「rl」の検索結果

Cleans a Quarto (.qmd) file by replacing LaTeX holdovers with proper Quarto/Pandoc syntax so the document renders correctly to HTML, PDF (Typst/LaTeX), and Word. Fixes citations (\citep → [@key]), cross-references (\ref → @label), figures (\includegraphics → markdown), tables, text formatting (\textbf → **bold**), inline math (\( \) → $ $), hyperlinks (\href → [text](url)), footnotes (\footnote → ^[]), list environments, section headings, and R Markdown chunk options (dot-style → #| YAML). Use when asked to clean, fix, modernize, or port a Quarto or R Markdown document; when LaTeX commands appear in a .qmd file; when a document fails to render to Word or Typst; or when citations/cross-references do not appear in non-LaTeX output.

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 covers Bayesian estimation and inference in quantitative social science. Use when the user is specifying priors, running MCMC, diagnosing chain convergence, or reporting posterior summaries — including hierarchical models, Bayesian structural models, and small-sample settings where priors regularize. Triggers on "Bayesian estimation", "Bayesian inference", "MCMC", "Markov chain Monte Carlo", "Stan", "PyMC", "NumPyro", "prior", "posterior", "credible interval", "Bayesian structural", "Bayesian BLP", "Bayesian DSGE", "hierarchical model", "random effects Bayesian", "posterior predictive check", "Bayes factor", "prior predictive check", "NUTS", "HMC", "Hamiltonian Monte Carlo", "R-hat", "rhat", "effective sample size", "ESS", "Bayesian calibration", "posterior distribution", "prior elicitation", "weakly informative prior", "brms", "rstanarm", "cmdstanpy", "pymc", "arviz".

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Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting multi-column regression tables to Word / Excel / LaTeX (Stata outreg2 / esttab / R modelsummary equivalent) and bundling an entire replication appendix into one .docx / .xlsx / .tex file. Triggers on keywords "StatsPAI", "statspai", "AER empirical analysis", "applied micro pipeline", "Table 1 balance", "event study", "first-stage F", "Oster bound", "honest_did", "spec_curve", "callaway_santanna", "dragonnet", "text as treatment", "outreg2 in Python", "regression table to Word/Excel", "sp.regtable", "sp.collect", "sp.paper_tables", "sp.feols", "summary_col", "modelsummary", "AER style table", "QJE style table", "epidemiology pipeline", "target trial emulation", "g-formula", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "TRIPOD", "公共健康", "流行病学", "DML", "double machine learning", "causal forest", "meta-learner", "CATE", "conformal causal", "policy learning", "因果机器学习", "ML causal", "decomposition", "Oaxaca-Blinder", "Kitagawa", "DiNardo-Fortin-Lemieux", "DFL", "Gelbach", "RIF decomposition", "wage gap decomposition", "sp.decompose", "sp.oaxaca".

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。

Systematic competitive analysis covering positioning, messaging hierarchy, objection handling, and CTA strategy from landing page URLs or screenshots. Use when pasting a competitor URL, uploading competitor screenshots, requesting positioning analysis, or needing to understand competitive messaging and differentiation strategies. Platform: Google and Meta.

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。

Compares your Meta ad sets and identifies where audiences overlap significantly, causing your ads to compete against each other in the same auctions. Tells you exactly which ad sets are cannibalizing each other and how much it's costing you in inflated CPMs. Platform: Meta.

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。

Organize menus and visit notes with grounded tasting analysis and comparisons to earlier experiences. Use when the user shares a restaurant menu, wants a tasting write-up, or asks to merge a visit into Notes/Gourmet.md without overwriting earlier visits.

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。

XML sitemap audit — find and fix the sitemap problems that quietly waste crawl budget and slow indexing. Discovers the sitemap (robots.txt, /sitemap.xml, sitemap index), validates structure and size limits, and cross-checks the URLs it lists against reality: non-200 / redirected / noindex / canonicalized-away URLs that shouldn't be in a sitemap, plus indexable pages that are missing from it. Reviews lastmod accuracy, sitemap-index organization, and robots.txt reference. Use this skill whenever the user asks about sitemaps, sitemap errors in Search Console, "sitemap couldn't fetch / has errors", crawl budget, pages not getting indexed, or whether their sitemap is clean. Trigger on: "sitemap", "sitemap.xml", "XML sitemap", "sitemap errors", "sitemap audit", "couldn't fetch sitemap", "crawl budget", "pages not indexed sitemap", "sitemap index", "lastmod", "robots.txt sitemap", or any sitemap/crawl-coverage question. For a full-site SEO audit use /seo-analysis; for broken links use /broken-link-checker.

