「general」の検索結果

Build full-paper Chinese-English side-by-side, figure/table-aware, source-grounded Markdown readers for journal or conference papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read a paper, make 中英文对照/原文对照/全文翻译解读, extract figures or tables into the right positions, preserve figure/table placement near relevant prose, or keep exact source anchors for every block. This skill must not degrade into a summary-only output unless the user explicitly asks for a summary. Also trigger on general paper-reading and translation requests even without the word "Nature", such as reading/translating an academic paper, literature reading, understanding a paper, and Chinese phrasings like 读论文、精读论文、论文翻译、文献翻译、文献阅读、学术阅读、帮我读这篇文章、翻译这篇paper.

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。

Remote-sensing literature search, citation verification, sensor/platform/dataset query strategy, citation file management (.ris/.bib/.enw conversion), and reference management (BibTeX, related articles, DOI/arXiv lookup) across CrossRef, arXiv, Scopus, ScienceDirect, Semantic Scholar, and domain web sources. Use when the user needs coordinated multi-step literature workflows for remote sensing, Earth observation, geospatial AI, GIScience, photogrammetry, SAR, hyperspectral imaging, LiDAR, satellite missions, benchmark datasets, land-cover mapping, change detection, object detection, segmentation, retrieval, or multimodal remote-sensing foundation models. Also trigger on general literature-search needs during academic writing even without the word "Nature", such as searching for papers/literature, doing a literature review, verifying a citation, converting citation files, and Chinese phrasings like 文献检索、查文献、找文献、 文献综述检索、查论文、引文核对、参考文献管理、文献去重.

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。

Polish, restructure, or translate academic prose into Nature-leaning English using writing-strategy principles, curated Nature/Nature Communications article patterns, and phrase-level support from Academic Phrasebank. Use whenever the user asks to polish a manuscript paragraph, abstract, introduction, results, discussion, conclusion, title, methods section, or Chinese academic draft for publication-quality English. Also covers LaTeX layout/typesetting (排版) fixes — loose or sparse pages, stranded section headings, figures that don't fill the page or split across pages, "Float too large", multi-panel figure arrangement, and Supplementary Information that looks empty — via references/latex-layout.md. Also trigger on general academic/scientific writing requests even without the word "Nature", including academic writing, scientific writing, SCI/paper writing, English manuscript polishing, language editing, proofreading, and Chinese phrasings such as 学术写作、科研写作、论文润色、写paper、SCI写作、英文论文润色、语言润色、润色、改写、学术英语、英文写作.

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。

Rewrite dense or technical text into a clear plain-language summary with a fixed structure (TL;DR, why it matters, key points, caveats). Use when the user asks to summarize, simplify, explain, "put this in plain language", or make something readable for a general / non-expert audience.

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。

Perform quick web searches, local business lookups, and URL content extraction. Covers web_search for general queries, brave_local_search for location-based discovery, and run_crawl4ai_job for deep page crawling. Use when fact-checking, trend spotting, doing competitor analysis, looking up local businesses, extracting page content, or performing content research that doesn't need multi-step deep research.

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 the user asks to "build a win-back campaign", "re-engage lapsed subscribers", "run a re-permission / re-consent sweep", or "sunset my dead list"; produces a closed-loop reactivation program — a lapsed-cohort definition, a staged offer ladder, a re-consent (re-permission) capture step, and a sunset-confirm / suppression rule. Owns none of the SEND-N sub-item notes: engagement-decay / sunset is email-sequence-designer's and preference-center / frequency options is preference-frequency-manager's — this skill references those notes, it does not re-emit them. Not for the general lifecycle flows (welcome/cart/post-purchase) — use email-sequence-designer; not for the preference-center / opt-down ladder — use preference-frequency-manager; not for the consent record itself — use consent-registry; not for computing EQS or the N1 veto — use email-quality-auditor. 流失召回/重新授权/沉默用户清理

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。

When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," "overlay," "collect emails with a popup," "exit popup," "scroll trigger," "sticky bar," or "notification bar." Use this for any overlay or interrupt-style conversion element. For forms outside of popups, see cro. For general page conversion optimization, see cro.

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。

When the user wants to create, generate, edit, or optimize images for marketing — blog heroes, social graphics, product mockups, profile banners, listing visuals, or brand assets. Also use when the user mentions 'AI image generation,' 'generate an image,' 'create a graphic,' 'product mockup,' 'hero image,' 'social media graphic,' 'banner image,' 'cover photo,' 'profile banner,' 'listing screenshot,' 'Flux,' 'Flux Kontext,' 'Midjourney,' 'DALL-E,' 'GPT Image,' 'ChatGPT Images,' 'Ideogram,' 'Gemini image,' 'Nano Banana,' 'Recraft,' 'Stable Diffusion,' 'Canva,' 'Figma,' 'image optimization,' 'compress images,' 'WebP,' or 'OG image.' Use this for general-purpose marketing image creation and optimization. For paid ad image creative and platform-specific ad specs, see ad-creative. For video production, see video.

