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概要と使いどころ
Create and manage tickets — initiatives, epics, stories, bugs. Use when the user says "create a new initiative", "slice this initiative", "split this epic up", "break this into stories", "incept this epic", "make a ticket", "refine this ticket", "what's ready", "status of a story", "publish ticket changes", "validate these tickets", "set up the ticket store".
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Reviews HealthKit code for authorization patterns, query usage, background delivery, and data type handling. Use when reviewing code with import HealthKit, HKHealthStore, HKSampleQuery, HKObserverQuery, or HKQuantityType.
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。
RIPDPI's own Compose conventions: ViewModel pattern, Route/Screen split, DataStore->StateFlow, RipDpiThemeTokens. Use for how this app does Compose, not generic Compose API questions (see compose).
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。
Query Lore, the librarian agent, to search the owner's personal knowledge base. Use when looking up stored documents, personal records, insurance, manuals, notes, vehicles, or any "do I have a document about X?" type question.
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Preflight that resolves which private key(s) to use for the warp-deploy chain across every protocol in the route (EVM / Sealevel / Cosmos / Starknet / Tron). For each protocol the user supplies either a specific key spec (GCP secret name, env var, or keystore path) or a candidate list of GCP secret names for the agent to surface and the user to pick. The agent never enumerates GCP secrets on its own and never reads secret values without an explicit user-approved pick. Persists the resolution to `~/.hyperlane/key-contexts/ticket-id.yaml` for downstream warp-deploy skills to auto-load.
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。
Feedback synthesis — cluster support tickets, NPS verbatims, app store reviews, and churn surveys by theme, separate signal from noise, and produce an actionable insight report. Use when asked to "synthesize this feedback", "analyze support tickets", "what are users complaining about", "NPS analysis", "churn feedback synthesis", or "what's the feedback telling us".
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。
Expert guide for e-commerce architecture (Shopify Storefront, Medusa.js, Saleor), product catalogs, cart/checkout UX, and order management / Panduan ahli arsitektur e-commerce (Shopify, Medusa.js, Saleor), katalog produk, UX keranjang/checkout, dan manajemen pesanan.
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。
Expert guide for Angular 19+ enterprise applications — Signals, Standalone Components, NgRx SignalStore, SSR with Hydration, and Angular Material 3 / Panduan ahli aplikasi enterprise Angular 19+.
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。
Generates scheduled background jobs using Quartz.NET. Includes job definitions, triggers, cron scheduling, dependency injection, and persistent job store configuration.
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 a viewer, configure a viewer, change viewer options, find viewer, close viewer, view a scatter plot, bar chart, histogram, line chart, box plot, pie chart, heat map, correlation plot, 3D scatter, trellis, density plot, statistics, on a Datagrok TableView via the datagrok_exec tool. Use whenever the user asks to plot, chart, visualize, show a graph, draw a distribution, color by a column, swap a viewer's axis, toggle a legend / regression line / log scale, replace one viewer with another, close every chart, reset the view to just the grid, or find an existing viewer by type. Plugin viewers like "Chem space", "sequence space", "activity cliffs" are NOT viewer types — they're registered functions — route those to `grok.functions.call`. Does NOT cover filtering (separate skill `datagrok-filtering`), selection (`datagrok-selection`), grid cell rendering (`datagrok-grid-customization`), layout save/restore, or custom-viewer authoring.
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。
直近 1 週間の売上・廃棄・欠品・施策（Cowork から登録した発注を含む）を Dataverse から集計し、SV（スーパーバイザー）向けの週次店舗レポートを、チャットのグラフ（Render UI）と表、HTML レポート（頼まれたら Excel も）で作るスキル。 Use when ユーザーが「週次レポートを作って」「SV に今週の報告をまとめて」「今週の振り返りを Excel にして」と依頼したとき。 Dataverse MCP コネクタ（describe / read_query）を読み取りで使う。
Understanding and managing what digital services collect, store, share, and infer about you. Covers password security and entropy, multi-factor authentication, privacy settings, data minimization, the difference between first-party and third-party tracking, cookies and fingerprinting, privacy laws (GDPR, CCPA), and how to respond to data breaches. Use when helping a learner make informed decisions about what to share, with whom, and under what terms.
