「question」の検索結果

Prepare for recruitment at a Chinese state-owned enterprise (国企 / 央企), including its written test and interview: what these employers assess, the typical stages, answers that show fit and stability, and the questions to ask about the role. Use when asked 国企面试怎么准备, 央企校招, 国企笔试考什么, 国企和互联网怎么选, 国企面试问题, or prepare for a state-owned enterprise interview in China. Produces a stage map to confirm against the recruitment notice, a written-test preparation plan, a question bank with answer points, a mock interview with feedback, and a comparison with private-sector offers.

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。

Prepare a promotion defence (晋升答辩) for a level-based promotion process common at Chinese technology companies: the materials, the presentation structure, the evidence for the next level, and a rehearsal of the committee's questions. Use when asked 帮我准备晋升答辩, 晋升述职, 晋升 PPT, how do I prepare for my promotion committee, or 答辩问题. Produces a level-gap analysis against the target level's criteria, the defence presentation outline in Chinese, a timed talk track, and a question bank with answer points, then a mock committee round.

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。

Practise the essay paper (申论) of the Chinese civil service examination (国考 / 省考): work a question from the given materials (给定资料), find the scoring points (要点), draft an answer in the expected format, and mark it the way examiners do. Use when asked 申论怎么写, 帮我批改申论, 归纳概括题怎么答, 申论大作文, 公文写作题, or practise the shenlun paper. Produces a question-type diagnosis, the points extracted from the materials with their source paragraphs, a model answer within the word limit, a marking of the person's own answer against the points, and a practice plan.

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 asked 孩子这道题不会怎么教, 辅导作业总发火, 怎么给孩子讲应用题, 陪写作业, 作业太多写到很晚, or help a parent guide (not do) a child's homework in China. Produces, for the specific question, an age-appropriate way to explain it with guiding questions in order, a hint ladder that stops before the answer, a similar practice item, what to do if the child is stuck or upset, and a homework routine, written for the parent in Chinese; it never hands over a finished answer for the child to copy.

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 folder of accumulated documents into one decision-ready brief — the skim-map pass over the pile, the extraction against the brief's actual questions, the conflict reconciliation when documents disagree, and the provenance trail back to sources. Use when asked read all this and tell me what matters, synthesize this folder for the new lead, turn these 20 docs into a brief, or what does all this material actually say. Produces the pile map, the question-driven extraction, the reconciled brief with per-claim sources, and the didn't-read honesty ledger.

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。

Decode an auto repair estimate — what each line actually is, which items are urgent vs upsell, and the questions that separate a fair shop from a fishing expedition. Use when someone asks 'is this repair quote fair', 'decode my mechanic's estimate', 'do I really need all this', or 'is the shop ripping me off'. Produces a line-by-line decode with urgency triage, parts/labor sanity checks, ranked red flags, and the exact questions to ask the shop before authorizing.

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。

ALWAYS invoke this skill before asking the user any technical question or offering options, and whenever they ask to be asked in plain words - "ask simple", "ask me simply", "ask me in plain words" - in any language. ALWAYS invoke it too when the user asks for your view on a technical choice: whether it is worth doing, more than the problem needs, or replaceable by something simpler, and which option you would take. Invoke it even when the code makes the answer look obvious: the checks are what make the answer more than a guess. Rewrites the question in plain words and always ends with one marked recommendation. A structural choice first passes six checks, shown as a table: effort now, simpler substitute, extra work later, lock-in, over-engineering, easy to undo. Doing nothing is always weighed.

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 deciding whether a project fits Agricultural Systems (AgSy) and which article type to target. AgSy is a systems-science journal, so the test is a genuine systems question — interactions among components, across hierarchical levels (field → farm → landscape → region → food system), trade-offs, and emergent behavior — analyzed with integrated modelling, NOT a single-factor field trial. Helps frame the question; it does not collect 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。

Use when deciding whether a project fits AEJ: Economic Policy rather than J. Public Economics, AEJ: Applied, or AER, and when sharpening the policy question and its welfare stake for an AEJ: Economic Policy manuscript. Frames fit and the policy-question-first contribution; it does not design the identification or run estimation.