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 the user asks to "design a welcome flow", "set up an abandoned-cart sequence", "build a light re-engagement branch inside a lifecycle flow", or "plan a cold-outbound sequence"; produces general lifecycle automation flows (welcome, cart, browse-abandon, post-purchase, in-flow re-engagement, B2B cold outbound) with trigger, step timing, branch/exit conditions, goal, frequency governance (send caps, quiet hours, fatigue guardrail), a sunset path, and a SEND N-dimension score. Not for the closed-loop win-back / re-consent (re-permission) program on a lapsed cohort — use reactivation-specialist; not for writing each email's copy — use email-creative-builder; not for computing EQS or the N1 unsubscribe veto — use email-quality-auditor. 邮件自动化流程设计/购物车挽回/流失召回序列

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。

Core firmware context for the Megahub ESP32 project. Use when working on PlatformIO configuration, build scripts, library structure, main.cpp, or general ESP32 firmware development. Covers build system, project structure, common pitfalls, and debugging.

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。

사회복지 기관(복지관, 생활시설, 센터, 비영리단체)의 실무자가 업무용 앱을 만들거나, 업무를 자동화하거나, 엑셀·양식을 전산화하고 싶다고 할 때 코드를 만들기 전에 업무의 필요성, 개인정보 등급, 사람에 관한 기록의 윤리, 플랫폼 경로, 운영 조건을 판단하고 DECISION.md로 정리하는 절차. "프로그램일지 앱", "이용자 명부", "출석 체크", "사례관리 기록", "후원자 관리", "차량 운행일지", "복지 앱", "바이브코딩", "희망이음 자동 입력", "에이전트 브라우저로 동기화" 같은 말이 나오거나 사회복지 현장의 행정 업무를 자동화하려 할 때 사용한다. Use before writing any code when a Korean social-welfare practitioner wants to build an app or automate a workflow. Not for general coding 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。

Optimize and convert Zenn Flavored Markdown (ZFM) articles to Dev.to (Forem) compatible Markdown. Use this skill whenever the user wants to post a Zenn article to Dev.to, needs to convert markdown for cross-posting, or asks to "optimize for dev.to". It handles front matter conversion, Liquid tag embeds, KaTeX math blocks, callout transformations, and general formatting adjustments required by Dev.to.

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。

Plan or review FriendlyBet architecture, implementation, schema, data flows, scoring, provider integrations, static/PWA behavior, Supabase/RLS impact, migrations, automation, and technical sequencing. Use when a task asks how to build, refactor, generalize, integrate, test, or safely ship FriendlyBet code.

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。

Schedule meetings, parse meeting notes, extract action items. Three distinct intents covered. Invoke when user mentions a specific meeting (past or future) by name or context. NOT for general calendar checks (use direct calendar.py), NOT for time-zone math. Triggers: "schedule with X", "minutes of the X meeting", "action items from yesterday's meeting", "prep me for meeting with X".

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。

4-layer recall procedure (durable → semantic → PostgreSQL → daily notes) before claiming "no context". Invoke when user explicitly asks "do you remember", "what did we discuss about X", "search history". NOT a general assistant — only fires when recall is the explicit task. Drop the prior "use proactively" rule. Triggers: "do you remember", "what did we discuss", "when did", "find that conversation about".

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。

Multi-tiered memory management (HOT/WARM/COLD) for context compaction. Invoke ONLY for explicit compaction events: post-`/compact` cleanup, MEMORY.md tier promotion, archive batch, or "trim my context". NOT for general recall (use deep-recall) or routine memory writes (use storage-router). Triggers: "compact memory", "promote to durable", "archive old context", "tier this".

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-A LLMs Large Language Models (LLMs) are extensive, general-purpose models that can be pre-trained and adapted for specific 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。

Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.

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 git repositories to build a security ownership topology (people-to-file), compute bus factor and sensitive-code ownership, and export CSV/JSON for graph databases and visualization. Trigger only when the user explicitly wants a security-oriented ownership or bus-factor analysis grounded in git history (for example: orphaned sensitive code, security maintainers, CODEOWNERS reality checks for risk, sensitive hotspots, or ownership clusters). Do not trigger for general maintainer lists or non-security ownership questions.

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。

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.

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。

Perform language and framework specific security best-practice reviews and suggest improvements. Trigger only when the user explicitly requests security best practices guidance, a security review/report, or secure-by-default coding help. Trigger only for supported languages (python, javascript/typescript, go). Do not trigger for general code review, debugging, or non-security 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。

Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.

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。

Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.

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。

Translate Figma designs into production-ready application code with 1:1 visual fidelity. Use for Figma URLs, node IDs, implementation requests, or components that must match Figma specs; use the local `figma` skill for general Figma MCP context and setup.

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。