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MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.
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。
Instruction dispatch pipeline for routing work items to available agents. Implements a 7-stage fetch-allocate-prepare-hook-store-launch-confirm pipeline with batch convoy mode, formula expansion, idempotent dispatch, and crash recovery.
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。
Image generation, photo editing, background removal — transparent PNG images, cutouts, ecommerce packshots, product catalogs, lifestyle shots via Bria.ai. Build store-ready e-commerce catalogs at scale — shadows, product dimensions images, lifestyle scenes, marketplace listing variants. ALWAYS use this skill when the user wants to generate, edit, or transform any image — hero images, banners, social media visuals, product photos, illustrations, icons, thumbnails, ad creatives, or marketing materials — or to build a product catalog, create a packshot, stage a lifestyle shot, remove a background, make transparent PNGs, or batch-process product photos. Triggers on packshot, product catalog, product shot creator, lifestyle shot, cutout, product dimensions image, inpainting, outpainting, object removal, upscale, photo restoration, style transfer, relight, reseason, sketch-to-photo. Commercially safe, royalty-free.
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 MDMA into an application and build features with it — wire up parsing, the runtime store, React rendering, LLM streaming, custom components, prompts, and CI validation. Use this skill when the user asks to add MDMA to an app, build a chat that streams MDMA, author or maintain a custom prompt, validate MDMA documents, register a custom component, or expose MDMA to an agent via MCP. Generates focused, correct wiring that uses the right packages for the job instead of reinventing them.
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。
Build and navigate DSP (Data Structure Protocol) — graph-based long-term structural memory of codebases for LLM agents. Stores entities (modules, functions), their dependencies (imports), public API (shared/exports), and reasons for every connection. Use when: (1) project has a .dsp/ directory, (2) user asks to set up DSP or bootstrap project structure, (3) creating/modifying/deleting code files in a DSP-tracked project, (4) navigating project structure, understanding dependencies, or finding modules, (5) user mentions DSP, dsp-cli, .dsp, or structure mapping.
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 an application needs to store config, data, cache, or state files. When designing where user-specific files should live. When code writes to ~/.appname or hardcoded home paths. When implementing cross-platform file storage with platformdirs.
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。
Store instructional prose once and reference it from multiple skills or agents instead of duplicating it. Use when the same steps, rules, or reference material appear in two or more SKILL.md or agent files in one plugin, when deciding whether a shared doc belongs in the plugin-root docs directory or a single skill's references directory, or when tempted to use a symlink or a ../ path to share a file between components.
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。
Register a plan artifact via the MCP backlog server. Use when you produce a document or report that downstream agents or worktree-isolated environments need to retrieve — feature-context, codebase-analysis, architect, T0-baseline, TN-verification, or research artifacts. Triggers include "store an artifact", "register a plan artifact", "write a report to the backlog", "upload artifact content".
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。
How to determine which backlog backend a project is configured to use, and what to do with the answer. Use when about to call a backend-specific tool, choosing between a GitHub-only and a Beads-native operation, or reporting which store a result came from.
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。
Modern Git workflows, best practices, and commands. Use when the user asks about Git branch management (git switch vs checkout), file restoration (git restore), fixup commits, autosquash rebasing, git worktrees, rerere, force-with-lease, repository cleanup (git clean, stale branches, bloat analysis), history navigation (revision syntax, range notation, pickaxe search), recommended global git config, or GitButler and the but CLI.
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 about "plugin settings", "store plugin configuration", "user-configurable plugin", ".local.md files", "plugin state files", "read YAML frontmatter", "per-project plugin settings", or wants to make plugin behavior configurable. Documents the .claude/plugin-name.local.md pattern for storing plugin-specific configuration with YAML frontmatter and markdown content.
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。
Trace every user-facing button/touchpoint through its full state change sequence to find bugs where functions individually work but cancel each other out, produce wrong final state, or leave the UI in an inconsistent state. Use when: systematic debugging found no bugs but users report broken buttons, or after any major refactor touching shared state stores.
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。