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概要と使いどころ
Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.
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Remove the discourse-level (structural) signs of AI writing that survive surface editing: stated lessons and moral-of-the-story closers, tidy single-track arcs, embodied-emotion performance ("chest tightened"), vague allusions instead of named references, unbroken linear structure, and shape convergence across pieces. Grounded in the StoryScope study (Russell et al. 2026): narrative structure alone detects AI text at 93.2% F1, and professional stylistic rewriting moved detection only 1.6 points. Use as the SECOND pass after the humanizer skill (which handles words and phrasing) whenever writing or revising LinkedIn posts, course lessons, blog posts, essays, newsletters, or emails that must read as human. Triggers: "humanize", "de-slop", "AI tells", "make this sound human", "structural pass", "deep humanize".
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Provides patterns and code examples for building Next.js 16+ applications with App Router architecture. Use when creating projects with App Router, implementing Server Components and Client Components ("use client"), creating Server Actions for forms, building Route Handlers (route.ts), configuring caching with "use cache" directive (cacheLife, cacheTag), setting up parallel routes (`@slot`) or intercepting routes, migrating to proxy.ts, or working with App Router file conventions (layout.tsx, page.tsx, loading.tsx, error.tsx).
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How to find and fix performance problems in Shift's desktop app. Use when something is slow, choppy, janky, or laggy (scrubbing, dragging, editing, undo, opening fonts), when profiling or measuring, when adding or reviewing perf tests, and before claiming a change made something faster.
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 writing a complete SEO article. Includes the full anti-AI-slop ruleset (banned vocabulary, banned phrases, banned structural patterns) and voice rules. The agent researches the SERP itself if needed — no keyword data exports 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。
Use when rewriting or refreshing an existing page that's underperforming. The agent fetches the URL, analyzes the current content, researches the SERP, and rewrites using the full anti-AI-slop ruleset — no data exports needed.
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。
Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model. Use when asked to cut AI inference cost or latency, when scoping a performance engagement for a client, when reviewing an agent loop that feels slow, or when asked "where could we use Jev here". Produces a ranked table of candidates with measured latency and dollar deltas.
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。
Profile and optimize application performance including load times, memory usage, and rendering. Use when debugging slow performance, memory leaks, or optimizing app speed.
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。
Audit your calendar with Cowork's calendar tools -- measure meeting load and fragmentation, identify which recurring meetings earn their slot, propose consolidations and focus blocks, and draft the diplomatic messages that reclaim your week.
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Writes, explains, debugs, and optimizes BI calculations - Power BI / Fabric DAX measures and calculated columns, Tableau calculated fields (FIXED/INCLUDE/EXCLUDE LOD expressions, table calculations), and Looker LookML measures, dimensions, and derived tables. Use it whenever the request involves DAX, CALCULATE, a Power BI measure, a Tableau calc or LOD, LookML, or a business metric such as YoY, YTD, rolling 12 months, running total, percent of total, distinct customers, ranking, new vs returning customers, closing balances, or basket analysis. Use it especially when someone says a measure "shows the wrong total", "is blank", "ignores my slicer/filter", "repeats the same value on every row", or "is slow" - those are filter context, context transition, LOD order-of-operations, or fanout bugs this skill is built to diagnose. Produces the formula, the context it evaluates in, and a hand-checkable test with expected numbers.
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Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.
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。
Profiles Python code for performance bottlenecks and memory issues. Use when Python code is slow or when profiling for optimization before a release.
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。
Evaluates and improves skills, agents, commands, and hooks after a workflow slice. Use when execution felt slow, confusing, repetitive, or fragile.
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。
Updates documentation after code changes with quality gates, slop detection, and accuracy checks. Use when code changes require corresponding doc updates.
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。
Improves code quality across duplication, efficiency, and architectural fit. Use when code passes tests but quality is poor or before a major release.
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。
Detects time and space complexity hotspots via AST scan. Use when code feels slow, before performance-sensitive merges, or to find O(n²) regressions.
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Applies Functional Core, Imperative Shell to isolate logic from side effects. Use when business logic is entangled with I/O or unit tests are slow and brittle.
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用杜蕾斯黄金期（2011-2017 环时互动）那套「双层语义 + 留白」的方法产出文案与海报。先识别用户意图并给 3-5 个方案供选，再出短文案（长文案可选）+ 五种比例配图。适用于借势热点、节日节气、产品卖点、课程/知识付费推广、自媒体标题与封面、品牌人格化运营。当用户说「写个文案」「借势热点」「节日海报」「想句 slogan」「课程怎么推」「公众号/小红书封面」「像杜蕾斯那样写」「一语双关」时使用。
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。
Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.
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。
Remove signs of AI-generated writing from prose, and keep it out of a repo. Use when drafting, editing, or reviewing text to make it sound natural and human, or when auditing a whole docs folder for AI slop. Detects 41 patterns across content, language, style, communication, filler, and rhetoric, including: significance inflation, promotional language, -ing tails, vague attributions, AI vocabulary, copula avoidance, negative parallelisms, false agency, em dash overuse, chatbot artifacts, hedging stacks, staccato drama, and aphorism formulas. Includes voice calibration, a no-fabrication rule, statistical tells, and a draft -> audit -> final rewrite loop.
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Converges an underspecified request into a typed IntentSpec before planning or acting. Use when the user asks to implement, add, change, refactor, fix, migrate, configure, handle, triage, sort out, look into or decide something and the request leaves decisions open (which objects, which approach, what happens on failure, which trade-off), contradicts itself, admits two readings, or rests on an approach its sources may rule out — in a codebase, a ticket queue, a research brief or a runbook — or when the user says "clarify the intent", "what do you need from me", or invokes intent-router. Looks up what its sources hold (code, history, decision records, a ticket log, an order record, the policy in force) before asking; asks only preference or irreversible questions, one at a time, with a recommended default; halts instead of guessing; checks the delivered work against the spec. Silent on a fully stated task its sources do not contradict. Not for explaining existing state ("what does X do", "why is Y slow").
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。
Generate web assets including favicons, app icons (PWA), and social media meta images (Open Graph) for Facebook, Twitter, WhatsApp, and LinkedIn. Use when users need icons, favicons, social sharing images, or Open Graph images from logos or text slogans. Handles image resizing, text-to-image generation, and provides proper HTML meta tags.
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。
Plan, diagnose, and verify performance budgets, latency targets, load tests, capacity estimates, bottleneck analysis, caching strategy, query efficiency, queue throughput, and regression gates. Use when a feature may be slow, a system must scale, a performance regression is suspected, or release readiness depends on throughput, cost, memory, CPU, or response time.
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。
Diagnose slow or freezing VS Code-compatible editors with evidence-first, zero-hardcoded-assumption workflow. Use when the user reports editor lag, typing delay, UI freezes, extension host stalls, file watcher noise, high editor CPU/RSS, uses VS Code/Cursor as a file browser over a large folder, or wants a safe editor performance audit.
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。