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 deciding whether an applied-micro project belongs at the American Economic Journal: Applied Economics (AEJ: Applied) rather than AER, AEJ: Economic Policy, or a field journal, and what its central question should be. Sets venue fit and question scope; it does not design the identification (see aeja-identification).

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 questions about a brand's Meta ads (spend, results, which ad is winning, is it paused, why did Goose do that) only from reads made in the same turn, with the data window and sync time on every number and a plain caveat when the connection is stale or partial. Use it whenever a user asks how their ads are doing or why an ad action was taken.

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。

Answers natural-language business questions over existing Power BI reports and semantic models through the FabricIQ MCP endpoint, discovering artifacts, resolving entities, generating the DAX itself, and returning a plain-language answer. Use when the user asks a data question, including one that says to query the model directly. For DAX the user supplies, or measures to save, use semantic-model-authoring.

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。

Assigns isolate identity at the right resolution for the question -- ANI/Mash species triage, 7-locus MLST historical comparability, cgMLST/wgMLST outbreak resolution (chewBBACA, BIGSdb, Ridom SeqSphere, EnteroBase HierCC), in-silico serotyping (SISTR/SeqSero2 Salmonella, SerotypeFinder E. coli, Kaptive Klebsiella, SeroBA pneumococcus, spa+SCCmec S. aureus), and lineage callers (TB-Profiler/Mykrobe barcode for MTBC, Pangolin + Nextclade for SARS-CoV-2, PopPUNK GPSC for S. pneumoniae). Use when typing bacterial isolates for surveillance or outbreak investigation, choosing between cgMLST allele distance and core-SNP distance for cluster definition, harmonising calls across schemas/database versions, assigning MTBC lineage with the Napier 90-SNP barcode, calling Salmonella serovar via SISTR with monophasic Typhimurium awareness, running Pangolin UShER mode with pangolin-data version pinning, or selecting a typing resolution to match the surveillance question.

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 questions from the user's own concept pages so they can find out what they saved but never learned, and compare their answers against what the pages say. Use this skill when the user wants to test themselves, revise, make flashcards from their notes, or asks what they have forgotten. Do NOT use for generic quizzing on a topic the vault does not cover, or for answering a question from the vault.

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 periodic review of a second-brain vault: what was added, which topics are growing, what contradicts what, which questions are still open, and what is worth reading next. Use this skill whenever the user asks for a weekly or monthly review, asks what changed in their vault, what they have been learning about lately, or what they should look at next, and when a scheduled review task fires. Do NOT use for structural linting, for ingesting sources, or for answering a specific factual question.

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 all Japanese NLP resources (libraries, models, datasets, tutorials, dictionaries, Hugging Face). Accepts keywords or natural language questions in any language. Use whenever the user asks which Japanese NLP resource to use, or wants to find one: tokenizers / morphological analyzers, BERT or LLM models, embeddings, NER, text classification, datasets / corpora, dictionaries, tutorials, or Hugging Face models. Trigger phrases include '日本語の形態素解析ライブラリ', 'おすすめの日本語tokenizer', '日本語BERTモデル', '日本語の感情分析データセット', '日本語LLM 一覧', 'which Japanese embedding model', 'Japanese NER library'.

A discipline for designing experiments (A/B tests, multivariate, holdouts) so the results actually answer the question you asked. Hypothesis writing, sample size, duration, segment analysis, running discipline, matching a result to a pre-committed decision rule, and the common failure modes that produce confidently wrong shipping decisions. Use this skill whenever the user is planning a test that has not run yet: framing a hypothesis, sizing the sample, setting duration, choosing guardrails, or deciding whether something is worth testing at all. Triggers on design an experiment, experiment plan, A/B test, split test, multivariate test, holdout, experiment hypothesis, sample size, minimum detectable effect, MDE, test duration, guardrail metric, no peeking, pre-committed decision rule, is this worth testing. Use `experimentation-analytics` instead when the test has already run and the question is how to read the result 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。

Expert ITAR compliance advisor for US defense contractors, exporters, and manufacturers. Use this skill for any question about 22 CFR Parts 120-130, the United States Munitions List (USML), DDTC registration, export license applications (DSP-5/73/94), Technical Assistance Agreements (TAA), Manufacturing License Agreements (MLA), brokering regulations (Part 129), deemed export rules for foreign nationals, technology control plans, voluntary disclosures, violation mitigation, jurisdiction determination (ITAR vs EAR), or US Munitions List category scoping. Trigger even if the user doesn't say "skill" — any ITAR or US defense export control question should use this skill.

