This skill should be used when the user asks to "calculate iryubun", "遺留分侵害額", "遺留分", "遺留分侵害額請求", "遺贈", "生前贈与の遺留分算入", or wants deterministic computation of reserved-portion infringement under 民法 1042-1048.
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。
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概要と使いどころ
This skill should be used when the user asks to "calculate iryubun", "遺留分侵害額", "遺留分", "遺留分侵害額請求", "遺贈", "生前贈与の遺留分算入", or wants deterministic computation of reserved-portion infringement under 民法 1042-1048.
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 this skill whenever the user asks to inspect JoCoHunt, 조코헌트, jocohunt.jocoding.io products, ideas, rankings, makers, public security headers, or wants a CLI workflow for the Korean builder community. It runs the local `jocohunt` Go CLI, including authenticated product submission when explicitly requested.
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。
Transform transcripts, conversations, or raw notes into shareable content — either multiple social posts OR a single long-form article. Learns style from user's published content. Handles transcripts of any length (tested up to 5500+ lines / 90+ min). Use when user: (1) provides transcript/conversation/recording and wants social content, (2) asks to "turn recording/notes into posts", (3) mentions generating content from recordings/notes, (4) needs multiple publishable pieces from single source, (5) asks to write an article from a transcript — e.g. "write an article/long-form", "organize into an article", "write up this session", "turn this talk into a blog post", "deep dive". Social mode outputs 2-15 ready-to-publish pieces. Article mode outputs a structured long-form piece (3000-10000+ chars) written section-by-section.
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。
This skill should be used when the user asks to "优化简历", "resume optimization", "简历诊断", "tailor resume to JD", "改简历", "resume review", "简历优化助手", or when the user mentions "校招", "社招", "ATS", "JD匹配", "Word批注", shares a .docx resume path alongside a job description, or wants to match their resume to a specific job posting. Also triggers on "resume expert", "HR feedback", "interview prep with resume".
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。
MUST USE whenever the user wants to automate a real desktop or sandbox - clicking, typing, scrolling, screenshotting, running an OS shell command, or handing a high-level 'open browser and do X' task to an autonomous computer-use agent. Wraps the trycua/cua Python toolkit via its `cua` CLI - pynput-based, cross-platform (macOS / Linux / Windows). NO custom tools are registered; you call `cua` through pi's built-in bash and read screenshots back through the Read tool. Triggers: cua, computer use, computer-use, GUI automation, screenshot the desktop, click on the screen, type into the active app, scroll the page, control my computer, drive my browser, sandbox, docker sandbox, QEMU sandbox, Lume sandbox, ComputerAgent, cua do, cua sandbox, 컴퓨터 자동화, 스크린샷 찍어, 내 컴퓨터 조작, 브라우저 열어서, 샌드박스, 화면 자동화, 마우스로 클릭, 키보드 타이핑, computer use 위임, 자동으로 클릭, 자율 에이전트로 처리.
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。
遛狗不淋雨 — a self-evolving agent with a trustworthy judge. The agent decides each day which hour to walk the dog from a playbook (遛狗手册), real Open-Meteo weather judges it, and the agent may only PROPOSE playbook changes as Busabase ChangeRequests that a human merges. Use when the user invokes $kelly-dog-walk, asks 今天几点遛狗 / 遛狗不淋雨, wants to replay a city and month of walk decisions against real weather, run a monthly review, propose or roll back a playbook rule, or demo 风变第二期第 10 节 《打造自我进化的 Agent》. Never writes the playbook directly and never edits the judge.
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。
Pushes interfaces past conventional limits with technically ambitious implementations — shaders, spring physics, scroll-driven reveals, 60fps animations. Use when the user wants to wow, impress, go all-out, or make something that feels extraordinary.
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。
Performs a final quality pass fixing alignment, spacing, consistency, and micro-detail issues before shipping. Use when the user mentions polish, finishing touches, pre-launch review, something looks off, or wants to go from good to great.
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。
Improve layout, spacing, and visual rhythm. Fixes monotonous grids, inconsistent spacing, and weak visual hierarchy. Use when the user mentions layout feeling off, spacing issues, visual hierarchy, crowded UI, alignment problems, or wanting better composition.
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。
Audits and realigns UI to match design system standards, spacing, tokens, and patterns. Use when the user mentions consistency, design drift, mismatched styles, tokens, or wants to bring a feature back in line with the system.
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。
Diagnoses and fixes UI performance across loading speed, rendering, animations, images, and bundle size. Use when the user mentions slow, laggy, janky, performance, bundle size, load time, or wants a faster, smoother experience.
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。
Tones down visually aggressive or overstimulating designs, reducing intensity while preserving quality. Use when the user mentions too bold, too loud, overwhelming, aggressive, garish, or wants a calmer, more refined aesthetic.
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 typography by fixing font choices, hierarchy, sizing, weight, and readability so text feels intentional. Use when the user mentions fonts, type, readability, text hierarchy, sizing looks off, or wants more polished, intentional typography.
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。
Add strategic color to features that are too monochromatic or lack visual interest, making interfaces more engaging and expressive. Use when the user mentions the design looking gray, dull, lacking warmth, needing more color, or wanting a more vibrant or expressive palette.
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 technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality review.
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。
Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.
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。
Amplify safe or boring designs to make them more visually interesting and stimulating. Increases impact while maintaining usability. Use when the user says the design looks bland, generic, too safe, lacks personality, or wants more visual impact and character.
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。
Interacts with live Hunk diff review sessions via CLI. Inspects review focus, navigates files, hunks, and exact lines, reloads session contents, adds inline review comments, and paints attention marks on character ranges. Use when the user has a Hunk session running or wants to review diffs interactively.
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。
Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.
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 and run knowledge distillation (on-policy, off-policy, or multi-teacher) from a teacher model to a student model using the Tinker API. Use when the user wants to distill knowledge, compress models, or train a student from a teacher.
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。
Scaffold a new training recipe for the Tinker cookbook following repo conventions. Use when the user wants to create a new recipe, training script, or experiment.
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 and run supervised fine-tuning (SFT) on instruction or chat datasets using the Tinker API. Use when the user wants to do instruction tuning, chat fine-tuning, or supervised learning.
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 and run the full RLHF pipeline (SFT, reward model training, RL from reward model) using the Tinker API. Use when the user wants to do RLHF, train a reward model, or run the full preference-based RL pipeline.
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 and run multi-turn RL training for interactive environments (terminal tasks, tool use, search/RAG, games) using the Tinker API. Use when the user wants multi-turn RL, agentic training, tool-use RL, or interactive environment training.
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。