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概要と使いどころ
Generate code fixes and patches from bug reports, failing test cases, error messages, and stack traces. Use this skill when debugging code, fixing test failures, addressing GitHub issues, resolving runtime errors, or patching security vulnerabilities. Analyzes the bug context, identifies root causes, and generates precise code patches with explanations and validation steps.
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。
Compare runtime behavior between original and migrated repositories to detect behavioral differences, regressions, and semantic changes. Use when validating code migrations, refactorings, language ports, framework upgrades, or any transformation that should preserve behavior. Automatically compares test results, execution traces, API responses, and observable outputs between two repository versions. Provides actionable guidance for fixing deviations and ensuring behavioral equivalence.
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。
Instrument code to support efficient git bisect by producing deterministic pass/fail signals and concise runtime summaries for each tested commit. Use when debugging regressions with git bisect, automating bisect workflows, creating bisect test scripts, handling flaky tests during bisection, or needing clear exit codes and logging for automated bisect runs. Helps identify the exact commit that introduced a bug through automated testing.
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。
Polishes UI primitives one at a time using 2026 research-backed patterns — adds loading/icon states to buttons, replaces inline skeletons with primitives, adds secondary actions to errors, ensures 48px touch targets and visible focus indicators, applies a standard motion token ladder. Use when: 'make the UI feel more polished', 'add attention to detail', 'reduce component duplication', 'tighten the design system', 'apply craft to the frontend'.
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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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 for an HTML-first visual design deliverable or design judgment: interactive prototype, slide deck, motion demo, infographic, dashboard, landing page, whitepaper, changelog, business card, social cover, brand system, design critique, multi-variant exploration, or export to MP4, GIF, PPTX, PDF, or SVG. It is optimized to make AI agents do the routing, template selection, verification, and export prep so humans spend less time prompt-engineering. Do not use for production web apps, SEO sites, backend systems, or pure copy edits.
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。
One-time autonomous design context discovery. Scans the codebase to extract design tokens, typography, colors, spacing, brand patterns, and tech stack, then writes a Design Context section to CLAUDE.md. Zero questions asked. Use when: 'setup design', 'design context', 'design tokens', 'brand discovery', 'design system scan', 'initialize design', 'teach 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。
Use for writing, reviewing, debugging, or testing Pandera schemas and runtime validation for Polars DataFrame or LazyFrame pipelines installed with pandera[polars]. Trigger on pandera.polars DataFrameSchema, DataFrameModel, Column, Field, Check, PolarsData, decorators, coercion, strictness, lazy error collection, and validation-depth decisions. Do not use for pandas-backed Pandera, Pydantic object models, Polars transformations without Pandera, static dataframe typing alone, or generic data-quality platforms.
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 SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.
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。
Enterprise BPMN 2.0 diagram generator — converts natural language process descriptions into OMG-compliant BPMN 2.0 XML files and SVG previews via a 4-phase pipeline: Intent Extraction (LLM → JSON Logic-Core) → Validation (deadlock detection, structural soundness) → ElkJS Auto-Layout → BPMN XML + SVG output. Supports: multi-pool collaborations, message flows, boundary events (timer/error/signal), loop/multi-instance markers, data objects, all gateway types with correct gatewayDirection, and all BPMN 2.0 task types. Use this skill whenever the user wants to create, generate, or model a BPMN diagram, process flow, or workflow — even if they say "draw a process", "model this workflow", "make a BPMN for...", "create a Prozessmodell", "visualize this process", or describe a business process in natural language. Also use for editing or extending existing BPMN Logic-Core JSON.
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。
Production-grade Python and AI engineering standards. Use for ANY Python work — FastAPI services, LangChain/LangGraph agents, RAG pipelines, LLM API integrations, document processing, or data scripts — whether writing new code, reviewing, refactoring, or debugging. Enforces typed Pydantic boundaries, structured errors and logging, LLM call timeouts/retries/output validation, secret management, async correctness, and tested code.
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。
Manage persistent coding sessions across Claude Code, Codex, Antigravity (agy), Cursor, and OpenCode engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, autoloop autonomous workspace iteration, ultraapp building deployable web apps from a structured Q&A interview, switching models/tools at runtime, exposing the orchestrator's 69 tools as an MCP server to Hermes Agent / Claude Desktop / Cursor / Cline / Continue / Zed / Windsurf / Goose, or running as an Agent Client Protocol (ACP) agent that Zed / JetBrains / Neovim / Emacs / VS Code / dsh can drive directly. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "autoloop", "ultraapp", "Forge tab", "build a web app", "one-click app", "AppSpec", "autonomous iteration", "iterate until goal", "deep paper review", "auto research", "switch model", "multi-agent", "coding session", "session inbox", "cursor agent", "opencode", "mcp server", "clawo-mcp", "hermes mcp", "model context protocol", "ultracode", "dynamic workflow", "fanout", "fan-out", "best-of-N", "steer turn", "interrupt turn", "fork thread", "rollback turns", "acp", "agent client protocol", "clawo acp", "zed agent", "jetbrains agent", "external agent", "dsh subagent", "deepseek harness".
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 rich agent answers and any file, folder, URL, or export into a polished single-file HTML page. Auto-picks a default route plus 17 concrete design systems (teaching, dashboard, atlas, timeline-story, document, …).
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。
In-chat fallback for the Timeline Inspector Refine agent. Use when the user runs `/refine live`, asks to "refine live", "go live", or answer refine jobs — but ONLY when no persistent agent is wired (no `npx transitions-refine live`). Prefer `npx transitions-refine live` for run-and-forget (relay spawns agent per click, no idle credit burn). This skill long-polls the relay, posts suggestions, handles scan/apply jobs.
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 retrieve agent skills at runtime. This skill should be used when the agent needs to find specialized capabilities, workflows, or domain knowledge to accomplish a task. Skyll aggregates skills from skills.sh and returns full SKILL.md content ready for context injection.
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。
A 股全市场高流动性池内按趋势回踩（trend_pullback）产出买入候选与持仓卖出信号；另有 realtime_quotes 批量拉取现价快照。供下单前决策；不跑回测、不自动下单。Use when 用户要选股、看实时报价、判断买卖信号或检查持仓是否触发离场条件。
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。
A 股主板流动性池内按趋势回踩（trend_pullback）产出买入候选与持仓卖出信号；另有 realtime_quotes 批量拉取现价快照。供下单前决策；不跑回测、不自动下单。Use when 用户要选股、看实时报价、判断买卖信号或检查持仓是否触发离场条件。
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 subtitles or a transcript from a YouTube URL and save normalized timestamped text locally. Use when the user wants the spoken content of a YouTube video as text.
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 or improve onboarding flows, empty states, and first-time user experiences. Helps users get started successfully and understand value quickly.
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。
One-time setup that gathers design context for your project and saves it to your AI config file. Run once to establish persistent design guidelines.
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。
Skill permission analysis, one-time authorization, analyze skill permissions, batch authorization
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。
Determine whether an app is ready to submit, then drive the current App Store release flow with asc, including validation, staging, review submission, first-time availability, subscriptions, IAP, Game Center, and App Privacy 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。
Use when the user has a research question and needs a complete experiment package — design document, runnable code, results (measured or simulated with honest provenance), publication-grade figures, structured report. Single-stage, no Python runtime.
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 when the user has a vague research direction and wants to explore feasible specific topics. Outputs a structured analysis with candidate topics, innovation/feasibility scoring, and a pre-survey of 20–30 representative works. Single-stage, no Python runtime.
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。