964 件 ・ 関連度順
概要と使いどころ
Use when auditing an ACM MM (ACM Multimedia) submission for OpenReview readiness — thematic-area choice, the 6-8 page ACM sigconf budget, references-only overflow, double-blind anonymity versus the single-blind Reproducibility/Open-Source/Dataset tracks, supplementary media, dual submission, desk-reject triggers, and last-week sequencing.
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。
Use when auditing an ACL submission before an ACL Rolling Review cycle deadline, covering long/short paper page budgets, the mandatory Limitations section, Responsible NLP checklist, anonymized PDFs and supplements, preprint declarations, dual-submission rules, resubmission linking, and ARR desk-reject triggers on OpenReview.
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。
Detect buying intent from job postings. When a company posts a job in your problem area, they've allocated budget and are actively thinking about the problem. This skill finds those companies, qualifies them, extracts personalization context, and outputs everything to a Google Sheet. Does NOT do outreach — just delivers qualified leads with reasoning.
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。
Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.
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。
Balances Hi-C contact matrices (ICE via cooler.balance_cooler, KR/SCALE/VC context), computes distance-decay expected with cooltools (expected_cis per-diagonal P(s), expected_trans scalar), builds observed/expected (O/E) matrices, and diagnoses polymer state from the P(s) log-derivative. Covers the within-matrix-vs-cross-sample distinction (balancing is NOT a normalizer), the equal-visibility assumption that CNV/aneuploidy violates (use raw counts for copy-number), cis-only balancing, mad_max/blacklist masking before balancing, multiplicative cooler weights vs divisive juicer weights, and the resolution-vs-depth budget. Use when balancing a .cool/.mcool, computing expected or P(s), making O/E matrices for compartments/loops, deciding ICE vs KR vs SCALE, choosing a resolution for a given depth, or troubleshooting NaN/all-NaN balanced matrices; route cross-sample comparison to hic-differential.
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。
Implement, review, or improve WidgetKit widgets and controls. Use when building Home Screen, Lock Screen, StandBy, or CarPlay widgets with timeline providers; configurable widgets with AppIntentTimelineProvider; interactive widgets or Control Center controls with Button/Toggle wiring; WidgetKit push reloads, refresh budgets, deep links, Smart Stack relevance, Liquid Glass/accented rendering, widget extension setup, WidgetBundle, App Groups, and entitlements.
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。
Integrate antennas into RF systems using MATLAB Antenna Toolbox and RF Toolbox. Covers impedance matching network design (L/Pi/Tee topologies, evaluation parameters, Richards transformation), measured antenna creation (E-field, directivity-only, EmbeddedE, ffsReader import), RF propagation and site planning (txsite/rxsite, coverage, SINR, ray tracing, link budget), and SAR estimation (birdcage+Phantom, conformalArray+Custom3D, direct EHfields). Use when the user wants to match an antenna, create a measuredAntenna, compute coverage or signal strength, perform ray tracing, or estimate SAR.
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。
Configure, simulate, debug, and analyze radarDataGenerator within radarScenario. Use for: interactively building radar detection scenarios from datasheets or performance requirements; diagnosing missed detections and configuration errors; interpreting sensor spherical, body, and scenario-frame outputs; deriving ReferenceRange from hardware specs via link budget; scan mode configuration (mechanical, electronic/AESA, hybrid); and validating simulation results against analytical predictions.
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。
Reduce LLM API and infrastructure costs through model selection, prompt caching, batching, caching, quantization, and self-hosting strategies. Track spend by team and model, set budgets, and implement cost-aware routing.
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。
Optional design-improvement pass for when you have spare usage to drain. Finds the shallowest modules in the code the spec touches, researches a deeper design, and proposes refactors that shrink interfaces and hide decisions — behavior held constant, tests green before and after. Proposes §I/§V/§T edits, never silent rewrites. Triggers when the user says "deepen this", "improve the design", "this module feels shallow", "pull complexity down", "use spare budget on the codebase", or invokes /ck:deepen. Leans on the codebase-design skill's deep-module vocabulary when present.
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。
Full-stack e-commerce marketing strategy builder. Analyzes your product, market, and competitors, then builds a complete omnichannel marketing plan covering paid ads, SEO, email/SMS, content marketing, social media, influencer partnerships, and referral programs. Includes target audience persona, competitive landscape, channel prioritization with budget allocation, content direction, and a 90-day action plan. Works for any e-commerce platform — Shopify, Amazon, Etsy, WooCommerce, TikTok Shop, and more. No API key required.
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。
Etsy Ads strategy — budget allocation, bid management, promoted listings, offsite ads opt-out analysis
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。
Use when driving a web page in a browser — clicking, typing, navigating, logged-in or JS-rendered pages. Jev picks each step from the elements observed, under a host allowlist and a step budget.
