「exa」の検索結果

Comprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed, structured data.

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Google SEO APIs: Search Console (Search Analytics, URL Inspection, Sitemaps), PageSpeed Insights v5, CrUX field data with 25-week history, Indexing API v3, and GA4 organic traffic. Provides real Google field data for Core Web Vitals, indexation status, search performance, and organic traffic trends. Use when user says "search console", "GSC", "PageSpeed", "CrUX", "field data", "indexing API", "GA4 organic", "URL inspection", or "real CWV 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。

Technical SEO audit across 9 categories: crawlability, indexability, security, URL structure, mobile, Core Web Vitals, structured data, JavaScript rendering, and IndexNow protocol. Use when user says "technical SEO", "crawl issues", "robots.txt", "Core Web Vitals", "site speed", or "security headers".

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。

Comprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed.

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。

Save bounded progress as kernel.checkpoint/v1 for exact resume after reset. Triggers: checkpoint, save progress, context reset, compact soon, long task.

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 presenting results for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript — propositions, numerical examples, schematic theory figures, and empirical/experimental tables. Builds exhibits to AEA house norms; it does not derive the results (see aejmic-theory-model).

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Use when revising an ACL paper for computational-linguistics house style, covering task-first framing, linguistic examples tied to quantitative error analysis, scoping language claims to tested languages, LLM-era claim discipline, anonymous self-reference, Limitations prose, and compressing into the 8-page or 4-page ACL format.

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 writing or verifying the institutional / accounting-standard / regulatory background for a 《会计研究》 (Accounting Research) manuscript — getting standard clauses, regulatory rules, and effective dates exactly right, since precise institutional detail is a defining bar of this journal. Use before measurement and identification.

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Stitch video segments with ffmpeg concat, xfade, overlay, audio mux, and export settings, and trim a source into named clips at exact windows (trim_clips.py). Ships montage.py, a free montage assembler (python3 + ffmpeg, no keys). It takes a JSON EDL of clips and stills, normalizes them and hard-cuts them in order, burns captions from an SRT, a cue list or word timings, and lays a VO over a music bed that ducks under it, mastered to -14 LUFS.

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。

Scrape G2, Capterra, and Trustpilot reviews for your product and competitors, then extract recurring themes, objections, proof points, and exact customer language for use in messaging. Chains review-site-scraper with LLM analysis. Produces a weekly or monthly digest that feeds directly into copywriting, positioning, and sales enablement. Use when a marketing team needs to ground messaging in real customer language.

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。

Neural web search - find similar content, extract pages, and run 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。

Test and document API endpoints - validate responses, check status, generate examples

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。

End-to-end skill that turns a single reference image into a published Gooseworks style — analyzes the image, drafts the slim style spec, renders a hero example plus 2-3 additional formats via Playwright, writes the `gooseworks-style.json` manifest, and publishes via `npx gooseworks styles publish` so other agents can discover it. Mirrors goose-graphics-create-format but for styles.

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Final QC gate for a rendered 9:16 video ad, run before it is published. One script checks what a machine can decide — exact 1080x1920 size, a hook that moves and speaks in the first second, no frozen stretches, no dead air, no black frames, the brand's real logo on the end card (never a favicon, never a redrawn or wrong logo), end-card colours near the brand palette — and builds one contact sheet (frames with the TikTok/Reels UI safe zones shaded, beside the logo, product images and a font specimen) for the checks that need eyes — font, product likeness, product consistency across scenes, safe zones. A declared silent-text profile covers silent, text-led formats such as kinetic text (no audio track needed, short blank beats between text beats allowed within fixed bounds). Exit 0 PASS / 2 FAIL / 3 ERROR. Use on every finished video master before pinning it.

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。

Given the path to a finished content-goose ad-run folder, extract everything that defines that ad — recipe shot list, VO script, characters, voices, world, atom-skills, master mp4 — and emit a `source-sample.json` in the exact shape the `upload-ad-sample` skill writes to the Goose Ads library. Also links every character and voice to the central character library at `<repo-root>/assets/character-library/` (repo-root derived from the run-dir, not a hardcoded path), and if a character isn't in the library yet, adds it first then links. Use when the user wants to remix one of their existing ads — this skill produces the source JSON that the script-rewriting step and `remix-ad` consume.

