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概要と使いどころ
Elicit and challenge data-analysis requirements before querying. Use when the user asks ambiguous data questions, requests business/report answers, needs metric definitions clarified, or may be drawing decisions from incomplete 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。
Act as an interactive data analyst for ClickHouse-backed analytics. Use when the user asks questions about internal data, metrics, dashboards, telemetry, active users, revenue, funnels, trends, distributions, or wants an analyst-style conversation, ad hoc SQL, charts, or a data export against ClickHouse (local or ClickHouse Cloud).
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。
Elicit and challenge data-analysis requirements before querying. Use when the user asks ambiguous data questions, requests business/report answers, needs metric definitions clarified, or may be drawing decisions from incomplete 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。
Troubleshoot and resolve common issues with the ClickHouse Node.js client (@clickhouse/client). Use this skill whenever a user reports errors, unexpected behavior, or configuration questions involving the Node.js client specifically - including socket hang-up errors, Keep-Alive problems, stream handling issues, data type mismatches, read-only user restrictions, proxy/TLS setup problems, or long-running query timeouts. Trigger even when the user hasn't precisely named the issue; vague symptoms like "my inserts keep failing" or "connection drops randomly" in a Node.js context are strong signals to use this skill. Do NOT use for browser/Web client issues.
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。
Act as an interactive data analyst for ClickHouse-backed analytics. Use when the user asks questions about internal data, metrics, dashboards, telemetry, active users, revenue, funnels, trends, distributions, or wants an analyst-style conversation, ad hoc SQL, charts, or a data export against ClickHouse (local or ClickHouse Cloud).
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。
YC Office Hours — two modes. Startup mode: six forcing questions that expose demand reality, status quo, desperate specificity, narrowest wedge, observation, and future-fit. Builder mode: design thinking brainstorming for side projects, hackathons, learning, and open source. Saves a design doc. Use before /plan-ceo-review or /plan-eng-review.
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。
Answer a factual question from the web, or enumerate what exists, with sources
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 a question across web and academic sources and produce a cited report.
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。
Reads, searches and counts Claude Code session transcripts (the .jsonl files under ~/.claude/projects, subagents too) with the csift command-line tool, and ships hooks that re-inject compacted turns or record pending questions. Use when asked what a past or running session said, did, edited, spent or is doing now; when about to grep, jq or script over a transcript jsonl (a type=="user" filter overcounts the person's messages), count how many messages the person typed, tool calls or tokens, or claim something was never done or decided; to rebuild a lost file or plan from what a session wrote, extract pasted images, restore turns a compaction summary lost, find a subagent's own id when CLAUDE_CODE_SESSION_ID names the parent, wait on or message another session or subagent; or when csift prints "unknown label selector", "show needs an address", "looks like a session/agent id, not a project path" or "0 matches — a DEFINITIVE absence". Not for semantic or embedding search: csift matches regular expressions only.
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。
分析 msProbe compare 精度比对结果文件（CSV/XLSX），定位整网精度问题与数值差异。支持统计量模式，输出分析报告。支持辅助信息协助定位：辅助信息（历史结论/已知根因/已排除算子/嫌疑区域等）建议在请求中附带（不依赖交互工具）。若请求中未附带且运行环境无 AskUserQuestion，则跳过辅助信息收集、照常完成分析，报告中省略辅助信息章节并注明原因。
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。
Find authoritative guidance for installing, configuring, operating, securing, and troubleshooting OpenClaw, including channels, model providers, Gateway operations, tools, plugins, automation, nodes, multi-agent routing, and CLI errors. Use for any question or maintenance task involving an OpenClaw installation, `openclaw` CLI command, Gateway daemon, configuration key, or integration.
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。
On-brand visual marketing content using Paper MCP and parallel agent teams. Reads the project's brand/ directory to build a design system, then spawns designer agents in parallel — each creating a unique artboard with a different on-brand layout interpretation. Intelligently adapts agent count and approach based on user goals via AskUserQuestion interrogation. Produces Instagram carousels, TikTok slideshows, social posts, story slides, and visual assets. When used with content spec YAMLs from /slideshow-script, each agent gets a unique script AND unique design direction. Make sure to use this skill whenever the user wants visual marketing content designed in Paper — carousels, slideshows, social posts, story slides, or any visual asset. Even if they just say "design something" or "make slides" or "create visual content", this is the skill. Also triggers on "paper marketing", "instagram design", "TikTok design", "slideshow design", "create carousel", or "social graphics".
