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Conduct comprehensive code reviews identifying bugs, security issues, performance problems, code quality concerns, and best practice violations. Use when reviewing pull requests, examining code changes, evaluating new code, assessing code quality, or providing feedback on implementations. Analyzes code for correctness, security vulnerabilities, performance bottlenecks, maintainability issues, test coverage, documentation quality, and adherence to coding standards. Produces structured markdown reviews with categorized findings, severity ratings, specific examples, and actionable recommendations. Triggers when users ask to review code, check pull requests, evaluate implementations, find bugs, or assess code quality.

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Debug proof failures using counterexamples from Nitpick (Isabelle) or QuickChick (Coq) to identify specification errors, missing preconditions, and proof strategy issues. Use when: (1) A proof attempt fails and you need to understand why, (2) Counterexamples are generated by Nitpick or QuickChick, (3) Specifications may be incorrect or incomplete, (4) Theorems need validation before proving, (5) Missing preconditions or lemmas need identification, or (6) Proof failures need explanation and correction suggestions. Supports both Isabelle/HOL and Coq equally.

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Generate Dockerfiles, Docker Compose configurations, and Kubernetes manifests for containerizing applications. Use when: (1) Creating Dockerfiles for Node.js, Python, Java, Go, or other applications, (2) Setting up multi-service environments with Docker Compose, (3) Generating Kubernetes deployments, services, and ingress configurations, (4) Optimizing container images for production, (5) Implementing containerization best practices. Provides both ready-to-use templates and custom-generated configurations based on project requirements.

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Use this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.

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。

Analyze and improve business processes. Trigger with "this process is slow", "how can we improve", "streamline this workflow", "too many steps", "bottleneck", or when the user describes an inefficient process they want to fix.

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 professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server. Use this skill whenever the user asks for a tear sheet, company one-pager, company profile, fact sheet, company snapshot, or company overview document — especially when they mention a specific company name or ticker. Also trigger when users ask for equity research summaries, M&A company profiles, corporate development target profiles, sales/BD meeting prep documents, or any concise single-company financial summary. This skill supports four audience types: equity research, investment banking/M&A, corporate development, and sales/business development. If the user doesn't specify an audience, ask. Works for both public and private companies.

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Implement complete dark mode for any frontend project. Auto-detects theme system (Flutter ThemeData, Tailwind dark class, CSS variables, MUI, Chakra UI, styled-components), generates a dark color palette with proper surface elevation hierarchy and desaturated brand colors, creates a theme toggle with system preference detection and persistence, migrates all hardcoded colors to theme tokens, handles special cases like images, shadows, form inputs, and charts, then verifies WCAG AA contrast ratios for both modes. Use when you need to add dark mode, fix dark theme contrast issues, create a theme switcher, or migrate hardcoded colors to theme-aware tokens.

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Use when the user drops or points at a local talking-head video and wants the kinetic supers + rapid camera reposition prompt for video-to-video AI (one real take turned into split-second camera snaps that lock off, with bold motion-graphics text synced to the speech). Triggers on /kinetic-multicam, "kinetic supers", "kinetic typography video", a dropped .mp4 plus a motion-graphics-text + camera request. PT examples for reliability, "faz o prompt de kinetic supers desse vídeo", "gera supers cinéticos com cortes de câmera", "bota motion graphics e câmera nesse take".

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Overlay doctrine for the embedded-captions workflow — the caption MODEL (drop / rail / embed) and the rule that captions are an OVERLAY composited on top of the film, never a reserved bottom band you shift content up to avoid. Load when adding captions/subtitles to a talking-head or launch video, when deciding whether a phrase should be dropped, ride the verbatim rail, or be promoted to a scarce embedded climax, when laying out a composition that will carry captions (do NOT reserve a keep-out band), or when centering a composition on the true frame center under captions. Quotes the rail+embed model from embedded-captions and constraint

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Estimate market size using TAM, SAM, and SOM with top-down and bottom-up approaches. Use when sizing a market opportunity, estimating addressable market, preparing for investor pitches, or evaluating market entry.

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Build production-ready LLM applications, RAG systems, and intelligent agents. Use when implementing AI features, chatbots, vector search, or agent orchestration.

