「paper」の検索結果

Generate PDFs, resumes, CVs, letters, slide decks, portfolios, one-pagers, white papers, and professional documents. Use when user asks to create a PDF, make a resume, write a letter, design slides, format a document, typeset a report, build a portfolio, make a presentation, or create a one-pager. Warm parchment, ink-blue accent, serif-led hierarchy. CN uses TsangerJinKai02, EN uses Charter, JA uses YuMincho (best-effort). Triggers on Chinese: "做 PDF / 排版 / 一页纸 / 白皮书 / 作品集 / 简历 / PPT / slides". Do NOT use for code formatting, data charts, or wireframes.

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Any live-web task via the Firecrawl CLI — including ordinary web research: searching the web, reading or extracting pages, gathering sources, discovering site URLs, bulk extraction, downloading a site, change alerts, or pages needing clicks/login — web only; local files route to firecrawl-parse. For papers use firecrawl-research-index; for library, API, error, or bug questions use firecrawl-developer-index.

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Handle small bureaucratic tasks — jury duty responses, parking tickets, passport renewals, DMV forms, permit applications, and other government or administrative paperwork.

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。

Discovers and triages recent arXiv papers for AI/ML, agents, and software/QA. Use when scouting categories, arXiv IDs, or source lists.

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Claude adapter for diagnosing PlainTab bugs, broken flows, startup issues, storage or wallpaper regressions, and performance regressions.

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 agent or user is unfamiliar with a PlainTab module, needs a higher-level map of related files and callers, or asks how a section fits into startup, wallpaper, settings, search, command palette, storage, or i18n flows.

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 diagnosing PlainTab bugs, broken UI behavior, failing extension/web-mode flows, storage or wallpaper regressions, startup issues, or performance regressions.

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 before changing PlainTab persistent storage, including localStorage keys, IndexedDB keys or record formats, `LS_VERSION`, `DB_VERSION`, `ptab_schema_version`, migration code, backup/import/export storage shape, wallpaper cache cleanup, or any task that may affect old users upgrading across versions.

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 accurate, runnable quantitative strategy code for the JoinQuant (聚宽) platform. Use this skill when the user asks to write a stock/ETF/futures backtest, paper-trading, or live-trading strategy on JoinQuant — the skill provides progressive-disclosure access to the complete JoinQuant API knowledge base (14 categories, 294KB official docs), 5 production-ready strategy templates (single-stock MA, multi-factor, ETF rotation, momentum, mean-reversion), and a lint tool that catches hallucinated APIs, future-function violations, and unset price-mode/slippage/commission. Chinese-friendly. Code generated by this skill should paste directly into the JoinQuant online editor without 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。

Use when the user asks for a literature review, academic deep dive, research report, state-of-the-art survey, topic scoping, comparative analysis of methods/papers, grant background, or any request that needs multi-source scholarly evidence with citations. Also trigger proactively when a user question clearly requires academic grounding (e.g. "what's known about X", "compare approach A vs B in the literature", "summarize the field of Y"). Runs an 8-phase (Phase 0..7), script-driven research workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa) with optional Semantic Scholar / Brave MCP enrichment, with deduplication, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), self-critique, and structured report output with verifiable citations.

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 whenever the user mentions Zenodo, depositing or publishing research artifacts (datasets, software, papers, posters) to Zenodo, minting a DOI for a dataset/code release, uploading files to a Zenodo record, creating a new version of a Zenodo deposit, or searching Zenodo records. Covers the full Zenodo REST API workflow — create deposition, upload files via the bucket API, set metadata, publish, version, and search — for both production (zenodo.org) and sandbox (sandbox.zenodo.org).

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GPS-aligned genealogical research assistant for family history, ancestor research, record analysis, source-information-evidence classification, conflict resolution, citations, FAN research, privacy-sensitive document analysis, and proof statements, summaries, or arguments. Use for census, vital, probate, land, military, church, newspaper, DNA-plus-documentary, and other genealogy evidence tasks. Not for GEDCOM creation (use gedcom-creator), general history questions, DNA-kit shopping, or generic writing 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。

Parse PDFs, Office files, images, and HTML into Markdown/structured outputs with MinerU. Use when SciForge or Codex needs document parsing/OCR for scientific papers, supplementary files, PDFs, scanned documents, tables, formulas, or URL/local-file parsing through MinerU standard or Agent lightweight APIs.