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。

Assemble a myth-vs-fact kinetic-typography explainer video ad (≈29.5s, 9:16) from N myth/fact pairs + hook / turn / punch copy + palette + a brand end-card PNG + a VO track — a hook, 3 red-strike MYTH cards that flip to teal-check FACT cards (per-line strikethrough that crosses EVERY wrapped line), a "what actually works" turn, an optional proof reveal, a punch line, and a static end card. DETERMINISTIC assembly with ZERO AI-gen visuals — HTML hyperframes rendered frame-exact via Playwright (`window.renderAt(t)`, animation a pure function of beat-local time), Whisper beat-snap to VO word onsets, concat at a uniform fps, karaoke `.ass` captions burned last (suppressed on the proof + end-card beats), and a VO + optional music mix (music −20 dB, `amix normalize=0`, tail fade). FREE (Python + Playwright + ffmpeg); the recipe supplies the copy / palette / end-card / VO and gates the paid VO / music / Whisper calls to their own capabilities. Use for the myth-vs-fact format.

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。

Assemble the FREE steps of the flat-vector-explainer video format — a flat-illustration host character (who, where, tone, voice and music come from the recipe choices) walks a countable N-step product routine, one step per beat, and Remotion composites every chip/numeral/tagline/slate/CTA as an animated DOM overlay ON TOP of the Kling i2v character clips (text is NEVER baked into a keyframe — i2v warps type), the closing 'N products' grid is a PIL composite of the REAL product photos (not AI), full-sentence VO drives word-by-word burned captions over a VO-forward music bed, and the ~50s animated silent master is re-cut to a 30s deliverable FROM the animated master (never a static intermediate). Documentation-grade — ships config.example.json + PIPELINE.md + a README of the free assembly; the paid gen steps (keyframes, Kling i2v, VO, music) are separate capabilities the recipe orchestrates. Use for the flat-vector-explainer format.

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Use the skill to control and verify the code interface configuration of your model — how Simulink® model elements are represented in the generated C or C++ code. This includes — (1) specifying names, types (storage classes), and placement of variables that represent model elements (for example, making a model parameter tunable as a global extern variable); (2) specifying names, types, and placement of functions that represent model algorithms; (3) selecting the deployment type (Component, Subcomponent, or Automatic); (4) selecting the interface configuration type (data or service interface); (5) linking a shared Embedded Coder dictionary to a model; (6) creating Embedded Coder dictionary entries and setting their properties; (7) for service interface configuration — specifying service interface definitions, including sender, receiver, client, and server services. Items 1 and 2 can be set per element or as a category-wide default. Item 1 applies to GRT and ERT models; the rest to ERT models only.

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Reads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values() is mandatory, the NaN-not-zero gap-handling fork, choosing mean vs max vs sum vs coverage by biological question, and the sorted-bedGraph plus chrom.sizes build requirement. Use when extracting signal at regions, computing mean signal per gene/peak, building a browser track from bedGraph, comparing tracks, or building TSS/gene-body metaprofiles.

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Corrects the gene-independent copy-number artifact in CRISPR-Cas9 screens (Aguirre 2016 / Munoz 2016 Cancer Discov) where amplified loci appear essential from DNA-damage burden of simultaneous cuts. Covers the gene-independent DNA-damage / G2-arrest mechanism, CRISPRcleanR (Iorio 2018) unsupervised pre-hoc correction, CERES (Meyers 2017) joint CN + gene-effect model, Chronos (Dempster 2021) DepMap-standard population-dynamics + CN model with lowest residual bias, the decision tree by data availability, the Spearman LFC-vs-CN diagnostic, focal-amplification examples (ERBB2 in HER2+, MYC in colorectal, FGFR1 in head and neck), and CRISPRi/a alternatives that bypass the artifact. Use when screening cancer cell lines, diagnosing essentiality at amplified loci, choosing CRISPRcleanR / CERES / Chronos, deciding whether CN correction is needed before MAGeCK / BAGEL2 / drugZ, or switching from Cas9 to CRISPRi.

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Tests associations between categorical variables in clinical data using chi-square, Fisher's exact, Boschloo, Cochran-Mantel-Haenszel, and modern McNemar variants with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen). Use when analyzing categorical outcomes, paired binary endpoints, or testing treatment-outcome independence in confirmatory or exploratory clinical trials.

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。

Decode the marker fixture used by the DeepSeek Harness integration example.

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。

Produce only the assigned result, protect other owners' work, run the listed checks, and report an exact conflict instead of expanding scope.

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。

Independently review the exact result and run its real checks in one context, using isolated resources and without changing the deliverable.

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。