「eyes」の検索結果

Diagnose, fix, or validate SwiftUI animation and scroll behavior by temporarily instrumenting views with MotionEyes, capturing console traces over time, and comparing motion data to user intent or test expectations. Use when users report bugs such as wrong direction, premature fade, timing or easing mismatch, unexpected movement, missing movement, incorrect relative movement between views, scroll jumps, scroll restoration drift, content-offset desynchronization, or when they want to turn MotionEyes traces into focused assertions. Prefer XcodeBuildMCP log capture first and fallback to CLI log streaming; remove only agent-added MotionEyes instrumentation after validation.

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。

Pixel-based motion and UI change analysis from frame sequences or screenshots using computer vision and visual comparison. Use when `.transition` or visual effects are not observable via MotionEyes logs, when you only have screenshots/video, when you need regression diffs across builds, or when you need to summarize on-screen changes without instrumentation. Do not use when MotionEyes traces are available and you need precise timing/values; use `motioneyes-animation-debug` instead.

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Use the unprimed sub agent as a second set of eyes before accepting visual work — MANDATORY before declaring any user-reported visual bug fixed or claiming a visual change verified; primed eyes pass defects fresh eyes catch.

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。

Run a targeted fresh-eyes blunder pass over code, specs, plans, reviews, closure gates, or skill edits. Use when asked to reread with fresh eyes, find obvious bugs, catch mistakes/oversights/omissions, check for embarrassing misses, or do a second independent pass before closure. Use it as the final falsification/check pass for another workflow, not as a substitute for implementation or verification.

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。

Review one or more ADRs with fresh eyes (as a team member without prior context), identify narrative and structural issues, then rewrite them. Use when the user asks to review, improve, rewrite, or take a fresh-eyes pass on an ADR or a set of ADRs (Architecture Decision Records).

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。

Draws hand-inked cartoon critters and living objects as SVG cards (480x360), each acting out a coding or design idea. Identified by thick black brush-pen outlines that swell and taper and break into separate strokes, loose marker-and-watercolour fills that slip past the line, diagonal hatching instead of solid shadow, huge googly eyes with two specular dots, and stray ink hairs and flecks, in tomato red, leaf green, sunflower yellow, sky blue and teal on white. Covers palette, brush numbers, mascot construction (eyes, blush, mouths, 3-finger hands), props, ground patches and a tested, seeded brush kit. Use when asked for ink sketch, hand-drawn, brush pen, sketchy, doodle, marker or watercolour-cartoon illustrations, googly-eyed mascots or critters, playful empty states, onboarding, 404 or error pages, loading states, blog headers, feature spots or marketing cards.

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。

AWT — AI-Powered E2E Testing (Beta) workflow skill. Use this skill when the user needs AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

AWT — AI-Powered E2E Testing (Beta) workflow skill. Use this skill when the user needs AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Look at real hardware, screens, panels, and wiring on a desk or workbench through a camera, so Claude can SEE physical output that no unit test, log, or API can. Use whenever the user says "are you seeing this?", "olha isso", "look at this", "watch this", "can you see...", "check the camera", "look at the screen" / "look at the display", "is this wired right?", or "what's this part?"; asks to visually verify a rendered display, LCD, LED, or panel; wants a wiring, polarity, or voltage-rail check before power-on; or needs to confirm a font, layout, clipping, or on-screen value renders correctly instead of trusting green tests. Grabs the current camera frame(s) and reads them; supports a watch mode for an action in progress. Needs a snapshot-capable camera reachable over HTTP (Android IP Webcam app, Raspberry Pi camera-streamer, or any snapshot URL) set via CCE_CAM_URL.

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。

AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g

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。

Document analysis with inline source screenshots. When you ask Copilot to analyze a document, Eyeball generates a Word doc where every factual claim includes a highlighted screenshot from the source material so you can verify it with your own eyes.

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。

Tiered LLM extraction pattern for large corpus processing (email archives, document dumps, transcript libraries). A utility-tier model triages and classifies at speed; the reasoning tier does the default deep read; the deep tier is the escalation for the highest-value content. Prevents spending deep-tier money on noise while ensuring the important content gets the best eyes. A deterministic privacy wall runs before any LLM call.

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。

Draws original cosy animal scenes as hand-authored SVG cards (480x360) in a grainy gouache, risograph-like style. Identifying traits are flat shapes with no outlines on a hot-pink card; soft 3-4 stop gradient shading on every rounded form, darker toward edges and base; a fine dark multiply grain that gathers at each shape's edges; a chalky purple-pink shadow pooled under the group; and big glossy eyes with cream star highlights, framed by grained autumn leaves and sparse cream sparkles. It covers design and coding moments with one cute animal at a laptop, monitor, desk, window, mug or book stack. Use when someone asks for grainy, gouache, riso, risograph, textured, cosy, cozy, autumn, hygge, storybook or children's-book style animal art, or for an empty state, onboarding step, 404 page, blog header, feature spot or marketing card with a warm handmade feel.

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。

Draws original hand-authored SVG spot illustrations in an outlined cartoon style for design and coding subjects. Identifying traits: a warm near-black ink outline on every flat fill in three weights (2.5 / 2 / 1.5 px), tapered ink slivers for folds, brows and finger splits, big-headed characters with huge white eyes, thick brows, open smiles and pink blush, blob hair with white highlight dashes, and a small decor set of "+" sparkle crosses, outline rings, squares and coloured dots on a transparent card. Covers one character with one hero prop, gripping and holding hands, sneakers, clothing folds and UI props. Use when someone asks for an outlined, inked, comic, sticker or doodle-style cartoon, a friendly character or mascot, or a spot illustration for an empty state, onboarding, 404 or error page, success state, blog header, feature spot or marketing card.