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Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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Lead-Magneten und Erfassungsstrecken einwilligungsbasiert und transparent planen, erstellen und prüfen. Nutze diesen Skill für Whitepaper, Checklisten, Vorlagen, Rechner, Webinare und Kurse hinter einem Formular, für die Entscheidung ob überhaupt gegated wird, und für die Prüfung eines vorhandenen Formulars oder Funnels. Hält Formulare minimal, trennt die Marketingeinwilligung von der Auslieferung, ordnet Double-Opt-in und TDDDG-Fragen ein und achtet auf Auftragsverarbeitung und sichere Bereitstellung. Setzt nichts live und richtet kein Tracking ein.

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。

Audit the reasoning of a document or argument (memo, proposal, investment analysis, board paper, article, the user's own draft): claims, evidence, unstated assumptions, logical gaps, fallacies, and what would falsify it, each anchored to quoted text. Use whenever the user wants reasoning examined, in any language ("audit this argument", "is this analysis sound", "poke holes in this proposal", "review the logic of my draft"; Vietnamese "phản biện giúp tôi", "soi lập luận này", "tài liệu này có lỗ hổng gì", "góp ý bản nháp của tôi"), even when they never say "critical thinking". Also use when the user drops a document and asks whether to trust or act on it, or asks to see their reasoning profile. Not for company-level strategic bets (strategy-board) or learning a topic through dialogue (socratic-questor).

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 the user asks about "production management", "film budget", "scheduling", "crew management", "location logistics", "risk assessment", "act as a production manager", "production manager mode", "line producing", "production budget", "crew roles", "film insurance", "production timeline", "location permit", "production paperwork", or needs expertise in film budgets, production scheduling, crew management, location logistics, and risk mitigation for film projects. Part of the AlterLab FC Skills collection (Cinema & Digital Media department).

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Guide a researcher step by step into an unfamiliar research field, or decode a paper, abstract, figure caption, or referee comment they cannot parse. Builds understanding in rungs (motivation, vocabulary, core framework, methods, frontier), anchored to what the user already knows, with a checkpoint before each advance. Use when the user says they are new to a field, asks what a research area or method is, says an explanation was too technical, asks to be walked through something step by step, asks for a reading path, or supplies dense research text. Trigger even when the user only names an unfamiliar field or pastes an abstract without asking to be taught. Do not trigger for a one-line summary, a short overview request, or a direct factual answer; do not use it for translation, editing, search, debugging, or specialist questions inside the user's own field. Also trigger in other languages, including Chinese such as 入门, 一步一步讲, 看不懂, 这篇论文讲什么, 帮我理解这个领域.

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。

Draft equity plan documents — option grants, vesting schedules, 409A notes. Use when asked to "draft an option grant", "write a vesting schedule", or "prepare 409A paperwork".

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Build and maintain a persistent, LLM-maintained markdown wiki following Andrej Karpathy's LLM Wiki pattern — the agent incrementally compiles raw sources into an interlinked knowledge base instead of re-deriving answers from scratch. Use this whenever the user wants to ingest a source (an article, paper, transcript, chapter, meeting notes), file knowledge or conversation findings into the wiki, query the accumulated knowledge base, lint or health-check the wiki, or mentions "the wiki", "knowledge base", "ingest this", "file this", "what do we know about X", or accumulating notes on a topic over time — even if they don't say "LLM Wiki" explicitly.

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。

Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.

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。

ALIFE 2025 Proceedings - 337 pages, 80+ papers on artificial life, evolution, emergence, cellular automata, neural networks, agent-based models. Full Mathpix extraction with equations and figures.

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Anna's Archive integration for academic paper and book retrieval. Search shadow libraries (Library Genesis, Z-Library, Sci-Hub) via unified API with GF(3) balanced caching.

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Comprehensive Artificial Life skill combining ALIFE2025 proceedings, classic texts (Axelrod, Epstein-Axtell), ALIEN simulation, Lenia, NCA, swarm intelligence, and evolutionary computation. 337 pages extracted, 80+ papers, 153 figures.

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。