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概要と使いどころ
This skill should be used when the user asks to "analyze experimental results", "run strict statistical analysis", "compare model performance", "generate scientific figures", "check significance", "do ablation analysis", or mentions interpreting experiment data with rigorous statistics and visualization. It focuses on strict analysis bundles, not Results-section 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。
Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python 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。
Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic online, search the web, look something up, find current information, compare options, or produce a research 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。
Detect and humanize AI-generated Chinese text. 20+ rule detection categories plus statistical features (sentence-length CV, short-sentence fraction, comma density, perplexity, GLTR, DivEye) plus scene-aware LR fusion (rule × 0.2 + LR × 0.8) trained on three scenes: general / academic / longform 长文本 (≥1500 字)。Unified CLI: ./humanize {detect,rewrite,academic,style,compare}. 8 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo/novel)。 Multi-paragraph rewriting (paragraph length CV、跨段 trigram 重复) plus best-of-N humanize (默认 N=10 取最低 LR)。165 replacement patterns + CiLin 同义词词林 38873 with collision blacklist。 Academic paper AIGC reduction for CNKI/VIP/Wanfang (知网/维普/万方 AIGC 检测降重)。 Pure Python, no dependencies, offline。v5.0.0 — HC3 fused 准确率 95%、学术 hero 100→35 (-65)、 工作汇报 96→13 (-83)、长篇博客 96→41 (-55)。 Use when user says: "去AI味", "降AIGC", "人性化文本", "humanize chinese", "AI检测", "AIGC降重", "去除AI痕迹", "文本改写", "论文降重", "知网检测", "维普检测", "AI写作检测", "让文字更自然", "detect AI text", "humanize text", "reduce AIGC score", "make text human-like", "去ai化", "改成人话", "去机器味", "降低AI率", "过AIGC检测", "长文本改写", "小说改写"
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 asked to compare, cross-review, merge, judge, choose, or arbitrate competing plans from multiple agents such as Codex and Claude Code; when given two or more proposed plans, session IDs, transcripts, plan documents, PR descriptions, or pasted strategies; or when the user wants one recommended execution plan after agents review each other's 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。
Investigate a topic against preserved sources and write a draft-status research article under `research/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when asked to research a topic, compare options, synthesize sources, gather evidence, or extend an existing research doc. Carries the full procedure: scan existing coverage, agree a research rubric, capture every source verbatim before analyzing, write the article incrementally so a crash never loses work, cite every claim, and link it back into the graph. Does not promote findings to canonical knowledge — that is the sibling `consolidate-notes` skill, after a decision lands.
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。
Compares your key metrics against industry benchmarks for your specific vertical, campaign type, and platform. Tells you where you're ahead, where you're behind, and where there's room to improve — adjusted for your account size and spend level, because a $10K/month account and a $500K/month account have different benchmark realities. 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。
Track how AI assistants describe your brand over time and catch wrong, outdated, or damaging claims. Claude builds your monitoring prompt set, compares runs week over week, and drafts the correction plan when answers drift. Platform: AI visibility.
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 system architecture, simplify boundaries, or compare interface designs from repository evidence
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。
Compare the user's notes with an expert's work on a topic. Return relevant exact quotes, source locations, and at least one gap in the user's thinking. Use only when /signal-from-expert is explicitly invoked.
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。
Search and inspect external sources for product, design, technical, or comparative research; verify provenance and turn findings into decisions. Use for requests to investigate online, research X posts or demos, compare approaches, or deepen a research task. Complements a research orchestrator without taking over its state or stop conditions.
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 code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.
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。
Deploy a significant code change (backend, kernel, optimizer, data path) to the live hero run: relaunch it under a new run id from a permanent checkpoint, compare against the old run over a trial window, and roll back if the gate fails; use only when the user asks to deploy a change to the hero.
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 explicitly requested Zephyr control/treatment benchmark on the same pre-normalized sample and compare Finelog stage metrics.
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。
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc 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。
SEO drift monitoring — snapshot a site's SEO state and detect regressions over time. Captures a baseline (rankings/positions, indexed page count, titles & meta descriptions, canonical/robots directives, schema presence, key on-page elements) and on later runs diffs against it to surface what changed: ranking drops, pages that fell out of the index, titles/metas that were accidentally overwritten (a CMS/redeploy classic), canonicals or noindex flipped, schema that disappeared. Use this skill when the user wants to monitor SEO over time, catch regressions after a site change / migration / redeploy, set a baseline, diff against a previous state, or asks "what changed on my site's SEO" or "did my redesign break SEO". Trigger on: "SEO drift", "SEO monitoring", "track SEO over time", "did my site change break SEO", "after migration SEO", "SEO regression", "baseline my SEO", "compare SEO to last month", "my titles changed", "pages fell out of the index". For a one-time full audit use /seo-analysis.
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。
Competitor page gap analysis — take a query you want to win and the pages currently outranking you, and produce a concrete brief on what to add or change to compete. Compares content depth and coverage (subtopics they cover that you don't), structure (headings, format, tables, media), search-intent match, E-E-A-T signals, schema/rich-result eligibility, internal linking, and on-page optimization — across your page vs. the top-ranking competitors. Use this skill whenever the user wants to know why a competitor outranks them on a specific query, what a top-ranking page does better, a content-gap analysis against specific competitor URLs, or a brief to beat a specific SERP. Trigger on: "why does competitor X outrank me", "content gap analysis", "competitor page analysis", "what does the top result have that I don't", "how do I beat this page", "analyze competitor pages", "SERP gap", "compete for [keyword]". For seed keyword discovery use /keyword-research; for a content calendar use /content-planner.
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。
Score and diagnose Google Ads landing pages. Use when asked to audit a landing page, check landing page quality, diagnose high-CTR but low-conversion-rate ad groups, improve Quality Score's Landing Page Experience component, or compare an ad group's messaging against its landing page. Trigger on "landing page audit", "landing page score", "landing page quality", "why is my conversion rate low", "LPX", "landing page experience", "ad to page match", or when `/google-ads-audit` surfaces a high-CTR / low-CVR ad group.
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。
Diagnose LazyCodex and Codex CLI installation health against the latest sources. Use whenever the user asks for a doctor or health check, says LazyCodex, lazycodex-ai, omo-codex, or Codex behaves oddly after an install, update, or config change, suspects a stale, drifted, or broken setup, or wants the local install audited and compared with the latest LazyCodex and Codex 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。
Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit
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。
Compare current benchmark results against historical baselines to spot performance regressions. Use as an explicit/manual helper for build-to-build degradation review, not for broad optimization strategy or low-level profiling ownership.
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。
Compare option candidates and score components, with funding, expiry and assignment risks. Use when the user asks to options strategies with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
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。