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概要と使いどころ
Build branching dialogue and narrative — a node/choice graph with conditions, variables, and localization hooks — and choose between authoring tools Ink and Yarn Spinner or a custom data-driven runner. Engine-neutral. Use when the user mentions dialogue system, branching dialogue, conversation tree, choices, Ink (.ink), Yarn Spinner (.yarn), or NPC dialogue.
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。
Persist player data in Roblox with DataStoreService: GetDataStore, GetAsync/ SetAsync/UpdateAsync/IncrementAsync wrapped in pcall, load-on-join and save-on-leave plus BindToClose, retries, and OrderedDataStore leaderboards. Use when saving or loading persistent data in a Roblox experience — when the user mentions DataStore, DataStoreService, GetAsync, SetAsync, UpdateAsync, save player data, or leaderboards. For general Luau scripting use roblox-luau.
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 edit tile-based 2D levels in Godot 4.7 with TileMapLayer and TileSet: paint layers, set up collision/navigation/custom-data on tiles, autotile with terrain sets, and read/write cells from code (set_cell, get_cell_tile_data, local_to_map). Use when working with TileMapLayer nodes, .tres TileSets, autotiling, or migrating a deprecated TileMap node to TileMapLayer.
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 natural, controlled prompt variants from a target-blind design brief and prompt architecture while preserving approved jobs, acts, journeys, constraints, roles, locales, proximity bands, and evidence language. Use after architecture design and before contamination or semantic QA.
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 a Google Alerts-style keyword coverage tracker. Uses news-search for recent keyword queries, lets the LLM dedupe and classify real features versus junk, stores decisions in SQLite, and alerts only on new real coverage.
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。
Research any company, product, or service from a URL plus description and build a comprehensive, evidence-bound AEO/GEO/AI-visibility prompt panel across buyer jobs, information acts, journey states, B0-B5 proximity, aided status, roles, locales, variants, partitions, surfaces, and measurement lanes. Use when a user wants prompts to track in ChatGPT, Claude, Gemini, Perplexity, AI search, or answer engines; wants an AI-visibility measurement design; or needs a versioned panel rather than an SEO keyword list.
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。
Turn an approved measurement charter, ICPs, and buyer jobs into a budget-aware prompt coverage blueprint across proximity bands, aided status, information acts, journey states, roles, locales, evidence grades, partitions, and measurement lanes. Use before prompt wording to define required, optional, and prohibited canonical intent cells.
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。
Turn a source-bound company and market dossier into testable ideal-customer-profile hypotheses, buying roles, triggers, constraints, disqualifiers, standing, counterevidence, and research gaps. Use when a URL, company description, monitor profile, or evidence manifest needs to become defensible ICP inputs for buyer research or an AI-visibility prompt panel.
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。
Recover source-bound buyer jobs, struggling moments, desired progress, forces, workarounds, information acts, journey states, criteria, constraints, roles, locales, and authentic language. Use on approved ICP hypotheses plus customer, search, review, forum, procurement, support, or public-market evidence before designing an AI-visibility prompt architecture.
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。
Extract facts from meetings and update your knowledge base — person profiles, chronological log, and index. Use when the user asks "ingest my meetings", "update my knowledge base", "extract facts from meetings", "sync meetings to wiki", "backfill knowledge", or wants their PARA/Obsidian/wiki profiles updated from conversation data.
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。
Comprehensive diagnostic analysis of time series data. Use when users provide CSV time series data and want to understand its characteristics before forecasting - stationarity, seasonality, trend, forecastability, and transform recommendations.
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 and render Mermaid diagrams for architecture docs, READMEs, PRs, terminals, chats, and CI as themed SVG, PNG, or ASCII/Unicode art. Use this skill whenever the user provides Mermaid code or .mmd files; asks for a flowchart, sequence/state/class diagram, ERD, XY chart, or architecture/workflow/data-model visualization; or wants to beautify, theme, batch-convert, or make a diagram terminal-friendly. Runs locally without a browser or DOM, with 15 built-in themes and custom colors.
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 a bounded task plan (and the agent prompt for it) that runs the 5-layer profiling methodology in .claude/skills/model-profiling/METHODOLOGY.md against one model, stops for human confirmation before any GPU work starts, then delegates the actual run to a background agent, verifies its completion claim, and routes findings into a gitignored .profiling-runs/<model>/ directory — durably tracked as a sub-issue under the tracking issue named in SKILL.md's "Result tracking" section — plus the methodology doc.
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。
Attribute GPU time in one sglang-omni stage to specific lines of `sglang_omni/` python source. Drives the `llm-torch-profiler-analysis` backend with a mapping/formal trace pair plus steady-state gates, and carries the rules for reading its three tables on an omni stage. Use when a stage is slower than expected and the question is which python code is the bottleneck, not which kernel is hot.
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 synthetic datasets in a Jupyter notebook after the dataops synthetic-data preflight passes. Use when creating new synthetic data or preparing a guarded local replacement candidate from an approved source.
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。
Create, improve, refactor, replace, review, or validate prompts, instructions, agents, subagents, and skills. Use for Copilot customization cleanup, extending HVE workflows with project-specific capabilities, and parent-owned correction of material review findings.
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。
Design evaluation datasets and supporting documentation for AI systems and agents, covering the scoping interview, difficulty distribution, dataset contract, sample review, and metric and tooling selection. Use when building or reviewing an evaluation set for a conversational agent, assistant, or retrieval-grounded AI 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。
Author and validate durable data and ML feasibility studies using the Feasibility Study Interchange Profile, constrained YAML authority, UUID URN identity, lifecycle lineage, and evidence traceability. Use when assessing whether available data and technical evidence support a proposed outcome.
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。
DataOps and DS/MLOps testing reference for data tiering, Bronze-to-Silver validation placement, pipeline invariants, pytest categories, and validation-versus-drift. Use when designing, reviewing, or generating data pipelines, transformation code, data validation, or data-science test suites.
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。
State, resume, reconstruction, job-lifecycle, transition, and flow-state mechanics for the Data Science and Engineering Coach. Loaded by the coach; not a user entry point.
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。
Machine learning experimentation reference for model-experimentation conventions, experiment tracking and reproducibility, dataset and model abstractions, ML engagement fundamentals, and model-production readiness. Use when standing up ML experimentation infrastructure or assessing whether a trained model is ready for production.
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。
Create and enrich durable data catalogs using the native DS_CATALOG_V1 Markdown contract, declared entity relationships, privacy citation fields, and stable relationship IDs. Use when inventorying engagement data, recording semantic relationships, or preparing a catalog for ERD rendering.
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。
Privacy planning reference for data-flow reasoning, standards mapping, and DPIA thresholds
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 conventions for exploratory data analysis notebooks and analytical dashboards, covering section sequence, visualization selection, scale thresholds, caching and state, and dashboard validation budgets. Use when composing or reviewing an EDA notebook, an analytical dashboard, or a dashboard test pass.
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。