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。
Map who is winning an SEO market with measured data — recurring SERP domains, organic footprints, winning content themes, backlink authority, and the gaps worth attacking. Use this skill for market-level questions ("who ranks for X", "how competitive is this space", "where are the SEO openings") before committing to keyword targets or content themes. For one named competitor, use openseo-competitor-analysis instead. Triggers: "competitive landscape seo", "who ranks for", "seo market map", "how competitive is", "serp competitors".
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。
Trigger when: (1) User wants to create an educational/explainer video, (2) User has a vague concept they want visualized, (3) User mentions "3b1b style" or "explain like 3Blue1Brown", (4) User wants to plan a Manim video or animation sequence, (5) User asks to "compose" or "plan" a math/science visualization. Transforms vague video ideas into detailed scene-by-scene plans (scenes.md). Conducts research, asks clarifying questions about audience/scope/focus, and outputs comprehensive scene specifications ready for implementation with ManimCE or ManimGL. Use this BEFORE writing any Manim code. This skill plans the video; use manimce-best-practices or manimgl-best-practices for implementation.
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。
Builds, measures, and improves an Answer Engine Optimization program across ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, and Copilot. Use for AEO, GEO, LLMO, AI search visibility, AI citations, brand mentions, answer-engine monitoring, llms.txt, AI crawler audits, or questions like 'what does AI say about us?' and 'why are competitors cited instead of us?'. Produces a durable evidence report and prioritized gameplan, not a one-shot content checklist.
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。
First-run conversational setup wizard for marketing-cli. Use when /cmo detects no populated brand files and no cmo-preferences.md, when the user just installed marketing-cli for the first time, or when they say things like "set up marketing", "start marketing", "marketing wizard", "I just installed marketing-cli", "onboard me", "first time", "initial setup". Asks 3 sharp questions (identity, posture, distribution preferences), records the answers to brand/cmo-preferences.md, then hands off to /cmo's foundation flow which spawns 3 research agents in parallel. The user never has to type /mktg-setup directly — /cmo auto-routes here on first run.
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。
Analyzes Perfetto traces to find the root cause of latency, memory, or jank issues in Android apps. Use when the user provides a Perfetto trace file and asks any question, ongoing investigation, or open-ended request to analyze its contents.
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。
Correct, idiomatic, high-performance JSON encoding and decoding with github.com/go-faster/jx. Use this skill whenever you write or review jx-based Encode/Decode methods, implement JSON marshaling over jx, use jx.Decoder / jx.Encoder / jx.Writer, or encounter questions about jx buffer safety, Capture, pooling, or Writer vs Encoder trade-offs. Also trigger when the file imports "github.com/go-faster/jx" and you are adding or changing any encoding / decoding logic.
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。
Structured design intake for non-trivial tasks. Asks clarifying questions and produces a design proposal.
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 when read_file is not enough to read a PDF — it failed, timed out, or returned garbled or empty text, or the question is about figures, charts, layout, embedded images, attachments, or form-field values. Covers content inventory, text extraction, page rasterization for visual inspection, embedded image/attachment/table/form-field extraction, and choosing the right reading strategy for different document types (text-heavy, scanned, slide-decks, forms, data-heavy). Do NOT use this skill for PDF creation, form filling, merging, splitting, watermarking, or encryption — use the pdf skill instead.
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。
Enter talk mode — conversation and research, no repo changes. ONLY invoke when the user explicitly types `/talk` or `$talk`; never auto-select from a natural-language question or design discussion.
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 explore an Obsidian vault through the NoteBrain CLI (semantic search, tags, backlinks, connections, hidden links, boosted retrieval). Use it whenever the user mentions their notes, knowledge base, Obsidian vault, semantic search, finding connections or unlinked notes, or asks exploratory questions like "what do I know about X", "find notes related to Y", "what connects to Z", or "summarize my notes on W" — even when they never say NoteBrain, vector search, or ChromaDB.
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。
Hard-gated ideation for an Arbor research run. Load at the START of every IDEATE round, before drafting any hypothesis. Enforces the PI mindset (mechanism over knob), the four-question first-principles probe, the kill-filter, and the four-line hypothesis format. Ported from Arbor's idea_drafting + first_principles_probe.
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。
REQUIRED before declaring a task done when the diff touches user input, SQL, shell, auth, credentials, file paths, serialization, crypto, network endpoints, data deletion, or dependency surface. Judge by that surface, not the task label — research/experimental/local-only code with none of it can skip this. Walks OWASP/ASVS/MITRE questions the compiler and the test suite won't catch. Pair with `rafter run` on the same diff. When the diff has that surface, marking it complete without both has been under-delivered — do not claim 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。