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。

International / multilingual SEO audit focused on hreflang correctness: missing or broken return tags, wrong language/region codes, missing x-default, self-referencing errors, hreflang vs canonical conflicts, and inconsistent signals across HTML head, HTTP headers and XML sitemap. Also reviews URL structure (ccTLD vs subdirectory vs subdomain), language targeting and geo signals. Use whenever the user runs a multi-language or multi-region site and asks about hreflang, international or multilingual SEO, the wrong-language page showing in search, or duplicate content across country versions. Trigger on: "hreflang", "international SEO", "multilingual SEO", "multi-region", "wrong language in Google", "x-default", "ccTLD vs subdirectory", "geo targeting", "language targeting", "my English page shows for Thai users", "th/en page ranking in the wrong country", or any cross-language/cross-country ranking question. For full-site audits use /seo-analysis; for a single URL use /seo-page.

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 this skill when the user wants to search the web without a full browser session: find URLs, titles, and metadata for a query. Prefer it over a browser when you just need search results, not page content. Returns structured results with titles, URLs, authors, and dates.

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。

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

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 planning an end-to-end scholarly publishing workflow, including manuscript source-of-truth, submission assets, revision/rebuttal files, camera-ready checks, reproducible build expectations, and publication package structure.

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。

Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.

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。

Python library for accessing, analyzing, and extracting data from SEC EDGAR filings. Use when working with SEC filings, financial statements (income statement, balance sheet, cash flow), XBRL financial data, insider trading (Form 4), institutional holdings (13F), company financials, annual/quarterly reports (10-K, 10-Q), proxy statements (DEF 14A), 8-K current events, company screening by ticker/CIK/industry, multi-period financial analysis, or any SEC regulatory filings.

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。

Look up people's LinkedIn profiles by URL with Datacircle, no markup, and manage the Datacircle balance and files. Use when the user gives LinkedIn profile URLs to enrich or research (a candidate, a customer, a person in their CRM), asks what their Datacircle balance is, or wants the co-op flat file.

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将数据可视化为图表。当用户需要生成柱状图、折线图、饼图、散点图、雷达图、桑基图、思维导图、流程图等图表时调用此技能，通过 curl 工具调用 AntV API 生成图表图片。产出静态图片 URL（25+ 类型）；要本地渲染的信息图/GIF 动画图表用 infographic，要 CSV/JSON→整页报告用 data-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。

Convert existing Hugging Face Transformers Trainer or TRL SFTTrainer training code into an NVFLARE federated job using flare.patch(trainer), local validation, and job export; use when the user names Hugging Face or preliminary source inspection identifies one Hugging Face owner, and not for manual PyTorch loops, Lightning, inference-only pipelines, deployment, or experiment workflows.

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。

NVIDIA App MCP: drivers, games, laptops, overlay. Check drivers, manage and optimize games, configure laptop features.

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。

Documentation conventions for NeMo-RL. Covers docs/index.md updates and docstring format. Do NOT use for: bug fixes, test fixes, dependency bumps, refactoring, CI/CD changes, performance tuning, or any task that does not involve writing or updating documentation.

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。

Operational guide for enabling TP, DP, and PP communication overlap in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.

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。

[omh] Shell command or test-suite execution: policy overlay for terminal commands - add cwd, environment, safety, and result-evidence gates after preferring native shell tools for ordinary CLI, package-manager, and test runs. Use when the user says: command-operator, command operator, terminal command, terminal task, shell command, shell task, cli command, command execution.

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。

[omh] Hostile scenario testing: adversarial QA and fix loops. Use when the user says: ultraqa, adversarial qa, hostile scenarios, e2e qa, real-world qa, qa scenario, release qa, 敵対的QA.

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[omh] Fine-tuning a model on your own data -- SFT, DPO, RLVR or a LoRA adapter: decide first whether prompting or retrieval already closes the gap, choose the method from the data you have, and promote a checkpoint only when it beats the untuned baseline on a held-out eval. Use when the user says: model-finetuning, model finetuning, model fine-tuning, fine-tune a model, fine-tune the model, fine tune a model, fine tune the model, fine-tune.

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。

[omh] Unvalidated early product idea: test whether a customer problem, segment, and business hypothesis deserve product investment, ending in kill, pivot, persevere, or inconclusive before any PRD. Use when the user says: product-discovery-validation, product discovery validation, product discovery, customer discovery, customer discovery plan, zero to one validation, validate the problem before building, problem solution interview.

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。

[omh] Live rate, score, or local time a work task needs: policy overlay for live lookups - add provider, freshness, units, and source-quality gates after preferring native live-data tools for ordinary weather, finance, sports, maps, and time-zone requests. Use when the user says: live-info-operator, live info operator, live information, real time information, real-time information, weather today, current weather, weather forecast.

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。