「que」の検索結果

Professional business analysis skill aligned with BABOK® Guide v3 standards. Use this skill when you need to elicit and document requirements, analyze business processes, conduct stakeholder analysis, develop business cases, perform gap analysis, or create business requirements documents (BRD). Ideal for requirements gathering, process mapping (BPMN), financial analysis (ROI, NPV), stakeholder engagement, and solution evaluation. Triggers: "gather requirements", "create business case", "analyze process", "stakeholder analysis", "gap analysis", "BRD", "functional requirements", or requests involving business analysis 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。

This skill should be used when creating bug/defect reports during system testing. Use this skill when you discover a bug, need to document test failures, or want to create comprehensive bug tickets with proper reproduction steps, severity assessment, and environment details. Guides users through interactive questioning to gather all necessary information and generates professional bug ticket documents in Markdown format. Supports Japanese (default) and English.

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 only when the user explicitly requests agy, Gemini through agy, Antigravity, or an external-model second opinion for review or research. Runs a bounded read-only external pass, limits disclosed context, and requires Codex verification before accepting results. 中文触发：明确要求 agy、通过 agy 调用 Gemini、Antigravity、外部模型复核或外部第二意见。

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。

Ultra-compressed communication mode. Cuts output tokens 65% (measured) by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.

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 designing, reviewing, modifying, migrating, optimizing, securing, operating, troubleshooting, or decommissioning data architectures across data model exploration, SQL, ISO SQL conformance, NoSQL, vector, graph, document, time-series, NewSQL, search, in-memory, SQLite, CDC/Debezium, DBMS storage/query/recovery internals, database incident prevention, hardware acceleration, cache, big-data, lakehouse, and warehouse systems.

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 performing cybersecurity operations - offensive or defensive - including threat detection and hunting, SOC operations, incident response, digital forensics and DFIR, malware analysis and reverse engineering, threat intelligence, cloud/container/network/application/API security, identity-access and zero-trust, OT/ICS and endpoint security, vulnerability and supply-chain management, DevSecOps, cryptography, AI security, compliance and governance, and red-team or penetration testing under signed rules of engagement; routes to one of 800+ technique playbooks mapped to MITRE ATT&CK, D3FEND, and NIST CSF.

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 OpenVEX / CycloneDX VEX attestations from `.vulnetix/memory.yaml` triage decisions, optionally sign with cosign, optionally upload to Vulnetix and post to a GitHub PR. Use when documenting triage decisions for supply-chain consumers, attaching VEX to a CycloneDX SBOM, or satisfying customer attestation requests.

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 CycloneDX 1.7 and/or SPDX 2.3 SBOMs for the repo, optionally cosign-signed, optionally cross-validated against syft output. Use when producing an SBOM for compliance, attaching to a release artefact, integrating with an SBOM registry, or satisfying a customer attestation 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。

A six-year-old code reviewer who asks "why?" about everything. Catches over-engineering, confusing names, and unnecessary complexity. Output is a 1-2 line summary of the request as a whole: how is the file? Is the class nice? Is the function correct?

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 your data to a partner's data in the marketplace. Given a dataset you already own with person/edge data, this skill walks you through picking a partner data source to match against, choosing which identifier types to match on, optionally selecting which enrichment attributes to attach, and then submits the report — returning overlap, match counts, and demographic coverage. Use when: "how does my data compare to your marketplace", "compare my data to [partner]", "how much overlap do I have with [supplier]", "run a match report", "match my customers against 3P data", "see what enrichment is available for my dataset", or any open-ended question about marketplace overlap. (narrative-identity)

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。

Register a Snowflake table or view as a Narrative dataset from SQL, with no Configuration screen and no browser. Not for installing or configuring the native app, and not for querying a dataset that already exists. Use when: "register this table as a dataset", "register a Snowflake dataset from SQL", "onboard my_table to Narrative", "script dataset registration", "add this view to my data plane". (narrative-common)

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。

Produce a coverage & quality profile of a Narrative dataset (or access rule): row count, per-column null/fill rate, cardinality, ranges, top-values, inferred column shape, and quality flags. Reads bundled stats + sample first, recovers missing/stale stats by configuring and recalculating them, and escalates to a cheap `/write-nql` query only for a measure no stat can provide. Descriptive, not prescriptive. Use when: "profile dataset N", "what does dataset N look like", "coverage and quality of <dataset>", "what id types does N emit", "null rates / cardinality for <dataset>", "is this dataset's stats fresh". (narrative-common)

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 submit a Narrative workflow from a natural-language intent. Picks the closest example from `assets/examples/`, adapts it to the user's case, walks the YAML against the spec, resolves the data plane, and submits via `narrative_workflows_create` only after the user has approved the rendered spec. Use when: "create a workflow that does X", "schedule a daily refresh of dataset Y", "wrap this NQL as a workflow", "build a pipeline that creates view A then refreshes view B", "submit this workflow YAML", "productionize this query as a recurring job". (narrative-common)

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。

Weekly read-only repository audit. Grades one rotating rubric from .claude/audit-rubrics/, files between zero and five GitHub issues for what it finds, and changes no files. Use when the user asks to audit the repo, or when the audit-weekly workflow invokes it. It never edits, commits, branches, or opens pull requests.