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。

Saudi Arabia Governance, Risk & Compliance advisor — a compliance router that first determines WHICH Saudi regulations apply (NCA ECC-2:2024, Saudi PDPL, NCA Cloud Cybersecurity Controls, SAMA Cyber Security Framework, CST cloud framework, DCC/OTCC/TCC), then guides framework-specific compliance. Use for any Saudi Arabia / KSA compliance question: NCA Essential Cybersecurity Controls, SDAIA and the Personal Data Protection Law (نظام حماية البيانات الشخصية), PDPL breach notification and data transfers, SAMA compliance for banks/insurers/fintechs, cloud data residency in the Kingdom, CST CSP registration, government/CNI cybersecurity obligations, market-entry compliance ("expanding to Saudi Arabia"), gap assessments, and mapping Saudi requirements to ISO 27001 / NIST CSF / SOC 2. Trigger for any KSA, Riyadh, Vision 2030 compliance, NCA, SDAIA, SAMA, or Saudi data protection question even if no framework is named.

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。

United Arab Emirates Governance, Risk & Compliance advisor — a jurisdiction-first compliance router. In the UAE, WHERE an organization sits determines its law: mainland (Federal PDPL, Decree-Law 45/2021 — executive regulations still pending), DIFC (DP Law No. 5 of 2020 as amended 2025, with a private right of action), ADGM (DP Regulations 2021), CBUAE-licensed financial institutions (consumer-data residency, outsourcing approvals), healthcare (ICT Health Law data localization), and government/CNI (UAE IA Regulation, Cyber Security Council; Dubai ISR, ADHICS). Use for any UAE / Dubai / Abu Dhabi / Emirates compliance question: UAE data protection, DIFC or ADGM privacy, free-zone vs mainland obligations, health-data residency, CBUAE cyber and outsourcing rules, market entry ("expanding to the UAE"), breach notification, gap assessments, and mapping UAE requirements to ISO 27001 / NIST CSF / SOC 2. Trigger for any UAE privacy, cybersecurity, or regulatory question even if no framework is named.

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 GDPR compliance assistant covering all four core workflows: (1) auditing code and systems for GDPR violations, (2) drafting GDPR-compliant documents such as privacy policies, Data Processing Agreements (DPAs), and consent notices, (3) answering GDPR compliance questions with authoritative article citations, and (4) reviewing data flows and PII handling practices. Use this skill whenever the user mentions GDPR, data protection, privacy compliance, lawful basis, data subject rights, DPA, privacy notices, consent management, data breaches, DPIAs, controller/ processor relationships, cross-border data transfers, or any EU/UK data privacy topic. Also trigger for questions like "is this GDPR compliant?", "how do I handle personal data?", "what does a privacy policy need?", or any request involving PII, personal data, or data retention in a regulatory 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。

Design calibrated interview loops, competency-based question banks, and hiring calibration. Use when designing interview processes, creating hiring pipelines, generating scoring rubrics, analyzing interviewer bias, or building question banks.

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。

C-suite orchestration that routes founder questions to the right advisor role(s) and runs multi-role board meetings. Use when coordinating executive decisions, routing strategic questions, or resolving cross-department conflicts.

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 prediction questions using market trading data, not opinions. Use when the user asks probability questions about geopolitics, economics, markets, industries, or any topic where real money is being traded on the outcome. Examples: 'What's the probability of WW3?', 'Will there be a recession?', 'Is AI in a bubble?', 'When will the Russia-Ukraine war end?', 'Is it a good time to buy gold?', 'Will SPY drop 5% this month?', 'Is NVDA options premium overpriced?'. The skill reads prices from prediction markets, commodities, equities, options chains, derivatives, yield curves, and currencies, then cross-validates multiple signals to produce a structured probability 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。