「search skill」の検索結果

根据研究者提供的**研究计划书（Research Proposal）**执行基于中国制度环境的公司金融类实证研究全流程。**启动后第一件事：根据计划书的主题、识别策略、贡献边际与样本范围，从中国-context 英文顶级期刊池（JF/JFE/RFS/JFQA/MS/JCF/JBF/JAR/JAE/TAR/CAR/JIBS/China Economic Review/PBFJ 等 25+ 期刊）中推荐 5 本最匹配的目标期刊（[J1]–[J5]），等待研究者明确选定一本；该期刊决定 main.tex 的 bibliographystyle、Section 骨架、Introduction 风格与表注规范**。然后用 Python 完成数据清洗、描述性统计、基准回归、内生性检验（IV/2SLS、DML）、平行趋势、异质性、机制、稳健性检验与图表绘制。LaTeX 表格和图像严格遵循 template/ 示例格式，研究逻辑与排版严格遵循 rule/ 下的《通用实证研究逻辑与规范总结》与《回归表写作规范总结》。数据集与政策集从 asset/ 中按计划书中的关键词检索。**当计划书预期的实证结果无法实现时（系数不显著、平行趋势不通过、IV 弱工具、机制不成立等），skill 自动切换备选方案直至完成研究项目**。最终交付物：Python 代码 + LaTeX 表格 + 图像（.pdf/.png）。触发条件：研究者提交研究计划书（含 X→Y 假设、识别策略、样本、政策冲击等）。

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Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `teffects ipw` / `teffects ipwra` / `teffects aipw` / `eltmle`, Mendelian randomization via `mrrobust` (IVW / Egger / weighted median) and `mregger` / `mrpresso`, KM / Cox / AFT / RMST survival via `sts` / `stcox` / `streg` / `strmst2`, E-value sensitivity via `evalue` (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `ddml` / `pdslasso`, S/T/X/R/DR meta-learners via `crforest` and `ddml interactive`, causal forest via `crforest` / `cforest`, BART/BCF via `bart` / `bartCause`-style externals, CATE distribution + policy tree via `crforest`, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via `pcalg` / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".

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。

Integrate AI tools into economic research, teaching, and policy analysis with attention to privacy and reproducibility.

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Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.

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。

Remove AI writing patterns from prose. Use this skill when writing, drafting, editing, reviewing, or revising any text to eliminate predictable AI tells, slop, and formulaic patterns. Trigger this skill whenever the user asks to "deslop", "de-AI", "make it sound human," "remove AI patterns," "remove AI tropes," "clean up AI writing," fix "slop," "deslop" text, or review prose for authenticity. Also use when the user asks you to write or draft anything and wants it to sound natural rather than AI-generated. Common use cases include scientific writing (manuscripts, abstracts, cover letters, grant narratives, discussion sections, peer review responses), blog posts, newsletters, memos, reports, and any other substantial prose.

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。

Write competitive research proposals for NSF, NIH, DOE, and DARPA. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.

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。

中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact. Since 2026-08 it also carries a provenance layer (去水印): deterministic, CJK-safe cleaning of invisible-character carriers and file metadata (docx / png / jpg / svg / pdf, incl. C2PA), an honest account of Claude's statistical text watermark, and an author "ownership pass" — it never claims a text is watermark-free.

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。

Research a topic across the web, GitHub, and X/Twitter with DeepAPI and produce a concise Markdown report. Use for varied-search or requests to compare web coverage, relevant repositories, and social discussion.

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。

Research technology and advanced-manufacturing investments using Serenity-inspired supply-chain bottleneck analysis. Use for theme scans, company thesis challenges, candidate comparisons, or learning this method. Prioritize A-shares unless another market is requested. Return research priorities, dated evidence, profit implications, and conditions that would change the judgment.

