小説、シナリオ、ブログ、推敲、校正（1,899 件）
概要と使いどころ
A dated chronology of one topic across the user's recorded coding-agent sessions — the lessons the graph learned about it and the prompts that named it, oldest first. Use only when the user explicitly asks for the history or timeline of a topic across their sessions ("how did X evolve", "when did we first discuss X") or invokes /cognee-timeline. Not for questions about code or the current task.
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。
Apply when Genesis writes as itself — outreach, public content, community posts, emails, DMs. Not for writing in the user's voice (that's voice-master). Activate when Genesis is the author, not the ghostwriter.
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。
Scores team or opt-in individual contribution quality from commit and PR history. Use when building engineering scorecards or coaching; for AI impact use dev-ai-coding-metrics.
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 Monte Carlo's push ingestion model. Use this skill whenever a customer or engineer mentions: pushing data to Monte Carlo, the IngestionService, pycarlo push APIs, build me a collection script, push metadata/lineage/query logs, invocation_id tracing, custom lineage nodes or edges, deleting push tables, or any question about why pushed data is not showing up. Also trigger when they ask to generate code that collects metadata, table schema, row counts, freshness, lineage, or query history from any data warehouse or data source and sends it to Monte Carlo. If the user mentions any warehouse, database, or data platform alongside any Monte Carlo topic, this skill is almost certainly relevant.
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。
Search and ask questions about coding agent session history across Claude Code, Codex, and Cursor. Use when asking what was worked on, what was tried before, how a problem was investigated across sessions, what happened recently, or any question about past agent sessions. Also use when the user references prior sessions, previous attempts, or past investigations — even without saying 'sessions' explicitly.
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。
Implement from a spec or plan: extract stories, build in safe waves, test, commit, and get peer review per story. Use for "implement", "build/code this plan", or targeted fix findings. If no plan exists, use /ship:design first.
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。
Validate that a story file is implementation-ready. Checks for embedded GDD requirements, ADR references, engine notes, clear acceptance criteria, and no open design questions. Produces READY / NEEDS WORK / BLOCKED verdict with specific gaps. Use when user says 'is this story ready', 'can I start on this story', 'is story X ready to implement'.
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。
Orchestrate the narrative team: coordinates narrative-director, writer, world-builder, and level-designer to create cohesive story content, world lore, and narrative-driven level design.
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。
End-of-story completion review. Reads the story file, verifies each acceptance criterion against the implementation, checks for GDD/ADR deviations, prompts code review, updates story status to Complete, and surfaces the next ready story from the sprint.
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。
Fast sprint status check. Reads the current sprint plan, scans story files for status, and produces a concise progress snapshot with burndown assessment and emerging risks. Run at any time during a sprint for quick situational awareness. Use when user asks 'how is the sprint going', 'sprint update', 'show sprint progress'.
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。
Read a story file and implement it. Loads the full context (story, GDD requirement, ADR guidelines, control manifest), routes to the right programmer agent for the system and engine, implements the code and test, and confirms each acceptance criterion. The core implementation skill — run after /story-readiness, before /code-review and /story-done.
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 player-facing patch notes from git history, sprint data, and internal changelogs. Translates developer language into clear, engaging player communication.
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。
Break a single epic into implementable story files. Reads the epic, its GDD, governing ADRs, and control manifest. Each story embeds its GDD requirement TR-ID, ADR guidance, acceptance criteria, story type, and test evidence path. Run after /create-epics for each epic.
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。
Investigating git history, analyzing regression root causes, and performing code archaeology. Time-travels through commits to uncover truth. Use for git history investigation.
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。
Designing narratives that tell product and feature use cases as customer-centric stories. Use when customer experience storytelling, scenario stories, or product narratives are 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。
Authoring Storybook stories, component catalogs, and Visual Regression integration (CSF 3.0/Factories, Storybook 10 ESM-only, React Cosmos). Use when building a component catalog.
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。
Analyzing session replays, extracting persona-based behavioral patterns, and storytelling UX issues. Reads the 'why' from real user operation logs. Works with Field/Echo for persona validation.
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。
Writing and producing product videos: scripts, storyboards, narration, and reproducible Playwright demo recordings. Use for explainers, onboarding, feature walkthroughs, multi-aspect exports, captions, and video quality checks.
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。
Planning releases and reporting delivery work from GitHub PR history. Use when versioning, CHANGELOGs, rollout or rollback plans, engineering metrics, retrospectives, or stakeholder reports are 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。
Detect image copyright infringement risks by comparing against a database of registered copyrighted works with similarity scoring, TRO litigation history, and radar-based infringement assessment. Triggered when users mention copyright detection, image infringement check, copyright risk analysis, TRO risk, copyright similarity search, rights owner lookup, or copyright compliance verification. Even if the user does not explicitly say "copyright," this skill should be triggered whenever the need involves checking whether an image may infringe registered copyright works.
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 Walmart product details via Nexscope, including pricing history and sales trends. Trigger when users mention Walmart product detail, Walmart product data, Nexscope, Walmart price trends, Walmart sales trends, Walmart product detail, Walmart price history, Walmart sales trend, Nexscope product. Even if the user does not explicitly mention "Nexscope", trigger this skill whenever their need involves viewing detailed information, historical price changes, or sales trends for a single Walmart product.
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。
Nexscope Ozon Russia single SKU daily time-series performance. Returns daily sales units, price, stock, rating, and optionally search position/visibility data for one Ozon product by date granularity. Use for validating growth trends, seasonality, and anomaly detection. Trigger when the user mentions Ozon trend, Ozon sales trend, Ozon price trend, Ozon daily data, Ozon stock trend, Ozon search ranking, Ozon product history, Nexscope trend, Ozon daily performance, Ozon time series, Ozon search visibility, Russian marketplace product history. Also trigger when the intent is to view the daily/period trend of an Ozon product, even without explicitly mentioning Nexscope.
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。
Search public Temu products and inspect product details and recent history through Nexscope.
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 Amazon product historical time-series data, including price trends, BSR (Best Sellers Rank) trends, rating changes, seller counts, and monthly sales, supporting any ASIN across multiple Amazon marketplaces. Triggered when users mention price history, price tracking, BSR history, BSR trends, historical pricing, price fluctuations, Nexscope data, rank history, price drop alerts, lightning deal historical prices, Buy Box price trends, coupon prices, FBA/FBM price comparison, seller count changes, rating trends, sales history. Even if users do not explicitly mention "Nexscope" or "time-series data", this skill should be triggered whenever the task involves analyzing Amazon product-level historical data (such as price, rank, or sales trends over 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。