「quest」の検索結果

Extract tacit knowledge through non-obvious microquestions — things only the user knows that can't be found in code, docs, or research. Use when you're about to guess at intent, context, or constraints during SAFEWORD's understanding flow. Also use when user says 'ask me', 'what do you need to know', or when another skill (bdd, brainstorm, debug) needs user context before proceeding. Do NOT use for questions answerable by reading the codebase or searching the web.

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。

GitHub patterns using gh CLI for pull requests, stacked PRs, code review, branching strategies, and repository automation. Use when working with GitHub PRs, merging strategies, or repository management tasks.

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。

Socratic questioning protocol + user communication. MANDATORY for complex requests, new features, or unclear requirements. Includes progress reporting and error handling.

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 automatically for PDF tasks, including Chinese requests like 查看PDF, 读取PDF, PDF里写了什么, 总结PDF, 分析PDF, 提取PDF文字, 识别PDF内容, or questions about any .pdf file. Prefer text extraction with pdfplumber/pypdf first, then render pages with Poppler when layout, images, tables, stamps, watermarks, or scanned content matter.

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。

Specialized GitHub PR intelligence agent for automatically gathering comprehensive context from Pull Requests. Activate when users need CI failure analysis, review comment investigation, or PR status assessment. Triggers on requests like "CI 실패 원인 찾아줘", "gather review comments", "check PR status", "analyze PR".

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 users need to search academic papers, download research documents, extract citations, or gather scholarly information. Triggers include: requests to "find papers on", "search research about", "download academic articles", "get citations for", or any request involving academic databases like arXiv, PubMed, Semantic Scholar, or Google Scholar. Also use for literature reviews, bibliography generation, and research discovery. Requires OpenClawCLI installation from clawhub.ai.

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。

Native video analysis using Google Gemini API. Upload and analyze video files — describe scenes, extract text/UI, answer questions about content, transcribe speech, identify objects and actions. Use when: (1) User sends a video file and wants it analyzed, (2) Video summarization or description needed, (3) Extracting text, UI elements, or information from screen recordings, (4) Answering questions about video content, (5) Comparing multiple videos, (6) Analyzing tutorials, demos, or walkthroughs.

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Own goal-first engineering outcomes: investigate, find root cause, design, plan, then delegate implementation unit-by-unit to implementation-loop with verification and honest reporting. Use to fix, build, or own an outcome end to end; investigate-and-fix; "make X work"; or plan-then-execute in one request. A request with an approved plan or equivalently precise spec that needs nothing beyond implementation belongs directly to implementation-loop. Requires implementation-loop and an authenticated backend CLI to execute.

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。

Own goal-first engineering outcomes: investigate, find root cause, design, plan, then delegate implementation unit-by-unit to cursor-implementation-loop, this plugin's kernel skill, with verification and honest reporting. Use to fix, build, or own an outcome end to end; investigate-and-fix; "make X work"; or plan-then-execute in one request. A request with an approved plan or equivalently precise spec that needs nothing beyond implementation belongs directly to cursor-implementation-loop. Requires this plugin's cursor-implementation-loop skill; they ship together.

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。

Concise communication mode. Cuts ~60-70% of output tokens while keeping natural, readable English. Drops filler, hedging, and pleasantries but maintains grammar and sentence flow. Elaborate on request -- ask for detail and get it, then auto-return to concise. Use when user says "concise mode", "be concise", "less verbose", or invokes /concise. Also triggers when user requests token efficiency with readability.

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Drafts code-compliant California responses to Requests for Production (CCP § 2031.010 et seq.) for Superior Court. Use when the user mentions California RFP responses, CCP 2031, discovery objections, compliance statements, inability to comply, privilege objections, Bates numbering, verification requirements, or responding to document requests in California state court. California Superior Court only — not for federal court without substantial modification.

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。

Book lawyer services through Lokuli MCP. Use when user needs to find and book lawyer. Triggers on requests like "book a lawyer", "find lawyer near me", or any lawyer service request.

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 professional Marp presentation slides with 7 beautiful themes (default, minimal, colorful, dark, gradient, tech, business). Use when users request slide creation, presentations, or Marp documents. Supports custom themes, image layouts, and "make it look good" requests with automatic quality improvements.

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 the most influential Reddit venues for a brand's AI visibility using Xpoz. Finds which subreddits and threads AI engines actually cite when answering buyer questions, where those questions get asked, and which live threads are worth answering. Use when asked to "get cited by AI through Reddit", "which subreddits matter for my brand", "Reddit influence map", "where should we engage on Reddit", or "why does ChatGPT cite Reddit for my category".

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Reports how local Git branches are progressing toward canonical upstream inclusion as a compact terminal table plus a browser-rendered HTML report, with branch purpose, stack dependencies, Git Machete topology, ahead/behind counts, change-request status, and next action. Use when asked for upstreaming status, local branch submission progress, which branches have change requests, or what remains before they can land.

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Checks the canonical upstream issue tracker, discussion forums, and open pull requests, merge requests, or equivalent change queues for existing reports, discussion, or work on a problem just discussed. Use before diagnosing, fixing, or reporting an apparent open-source problem to avoid duplicate effort and missed context.

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 a pull request description for the current branch and writes it as Markdown to .git/magit/posts/new-pullreq for magit-forge. Use when the user asks for a PR description, PR summary, or write-up of what a branch changes, or is preparing to open a pull request.

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 sensemaking, frame revision, anomaly handling, questioning strategy, and ambiguity diagnosis with the Data/Frame model before action is chosen. NOT for deterministic classification, settled execution planning, or situations where the frame is already stable.

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。

Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization 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。

Use when answering factual or explanatory questions about the current project — codebase, architecture, conventions, workflows, constraints — with minimal cited evidence. Triggers on "how does X work here", "what does X mean", "why is X structured this way", "is this claim true in the repo", "explain X with sources". NOT for brainstorming or comparing approaches (mk:brainstorming), trade-off decisions (mk:party), debugging/root cause (mk:investigate), code review (mk:review), plans (mk:plan-creator), security audits (mk:cso), visual explanations/diagrams (mk:preview --explain), test-coverage gaps (mk:nyquist), library docs (mk:docs-finder), or workflow navigation (mk:help).

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。

Pre-routing ambiguity analysis — scores request clarity and asks clarifying questions when needed (inspired by ouroboros)

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。

Investigate reviewed Grafana APM evidence for request rate, error rate, and operational latency. Use when an application is slow, erroring, dropping traffic, regressing against a baseline, or needs a bounded request-rate, error, and p95 latency health assessment.

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Use this skill for ANY of the following — do not skip it even if the query seems simple: strategic decisions, business analysis, thesis building, complex problem solving, geopolitical analysis (non-market), philosophical reasoning, academic research, system design, decision under uncertainty, long-form thinking, or any question that benefits from First Principle deconstruction and multi-agent reasoning. ALWAYS use this skill when the user asks about: why something works the way it does, whether a decision is correct, how to think about a complex problem, what could go wrong, analyzing a situation from multiple angles, or any "should I..." question involving real-world trade-offs. This is NOT a market/trading skill — for financial markets, BTC, gold, stocks, forex, macro economics, use money-atlas-intelligence-os 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。

Analyze suspicious SMS/email messages, phishing links, redirects, domains, payloads, malware indicators, and suspected C2 infrastructure safely. Use when the user provides spam, smishing/phishing content, raw .eml headers, URLs, QR links, attachments, hashes, strange payloads, redirect chains, DNS/WHOIS/RDAP questions, C2 clues, exploit-chain questions, or asks what defenses to apply.

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。