user didn't supply it), pause and confirm it matches the user's vision before starting the build loop.
If the user gives a time budget, record the start time and check the clock
between rounds. Don't degrade visual fidelity to hit the budget — strive for
the absolute best result, and don't rush work to the judge. Parallelize or
distribute work (e.g. delegate_task for independent assets) to hit the
time goal, but no shortcuts: it's better to hit the time limit with
meaningful, beautiful progress than with something broadly complete but ugly.
If no time budget is given, run until an exit criterion — but warn upfront that this may consume a lot of tokens.
Look at the concept art and implement it in one go, making that first pass
count across every tier of the score ladder: composition, textures, lighting,
details. Sculpt and model assets carefully (or use external ones if allowed);
don't settle for basic procedural elements and flat surfaces unless the art
style calls for it. Write intermediate files/plans to .dream-loop/.
blender-3d-automation skill). For complex assets,
delegate to subagents via delegate_task.image_generate for textures, normal maps, skyboxes, etc. — better
looking and faster than procedural ones.Serve the build (e.g. python3 -m http.server in the build dir), then via
browser_exec: new_tab('http://localhost:8000'), wait_for_load(), allow
the scene to settle, capture_screenshot(). Target the same resolution and
aspect ratio as the concept art so the comparison is fair. Save as
.dream-loop/round-N.png.
Each time, review the candidate screenshot yourself before submitting. Do not submit half-baked work. Compare screenshot and concept side by side and log an honest assessment of judge-readiness; only submit if confident you've significantly improved the score. Be rigorous and audit every pixel: big stuff (missing/incorrect objects, wrong scale, perspective, positioning) and small stuff (rendering glitches, flat untextured surfaces, ugly lighting, poor contrast, washed-out or oversaturated color, speckles, ugly shadows). Scan surface by surface, object by object, and list findings.
Judging should ideally be done by a fresh subagent with a clean context each
round (delegate_task), to keep it objective and cheap. Give the judge the
latest screenshot, the concept, and (from round 2 on) the previous round's
screenshot and verdict.
Mechanics: vision_analyze takes one image per call. Either have the judge
make sequential calls (concept, then screenshot, then compare from memory of
its own descriptions), or — better — stitch a labeled side-by-side composite
with ImageMagick (convert concept.png shot.png +append compare.png) or PIL
and analyze that single image.
Judge prompt:
You are an art director reviewing a real-time render against its concept art. Compare the screenshot to the concept and score it 0-10 using this ladder. The ladder is gated: a frame cannot score above a tier's cap until every requirement of the tiers below it is fully met. Be strict about the gates.
- Tier 1, shape (0-3): camera, framing, composition, and the position and rough scale of every major object match the concept. Layout, not finish: every major element present, in the right region of the frame (within ~10% of frame width/height), at roughly the right size (within ~25%). Right place and vaguely correct outline passes even if edges and surface are wrong; save precision nitpicks for Tier 4. Cap 3 until true.
- Tier 2, light and color (3-5): key light direction and color, overall exposure (no clipping to black or white), shadow depth, palette, contrast, atmosphere. Attend to reflections, glows, etc. Ensure the scene is not too bright or dark relative to the concept. Judge the whole frame, not tiny details (Tier 4). Cap 5 until lighting/reflections/color/ contrast are generally right.
- Tier 3, materials and surfaces (5-7): every surface reads as the right material at a glance: textures, roughness, translucency, wetness, reflections. Assets must not look procedural, blocky, smooth/plastic; frame-dominating elements should be properly sculpted and detailed. Cap 7 until true.
- Tier 4, fine detail (7-9): the small things. Nitpick relentlessly; inspect every little object up close. Layout aligns near-perfectly; materials extremely convincing. Cap 9 until right.
- Tier 5, indistinguishable (9-10): holds up side by side and zoomed in. Nitpick every pixel.
If a previous verdict and screenshot are provided: you are one reviewer in a sequence, not the first. Maintain consistency. First mark each previous directive LANDED, PARTIAL, or NOT DONE against the new screenshot; carry forward anything PARTIAL or NOT DONE. Don't reverse a prior directive unless the result is clearly worse — and if you do, say so and why.
Output format:
- Score on the first line; "Tier N" (highest fully-passed gate) on the second. 1b. If given a previous verdict: the LANDED / PARTIAL / NOT DONE list.
- "Blocking:" the specific failures of the next tier's gate. The builder must clear these before anything else counts. Name the element and the change, with magnitudes: "Rocks: replace the stacked ovoid boulders with one continuous fractured slab; cracks 2-5cm wide, dark interiors, add surface texture so they don't look flat/plastic" — not "the rocks look artificial".
- Then at most 4 further directives from higher tiers, same style, ordered by points recoverable.
No non-actionable feedback ("this looks synthetic") — name the specific causes. Every directive must be actionable this round. Don't round up: if a gate isn't fully passed, the cap holds.
When building on an existing product (or the user re-invokes the skill for
refinements), don't create new concept art in a vacuum — it may diverge from
what exists. Instead capture a live screenshot of the current product and
prompt image_generate to render the best possible version of it (current
screenshot → AAA-graphics version of the same shot), then use that as the
target. Multiple screens can run parallel judge loops if asked, at higher
token cost.
vision_analyze call — it takes one image;
composite them first.wait_for_load() for asset streaming and animation warm-up..dream-loop/concept.png exists and passed the failure-mode review (and
user confirmation, if generated).Adapted from dream-loop by Anshu
Chimala (MIT). Upstream license vendored as LICENSE.txt.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
1Passwordに保存したパスワードやAPIキーを、コマンド実行や設定テンプレートで使うためのスキル。CLIの導入、認証、秘密情報の取得・受け渡しを案内します。
損益計算書・貸借対照表・キャッシュフロー計算書を数式で連動させるExcelモデルを作ります。実績と予測の入力、前提を変えた比較、財務三表の整合性確認を段階的に進めます。
Run PyTorch training across GPUs with minimal changes.
日本語の概要は準備中です。原文の説明を表示しています。
Set up Actual Computer (actual.inc) inference in Hermes.
日本語の概要は準備中です。原文の説明を表示しています。
技術が苦手で不満を抱きやすい利用者になりきってアプリを試し、操作の分かりにくさや離脱の原因を発見。実際の改善課題と個人的な不満を分け、修正案をまとめます。
2つのエージェントが別々に加えた変更のGit競合を、双方の差分と意図から中立的に解決。競合箇所ごとの判断理由を記録し、ビルドやテストで統合結果を確認します。