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深度研究代理系统社区版，对复杂主题进行多轮迭代的网络搜索和综合分析。使用场景：当用户需要对特定主题进行深入、全面的研究时，特别是需要基于最新网络信息生成详细分析报告的情况。支持多轮迭代搜索、智能搜索方向调整和综合分析报告生成。本skill是一个工作流描述文档，没有执行脚本，依赖web-search skill进行网页搜索。

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。

Image SEO audit — make a site's images discoverable in Google Images and stop them from dragging down Core Web Vitals. Audits alt text quality and coverage, descriptive file names, modern formats (WebP/AVIF), dimensions and compression, responsive srcset, lazy-loading, explicit width/height (CLS), image sitemaps, and ImageObject structured data. Use this skill whenever the user asks about image SEO, alt text, Google Images ranking, image optimization for search, image file size hurting page speed, WebP/AVIF, or image structured data. Trigger on: "image SEO", "alt text", "rank in Google Images", "optimize my images for search", "image alt tags", "WebP", "image compression SEO", "image sitemap", "ImageObject schema", or any image-discoverability / image-performance question. For overall page speed use /seo-analysis; for generating new images use the brand image skills.

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。

Self-improving browser automation via the auto-research loop. Iteratively runs a browsing task, reads the trace, and improves the navigation skill (strategy.md) until it reliably passes. Supports parallel runs across multiple tasks using sub-agents. Use when you want to build or improve browser automation skills for specific website 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。

Use when the user wants to research competitors' social media strategy, compare brands or creators, find what content is working in a niche, identify content gaps, or produce a practical social strategy brief from public social 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 the user wants to validate a product idea, find pain points, mine demand signals, discover objections, or gather voice-of-customer language from Reddit, social posts, video transcripts, and comments. Produces evidence-backed product 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。

Deep web research for VCO: multi-hop search+browse+extract with an auditable action trace and a structured report (WebThinker-style).

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Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.

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。

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

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。

Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).

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Query the OFR (Office of Financial Research) Hedge Fund Monitor API for hedge fund data including SEC Form PF aggregated statistics, CFTC Traders in Financial Futures, FICC Sponsored Repo volumes, and FRB SCOOS dealer financing terms. Access time series data on hedge fund size, leverage, counterparties, liquidity, complexity, and risk management. No API key or registration required. Use when working with hedge fund data, systemic risk monitoring, financial stability research, hedge fund leverage or leverage ratios, counterparty concentration, Form PF statistics, repo market data, or OFR financial research data.

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Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring RL or NeMo-gym runs, launching reproducible baselines and iterations, analyzing results, preserving human oversight, and using git plus TSV logs as the research ledger. Do NOT use for: bug fixes, code review, documentation, refactoring, dependency updates, or single-file changes.

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Apply plugin knowledge base updates to an existing generated system. Consults the Ars Contexta research graph for methodology improvements, proposes skill upgrades with research justification. Never auto-implements. Triggers on "/upgrade", "upgrade skills", "check for improvements", "update methodology".

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Find published research and institutional views with sources, dates and related assets. Use when the user asks to research reports with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

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Generic read-only fallback for sources opencli supports but no dedicated skill covers: finance sites (Yahoo Finance, Bloomberg, Reuters, Barchart, Eastmoney, Xueqiu, Sinafinance), communities (Reddit, HackerNews, Weibo, Zhihu, Xiaohongshu, Bilibili), newsletters and blogs (Substack, Medium), research (arXiv, Google Scholar), podcasts and video (Apple Podcasts, Xiaoyuzhou, Spotify, YouTube), Weixin, Amazon, and more. Use it when the user asks to read or fetch from such a site — for example r/wallstreetbets, the HackerNews front page, Eastmoney hot stocks, the Xueqiu feed, Bloomberg markets headlines, or an arXiv search — or says "use opencli to read". Prefer twitter-reader, linkedin-reader, discord-reader, telegram-reader, or yc-reader when the source matches. Read-only: never runs write commands.

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Read Twitter/X for financial research through opencli: the user's home timeline, tweet search, trending topics, bookmarks, a user's recent tweets, threads, articles, profiles, followers and following, and notifications. Use this skill whenever the user wants to know what people are saying on Twitter/X or fintwit about a stock, topic, or market event, check their feed or bookmarks, see what's trending, or look up an @account and its posts. Read-only: it cannot post, like, retweet, reply, or follow.

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。