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。

Draws original design and coding scenes as hand-authored SVG cards (480x360) in a two-colour brush-marker style. Identifying traits: thick wobbly black marker lines with blobby ends, built as filled ribbons; one second ink, mint green, in flat blocks that slip 2-10 px off the line like a misregistered print; solid black hair, trousers and shoes with mint highlight dashes; and a crinkly squiggle camo on cream areas, all on warm cream paper. Characters are chunky, with profile heads, big noses, dot eyes, big hands and feet. It covers people coding, thinking, carrying and resting with laptops, mugs, chairs and rubber ducks. Use when someone asks for brush, marker, felt-tip, two-colour, duotone, riso-print, zine, mint-and-black or doodle illustrations, or for empty states, onboarding, 404 pages, blog headers, feature spots or marketing cards with a bold hand-made look.

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。

Draws flat vector spot illustrations of tall, long-legged people doing design and coding things, with solid black as a main colour. Traits: no outlines on coloured fills; black as big solid shapes (hair, trousers, shoes, devices) and as thin detail lines; denim blue, leaf green, sun yellow and tomato red on cream skin; dot eyes, round glasses and blush; big shoes on one thin black ground line; two stars and two plus-sparkles as the only decor. Covers palette, line weights, characters, heads, hands, props, composition and a parts helper for limbs, shoes, heads, hands and paper props. Use when someone asks for flat illustration with black, flat people or character illustration, friendly product or SaaS illustration, or art for empty states, onboarding, 404 and error pages, blog headers, feature spots, marketing cards or app screens in this style.

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。

Hunt client-side DOM vulnerabilities — DOM Clobbering (overwrite JS globals via HTML injection), PostMessage hijacking (missing origin check), Service Worker abuse (intercept requests from same-origin script), CSS Injection/Exfiltration (attribute selectors → token char-by-char via OOB), client-side template injection, dangerouslySetInnerHTML. Grounded in named public research: Gareth Heyes / PortSwigger DOM-clobbering + DOM-Invader, Michał Bentkowski DOMPurify clobbering bypasses, jQuery htmlPrefilter XSS (CVE-2020-11022 / CVE-2020-11023), d0nut CSS-exfil research. Use when hunting DOM-XSS, client-side auth bypass, or token exfiltration without server-side interaction.

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End-to-end website audit through ICP eyes. Builds synthetic personas (if they don't already exist), runs a structured scorecard review of the client's site, then runs a head-to-head competitive comparison against top competitors. Produces a single consolidated report with persona feedback, competitive positioning, and prioritized recommendations. The complete "how do our buyers actually experience our site vs the competition?" workflow.

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。

Evaluate a website, landing page, content, or any online asset through the eyes of pre-built synthetic ICP personas. Loads personas from buyer-persona-generator output, then runs them against target URLs. Supports three modes: structured scorecard, freeform focus group, and head-to-head competitive comparison. Reusable — run against the same site after changes, or against new content anytime.

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Final QC gate for a rendered 9:16 video ad, run before it is published. One script checks what a machine can decide — exact 1080x1920 size, a hook that moves and speaks in the first second, no frozen stretches, no dead air, no black frames, the brand's real logo on the end card (never a favicon, never a redrawn or wrong logo), end-card colours near the brand palette — and builds one contact sheet (frames with the TikTok/Reels UI safe zones shaded, beside the logo, product images and a font specimen) for the checks that need eyes — font, product likeness, product consistency across scenes, safe zones. A declared silent-text profile covers silent, text-led formats such as kinetic text (no audio track needed, short blank beats between text beats allowed within fixed bounds). Exit 0 PASS / 2 FAIL / 3 ERROR. Use on every finished video master before pinning it.

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Review a business plan or product strategy through the eyes of a smart-money seed/Series-A investor. Asks the brutal questions: Is this fundable? What's the moat? What's the exit story? Outputs a verdict with one of four modes — SEED VIABLE / LATER ROUND ONLY / BOOTSTRAP-ONLY / UNFUNDABLE. Use when the user has a business plan and wants to know if it would survive an investor pitch. Triggered by: 'investor review', 'is this fundable', 'VC perspective', '投资人视角'.

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Use when turning a dense sculpt, scan, ZBrush export or AI-generated mesh into animation-ready or game-ready topology in Maya without a mouse ("clean up this AI mesh", "retopo this character", Quad Draw, Retopologize), testing it deforms before rigging (bad elbow or wrist bend, lids popping through the eyes), or unwrapping UVs, cutting seams, setting texel density, mip-safe padding, UDIMs, lightmap UVs, stacking mirrored shells, or preparing a low poly and cage for baking. Keywords: edge flow, eye and mouth loops, Unfold3D, bake prep.

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Use when sculpting a realistic head, face, portrait, body or creature in ZBrush (anatomy, landmarks, proportions, "the face looks off", eyes, lips, jaw), adding skin, pores, wrinkles or reptile and dragon scales with alphas, Surface Noise, layers or morph targets, planning subdivision or HD Geometry for a film close-up, reprojecting detail, sculpting cloth folds or running Dynamics cloth, growing FiberMesh fur or hair, making horns, or judging a character sculpt the way a senior character artist would.

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Use when turning a dense sculpt, scan or AI-generated mesh into clean topology in Blender, for animation, subdivision or a game low poly with a triangle budget. Also when choosing between QuadriFlow or voxel remesh and manual retopo, placing loops around eyes, mouth and joints, deciding pole placement, setting up a Shrinkwrap cage, fixing volume loss after subdivision, preparing a low poly for baking, or auditing topology for spirals, 6-poles and triangles. Keywords: retopo, retopology, quad remesh, clean up an AI mesh, edge flow, low poly, bake prep.

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。