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CLAUDE.mdを分割ルールファイル(.claude/rules/)で管理するスキル。学習トライアドの書き込み先を分割ファイルにリダイレクトし、CLAUDE.mdには目次だけを生成する（本文は .claude/rules/ を Claude Code が自動で読み込む）。/rules-init, /rules-merge, /rules-add, /rules-status コマンドを提供。CLAUDE.mdが肥大化している場合や、学習トライアドの蓄積を分割管理したい場合に使用。
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Implements SwiftUI Views from Figma designs using Figma MCP tools (figma:get_design_context, figma:get_screenshot, figma:get_variable_defs, figma:get_metadata). Splits large designs into appropriately-sized components, respects existing codebase conventions, and adds previews for each component. Use when implementing SwiftUI from Figma design URLs (e.g., figma.com/design/...), converting designs to code, or building iOS UI from Figma mockups. Do NOT call Figma MCP tools directly without reading this skill first.
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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.
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Use when the user wants to rename, extract, split, move, or restructure code safely. Examples: "Rename this function", "Extract this into a module", "Refactor this class", "Move this to a separate file"
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Classic block-game inspired design with playful colors, bold display fonts, and compact, high-energy layouts.
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A playful, energetic, cartoonesque interface inspired by friendly children's-book illustrations — warm cream backgrounds, big bold custom display typography, saturated brand color blocks, thick black outlines, generously rounded shapes
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A friendly, hand-drawn sketch interface inspired by pencil illustrations on warm cream paper. Soft teal brand accents, hand-written display headings, rounded pill controls.
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A sun-baked, clay-toned editorial interface built on warm cream surfaces, ink-brown headlines set in a display serif, and a single terracotta accent.
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Chinese-first AI image-generation tool landing template — TwelveMei-style centered hero with floating icon-island nav, FlyFlowerSong serif display headline, textured pill CTAs, glass canvas mockup of the tool itself, monochrome ink palette. Built for Chinese creative-tool / generative-image / AI-工具 product pages aimed at designers and editors, not loud growth-marketing.
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Magazine-grade, collage-driven editorial system: warm paper canvas, surreal plaster-and-architecture imagery, oversized display type, hairline rules, and Roman-numeral section markers.
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Long-form Chinese article / blog-post template — two voices: a wildstay-style lifestyle launch piece with editorial grid + oversized display type, and a WeChat-公众号-style serif beauty/longform with measured prose and quiet hairlines.
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Process, parse, create, and manipulate PDF documents using libraries like pdf-lib, PDFKit, or pdf-parse for document generation, data extraction, and PDF manipulation. Use when generating PDFs from HTML, extracting text from PDFs, merging or splitting PDFs, adding watermarks, filling PDF forms, creating invoices and reports, parsing legal documents, or building document management systems.
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When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," or "how long should I run this test." Use this whenever someone is comparing two approaches and wants to measure which performs better. For tracking implementation, see analytics-tracking. For page-level conversion optimization, see page-cro.
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Triage AEM Workflow issues on AEM as a Cloud Service by classifying symptoms, gathering the right logs and metrics, and mapping to runbooks or Splunk searches. Use when the user asks for workflow activity/errors on a Cloud Service host, needs to classify a Jira ticket, or wants to know what to collect for workflow debugging.
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Explain Ponytail's intended tradeoffs and measurement boundary. It does not estimate code, cost, or speed savings for the current repository. One-shot display, not a persistent mode. Trigger: ponytail-gain, "ponytail gain", "what does ponytail save", "show ponytail impact", or "ponytail scoreboard".
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Show ponytail's measured impact as a compact scoreboard: less code, less cost, more speed, from the benchmark medians. One-shot display, not a persistent mode, and not a per-repo number. Trigger: /ponytail-gain, "ponytail gain", "what does ponytail save", "show ponytail impact", "ponytail scoreboard".
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Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
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Quick-reference card for all ponytail modes, skills, and commands. One-shot display, not a persistent mode. Trigger: /ponytail-help, "ponytail help", "what ponytail commands", "how do I use ponytail".
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Check the health of published EAS Updates: crash rates, install/launch counts, unique users, payload size, and the split between embedded and OTA users per channel. Use when the user asks how an update is performing, whether a rollout is healthy, how many users are on the embedded build vs OTA, or wants to gate CI on update health.
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Plan ATM framework refactors by preserving atom/map semantics before splitting large governance modules.
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Sanguo RAG unresolved mention resolution loop. Use for: 三國 RAG, unresolved mentions, alias review, observed mentions, MCQ triage, manual roster seeds, false-positive exclusion, running run_resolution_loop.py, applying unresolved-triage answers, delegating 三國名詞查證 to a web-capable agent, or Pilot ETL chunk experiments with LangChain text splitters / LangGraph / LangFlow around 張飛長坂橋等小樣本流程。
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Content sanitization and hidden-content detection for agent skill security. Scans markdown, HTML, and text for visually hidden but agent-readable attacks: CSS-hidden text (display:none, color:white, font-size:0, opacity:0), HTML comments with instructions, collapsible details sections, zero-width unicode, homoglyphs, misleading links, and inline HTML in markdown. Enforces mandatory sanitization before external content enters agent context. Load as part of the secure-* sequence during any repo scan or skill audit. Also load for sanitize content, check hidden text, scan markdown attacks, strip HTML, detect invisible instructions, check zero-width chars. Core principle: visibility does not equal influence — hidden content is more dangerous than visible content because agents process it but humans cannot see it.
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Route user requests to the right skill, decompose complex work into parallel subagents, and manage project phase transitions. Load when the user asks "what should I do next", "which skill should I use", "orchestrate this", "run the full workflow", "split this into parallel tasks", or when a complex request spans multiple skills. Also triggers on "coordinate agents", "parallel execution", "task decomposition", "agent workflow", "what phase am I in", or when the user gives a broad instruction that requires multiple skills in sequence. This is the project's brain — it decides what runs, when, and whether to parallelise.
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Critically audit agent skills and remove content that is outdated, disproven, model-specific, or based on poorly cited sources. Load when improve-skills runs its per-skill cycle, when the user asks to prune skills, remove outdated techniques, check if skills are still valid, verify citations in skills, audit skill sources, or update skills for a new model release. Also triggers on "are these skills still valid", "check for obsolete techniques", "verify skill citations", or "update skills for GPT-5/Claude 4/Gemini 2". Runs before split-skill and compress-skill — removing bad content first means the remaining content is worth preserving.
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