「ai-use」の検索結果

Identify, score, and prioritize Salesforce AI use cases before any implementation begins — opportunity identification, Impact-Effort matrix scoring, feasibility evaluation across Technical/Operational/Data/Risk dimensions, and ROI framing. NOT for the license checks and enablement steps once a use case is approved — use agentforce/agentforce-service-ai-setup or agentforce/agentforce-sales-ai-setup. NOT for making the data model AI-ready — use architect/ai-ready-data-architecture.

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。

Improve the clarity and voice of AI-assisted academic writing (papers, theses, rebuttals) and funding proposals (NSF Project Summary/Description, NIH Specific Aims): preserve scholarly conventions, match claims to evidence (and, for proposals, claims to feasibility), and match the author's own voice. It never changes a number, result, or citation, and it is not for evading AI-use disclosure. Use when editing AI-assisted academic prose or grant proposals.

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。

Run a submission-readiness preflight on a manuscript before arXiv upload. Use when the user is preparing an arXiv submission, asks to check a paper before uploading, mentions hallucinated or fake references, leftover LLM meta-comments / prompts in text, placeholder data (TODO, TBD, XX%), AI-use disclosure, scholarly integrity, research integrity, or arXiv moderation risk — even if they don't say "preflight". Also trigger on phrases like "check my paper before arXiv", "verify my references", "scan for AI artifacts", "scan for LLM residue", "is my submission ready", or "review .tex/.bib before submit".

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 the submission-time disclosure block for a manuscript — the AI-use disclosure statement matched to the target journal's policy, CRediT author-contribution roles, conflict-of-interest statement, and data-availability statement. Use when the user says "AI disclosure", "disclosure statement", "do I need to disclose Claude", "CRediT roles", "conflict of interest statement", "data availability statement", or is preparing a submission package. NOT statistical-disclosure screening of restricted-data outputs — that is /disclosure-check.

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 auditing an AAAI main technical track submission for OpenReview readiness, double-blind anonymity, page limits, reproducibility checklist, supplementary material, author limits, multiple-submission policy, and AAAI AI-use policy compliance.

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。

Run an attorney-facing knockout/availability trademark search across the United States and Canada. Use whenever a user (founder, client, or attorney) asks whether a brand, mark, name, logo, slogan, or product name is "available," "free to use," "clearable," or "already taken" — or asks Claude to "search the trademark register," "check USPTO," "check CIPO," "do a knockout search," "clearance search," "availability search," or "see if [mark] is trademarked." Trigger even when the user phrases it casually ("can I call my company X?", "is this name taken?"). Covers federal USPTO (TESS/TSDR), federal Canada (CIPO), common-law/web/state, and WIPO Madrid international. Output is a knockout availability memo with green/yellow/red verdict for attorney review. Includes professional-responsibility gating (AI-use disclosure, conflict check, scope confirmation) before substantive analysis. Not legal advice — produces work product for attorney sign-off.

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 a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.

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 an instructor wants to create, interview for, configure, install, update, or review a course AI-use policy for MATLAB AI tutoring. Produces an AI-POLICY.md file for LMS sharing and local tutoring-session enforcement by assignment guardrails.

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 a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.

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。

Review final-assignment materials for requirements, citations, AI-use disclosure, and data-analysis completeness.

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。

「学内業務で AI を使う前に『禁止／限定／推奨／開放』のどれに該当するか判定したい」 「既存の機密情報 3 段分類と業務場面を接続したい」 「部署ごとの AI 利用方針を体系化したい」 場面で使う横断スキル。

Use when designing a course, assessment, or student-feedback workflow and deciding where AI assistance is acceptable, when grading or feedback must stay within academic-integrity and FERPA/KVKK boundaries, or when AI-use rules for students need stating.

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。