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 a business plan from the perspective of the solo founder who has to actually execute it. Asks the brutal operational questions: Can I build this with my time budget? Can I keep it running? Will my cashflow survive? Outputs a verdict — SHIPPABLE NOW / SHIPPABLE WITH DESCOPE / NEEDS HIRE / WRONG STACK. Use when a plan looks great on paper and you want to know whether a solo operator can actually execute it. Triggered by: 'operator review', 'can I actually do this solo', 'execution reality check', '操盘视角', '我能不能干得动'.
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。
Paid advertising automation for Google Ads, Meta Ads, and other ad platforms. Creates campaigns, optimizes budgets, manages keywords, writes ad copy, and tracks ROAS. Leverages third-party ad management skills when available. Use when the user needs ad campaigns, PPC, SEM, paid traffic, or says 'run ads', 'Google Ads', 'Meta Ads', 'Facebook Ads', 'ad campaign', 'PPC', 'ROAS', or 'paid traffic'.
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。
Define and check simple service-level objectives for Claude Code from Agent Monitor data — session completion rate, tool success rate (PostToolUse/PreToolUse), and error rate (APIError/total) — then compare each to its target and report the error budget remaining. Use when reporting reliability or when someone asks "are we meeting our SLOs?".
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。
Break down Claude Code costs using the Agent Monitor pricing engine. Shows per-model costs (input, output, cache_read, cache_write at $/Mtok rates), per-session costs, daily trends, and compaction baseline token recovery. Use when analyzing spending, comparing model costs, or planning budgets.
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。
When the user wants to improve crawlability, fix orphan pages, or optimize site structure for search engines. Also use when the user mentions "crawlability," "crawl budget," "orphan pages," "internal links," "site structure," "site crawlability," "infinite scroll," "pagination," "masonry SEO," "AI crawler optimization," "GPTBot crawlability," "ClaudeBot crawlability," or "content not indexed." For internal links, use internal-links.
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。
Use when a Unity 6.3 game is slow or stutters: low fps, frame spikes, hitches when entering areas or on first effects, GC spikes, too many draw calls or SetPass calls, CPU vs GPU bound questions, Profiler, Profile Analyzer, Memory Profiler, Frame Debugger or Project Auditor work, GPU Resident Drawer or SRP Batcher setup, LOD and Mesh LOD, shader warm-up and variants, memory or VRAM budgets, IL2CPP and build size, or proving a fix with numbers from a player.
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。
Use when a Unity 6.3 game targets Android or iOS: phone frame budget, overheating after 20 minutes, 'my game stutters on phones', ASTC or ETC2 textures, audio import, URP mobile settings, touch controls, on-screen joystick, UI under the notch or safe area, slow startup, ANRs, 'Android build fails', AAB, keystore signing, target API 36, Google Play upload, iOS Xcode export or archive, privacy manifest, app size, in-app purchases (IAP 5), rewarded ads (LevelPlay), ad consent.
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。
Use when making or fixing real-time VFX in Unity 6.3: explosions, fireballs, projectiles with muzzle, trail and impact, spells, slashes, shockwaves, smoke, sparks, Particle System (Shuriken) or VFX Graph ('VFX Graph explosion', 'particles look bad', 'effect kills fps on mobile', 'too much overdraw'), mesh effects with scrolling textures, an uber VFX shader with LUT ramps, Custom Data and custom vertex streams, flipbooks and VFX textures, pink particles after URP upgrade, VFX budgets and readability.
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。
Use when an Unreal Engine 5.8 project misses its frame rate or stutters: "optimize to 60 fps", "we run at 38 fps", CPU or GPU bound, game or render thread too long, hitches, traversal or shader stutter (PSO), GC spikes, camera-cut spikes, Lumen, Nanite, VSM, ray tracing, foliage, crowds or Niagara too expensive, async compute, stat unit, ProfileGPU, Unreal Insights, TraceQuery, snapshothitches, scalability and device profiles, dynamic resolution and TSR, streaming budgets, or proving a fix with before and after captures.
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。
Use when a UE5 task involves characters in motion: "import FBX from Maya" with a skeleton, "retarget animations", IK Rig, IK Retargeter, feet sinking or floating after retarget, mocap cleanup, Motion Matching, Pose Search, Choosers, GASP, state machine vs motion matching, root motion, offset root bone, foot IK, Control Rig, Animation Blueprint cost, "animation is expensive", budget allocator, URO, MetaHuman LODs, ML Deformer, cloth.
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。
Use when a Godot game stutters, drops frames, hitches when entering new areas, overheats a phone, or must ship: profiling and Performance monitors, CPU or GPU bound, draw calls, MultiMesh, occlusion culling, LOD, shader compilation hitches, export to macOS, Windows, Linux, Steam, Android (APK, AAB, keystore), iOS (Xcode), web (itch.io, COOP/COEP headers, SharedArrayBuffer, JavaScript interop), build size budgets, demo and DLC builds, signing and notarization.
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。