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。

ego-browser (ego-lite) is a Chromium-based browser that gives AI agents a CLI-accessible Node.js runtime for driving a real browser. Use this skill whenever the user needs to interact with a website opening pages, filling forms, clicking buttons, taking screenshots, extracting page data, testing web apps, logging into sites, automating browser operations, or any other browser automation task. Triggers include requests to "open a website", "visit a URL", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "extract content from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also used for exploratory testing, dogfooding, QA, bug hunting, or reviewing app quality. Prefer ego-browser over any built-in browser automation, web fetch, or other web tools.

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。

Verify a tested web agent's real behavior from its recorded Pi or Codex session and selected screenshots before scoring. The judge prompt provides absolute artifact paths; use targeted queries to compare actual outputs, errors, and the final answer with the recorded behavior, then return only the verdict JSON. Only for ego-bench judging—never to drive a browser or rerun the agent.

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。

Reconstructed reference notes on US NRC software assurance guidance for digital computer software in nuclear power plant safety systems, built from seven public-domain sources (Regulatory Guide 1.168 Revision 2, July 2013; Regulatory Guides 1.169, 1.170, 1.171, 1.172, and 1.173 Revision 1, July 2013; NUREG-0800 Branch Technical Position 7-14 Revision 6, August 2016; pinned 2026-10-12). Use for software life cycle process questions, software requirements specifications, software configuration management plans, verification and validation with reviews and audits, test documentation and unit testing, and what NRC staff examine in a BTP 7-14 software review of plans, implementation, and design outputs. SCOPE LIMITS: NRC Regulatory Guides and the BTP 7-14 staff review position only; the endorsed IEEE standards (IEEE Std 603-1991, 7-4.3.2-2003, 828-2005, 829-2008, 830-1998, 1008-1987, 1012-2004, 1028-2008, 1074-2006), IEC 60880, IEC 61513, IEC 62138, and IAEA guides are cited by designation and edition only, with no standard text; RG 1.152 Rev 4, BTP 7-18, BTP 7-19, other SRP Chapter 7 sections, and NUREG/CR contractor reports are outside the pack; reflects the pinned 2013 and 2016 editions, not later agency actions; synthesized reference notes, not legal or licensing advice and not a substitute for the source documents. LICENCE: Public Domain (US Government work, 17 U.S.C. § 105).

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。

Reconstructed reference notes on US FDA medical-device software regulation, built from seven public-domain FDA sources (the QMSR final rule 89 FR 7496, FDA device software guidance documents, and the eSTAR program page; pinned 2026-10-12). Use for regulatory pathways and premarket submissions (510(k), De Novo, PMA, eSTAR), premarket software documentation levels (Basic or Enhanced), software validation and Computer Software Assurance, the Quality Management System Regulation (QMSR, 21 CFR 820), medical-device cybersecurity (secure product development, SBOMs, FD&C Act section 524B, labelling), clinical decision support boundaries, off-the-shelf software, MDDS and other software-function scope questions, and the 510(k) software-change trigger. SCOPE LIMITS: US FDA device regulation only; no EU MDR or UKCA content (see `med-device-eu`); no ISO 13485, ISO 14971, or IEC 62304 text, which are named and summarized only as FDA obligations; no drug, biologic, or combination-product rules; reflects the pinned source dates, not later FDA actions; synthesized reference notes, not legal advice and not a substitute for the guidance documents. LICENCE: Public Domain (US Government work, 17 U.S.C. 105).

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 building serverless AI SDK for Python endpoints, handling hook approvals, deferring hooks, or resuming runs across requests.

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。

I/O + 계산 혼재 또는 Mock stub + 계산 혼재 메서드를 Functional Core / Imperative Shell(빵속빵, Impure-Pure-Impure Sandwich)로 분리. "순수 함수 분리", "I/O와 계산 나눠", "mock 없이 테스트하게", "functional core", "/segregate-functional-core" 요청 시 사용. 단, 값 반환과 부수효과 분리(CQS)만이면 /separate-query-modifier가 적합. /segregate-functional-core [commit-ref]로 호출.

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。

Iterative deep planning with critiques and alternatives. Use when facing complex design decisions, cross-platform changes, native module modifications, or API changes requiring thorough analysis.

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。

Load when configuring GitBook documentation sites, authoring GitBook-flavored markdown pages and custom blocks, integrating OpenAPI/Swagger specs, driving GitBook Change Request workflows (create, review, comment, resolve), or building GitBook platform integrations. Do not use for general repository code reviews, general .NET OpenAPI schema generation, or non-GitBook markdown.

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。

Load only when the user asks for Robin/Rodin neutral mode, Socratic intellectual sparring, steelmanning, anti-echo-chamber critique, or a deep societal/political/religious/economic discussion; not for coding, factual lookup, or ordinary advice.

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。

Load when building .NET ATProtocol applications with CarpaNet source generators, XRPC queries, Jetstream subscriptions, OAuth 2.0 DPoP, or Lexicon JSON models.

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。