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Map this codebase into project memory — a code graph of every symbol and how they connect, plus a written profile of stack, layout, conventions and workflows. Re-run any time to refresh both in place. Use when memory is newly installed on an existing project, or when the project has changed enough that the stored map is stale.

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Use when auditing a product, business, or project ecosystem — analyzing data sources, decision loops, bottlenecks, and implementation contours. Triggers on "аудит продукта", "product audit", "data audit", "аудит данных", "аудит бизнеса", "проанализируй экосистему", "аудит систем".

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Робот агента Personal Corp. Агент делает себе голову робота из набора деталей, хранит её в штабе в stack/avatars.json и кладёт в штаб страницу avatar/robot.html, где робот стоит за партой своего штаба и вокруг видны отделы, без сервера и интернета. На уроке по ссылке входа с экрана урока робот входит в живой класс и горит маячком, пока агент работает. Триггеры /avatar, /avatar <ссылка входа>, сделай себе аватар, покажи своего робота, приди на урок.

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。

Web performance audits with Core Web Vitals, bottleneck identification, optimization recommendations. Use for page load times, performance reviews, UX optimization, or encountering LCP, FID, CLS issues, resource blocking, render delays.

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。

Nuxt Content v3 Git-based CMS for Markdown/MDC content sites. Use for blogs, docs, content-driven apps with type-safe queries, schema validation (Zod/Valibot), full-text search, navigation utilities. Supports Nuxt Studio production editing, Cloudflare D1/Pages deployment, Vercel deployment, SQL storage, MDC components, content collections.

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GitHub repository automation (CI/CD, issue templates, Dependabot, CodeQL). Use for project setup, Actions workflows, security scanning, or encountering YAML syntax, workflow configuration, template structure errors.

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This skill should be used when the user asks to "add turnstile", "implement bot protection", "validate turnstile token", "fix turnstile error", "setup captcha alternative", or encounters error codes 100*/300*/600*, CSP errors, or token validation failures. Provides CAPTCHA-alternative protection for Cloudflare Workers, React, Next.js, and Hono.

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Firecrawl v2.5 API for web scraping/crawling to LLM-ready markdown. Use for site extraction, dynamic content, or encountering JavaScript rendering, bot detection, content loading errors.

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。

Type-safe Hono APIs with routing, middleware, RPC. Use for request validation, Zod/Valibot validators, or encountering middleware type inference, validation hook, RPC errors.

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Dependency upgrades: outdated/vulnerable deps planned into semver waves (patch/minor batched, majors gated and changelog-read), applied incrementally with lockfiles regenerated and tests green after each. Runs on green main. Triggers on "absolute upgrade", "upgrade our dependencies", "bump deps", "update packages", "move off the deprecated X", "clear the Dependabot backlog".

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。

Direct, build and render premium cinematic motion design from code — launch films, product and feature videos, looping feature animations for landing pages and social, logo stings and reveals, UI walkthroughs, app demos, kinetic type, teasers, trailers, intros and promo clips. Use this whenever the user wants any video, animation, motion graphic or animated demo made with Remotion, HyperFrames, HTML/GSAP or React, or asks to storyboard, time, score, render, polish or critique one — even if they only say "make a video about X", "animate this feature" or "we need a launch clip". It supplies concept and copy discipline, type and colour taste that defers to a supplied brand, a beat-locked timeline that drives both picture and a synthesized soundtrack, choreography and transition craft, real product UI in motion, film-grade finishing (true motion blur, no banding, BT.709, sync-checked mux) and a measured self-critique loop on contact sheets and renders.

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Remove AI-generated writing patterns from rebuttal prose to make it sound natural, direct, and authentically human-authored. Use when a Stage 2 refined draft or Stage 4 follow-up response reads too formulaic, robotic, or "GPT-like". Supports academic English.

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Think and work like an expert Knowledge Representation Researcher. Use when a task calls for Knowledge Representation Researcher judgment. Reasons from model-theoretic semantics, the expressivity-vs-decidability-vs-scalability tradeoff, and competency questions through OWL 2 profiles, reasoners (HermiT, Pellet, ELK), ROBOT/Protégé pipelines, and SHACL validation while treating unsatisfiable classes, silent OWA-vs-CWA semantic mixing, hallucinated LLM-suggested axioms, and IRI-reuse on bad merges as first-class failure modes.

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。