1,645 件 ・ 関連度順
概要と使いどころ
Rapidly maps the evidence landscape around a medical topic by organizing major research streams, target populations, endpoints, methods, evidence density, and thin areas. Use this skill BEFORE medical-research-gap-finder — it provides the structured landscape that makes formal gap analysis more rigorous. Do not use for formal gap identification, study design, or protocol planning directly.
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。
Organizes the evidence and competitive landscape around a drug, target, or pathway by separating disease relevance, tractability, preclinical evidence, clinical evidence, modality fit, and crowding. Always map what is biologically supported, what is druggable, what has actually advanced, and what remains strategically open. Never confuse target relevance with druggability, preclinical activity with clinical promise, or narrative excitement with validated development maturity. Never fabricate references, trial status, approval status, company activity, or asset metadata.
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。
Scans the biomarker landscape of a disease area by biomarker type, clinical/research use case, evidence layer, validation status, and maturity level. Use this skill when a user wants a field-level biomarker evidence map rather than a generic literature summary. Always separate exploratory biomarkers from externally validated or clinically embedded biomarkers, and never imply clinical maturity without explicit evidence support.
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 to "analyze competitors", "assess competitive landscape", "identify differentiation", "evaluate market positioning", "apply Porter's Five Forces", or requests competitive strategy 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。
From an idea to a sellable series design; series engine / franchise, story pattern, character webs that keep generating conflict, pilot types, and the documents that sell them (剧集引擎/franchise/故事模式/人物网/试播集类型/剧集圣经/跳板/提案). Merged from Rabkin's four-element franchise and story landmines, Landau's tacit contract and pitch recipe, Oberg's series types and bible specs, Douglas's franchise-plus-springboard and bible tiers, Goldberg & Rabkin's 100-episode test, Blum's concept/presentation/bible ladder, Miller's ten-component treatment, Calvisi's Compelling Crisis, Smith's series format, plus engines read off Sopranos, Fleabag and Succession. Use when turning an idea into a series, testing whether a premise has legs, choosing premise vs typical-episode pilot, designing a sustainable character web, writing a logline, pitch document, bible or treatment, picking a series type, adapting IP into a series, or diagnosing a project that feels "more like a movie than a series".
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。
Land the winning experiment from an evo run as a clean, mergeable change -- open a PR when the repo has a remote, otherwise merge into the working branch. Distills the best-scoring experiment down to the minimal diff that reproduces its behaviour, shaped for the qualities a maintainer merges on (scope discipline, test integrity, style adherence), then attaches an advisory mergeability report. Use when the user invokes /evo:ship, asks to land/merge/ship the best result, or wants to turn a finished optimization into a pull request.
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 a landing page or funnel step and produce a prioritised CRO test plan. Use when asked to improve conversion rate, audit a landing/signup/checkout page, reduce funnel drop-off, or plan A/B tests for a page. Produces a CRO plan — a heuristic conversion audit, the diagnosed friction, prioritised test hypotheses (ICE), test designs with sample-size math, and the measurement guardrails.
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。
Monitor competitor signals and surface strategic implications for your roadmap. Use when asked to monitor competitors, track the competitive landscape, produce a competitive briefing, or understand what has changed in the market this week or month. Produces a structured intelligence brief with high/medium/low priority signals, roadmap implications, and a strategic landscape summary. For a single competitor announcement use competitor-signal-tracker; for a one-off deep dive use competitor-teardown.
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。
Analyze competitors and create competitive landscape documentation with feature matrices, positioning maps, and strategic recommendations. Use when asked to analyze competitors, create competitive analysis, compare features with competitors, build a competitive landscape, track competitive positioning, or prepare sales battlecard inputs. Produces structured competitor profiles, feature comparison matrix, win/loss analysis, and prioritised strategic recommendations. For a one-off teardown of a single rival use competitor-teardown; for a recurring market briefing use competitive-intelligence-monitor.
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。
Explicit landing-page build and deploy operator. Loads marketing and frontend judgment, scaffolds the smallest suitable site, verifies it rendered, deploys to the configured target.
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 targeting Agricultural and Forest Meteorology or deciding whether a land–atmosphere manuscript fits this venue. Encodes the journal's fit, the land–atmosphere-process and flux-measurement bar, micrometeorological evidence expectations, Elsevier house style, official-submission re-check, and desk-reject heuristics.
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 competitor ads from Meta, TikTok, Google, and LinkedIn ad libraries, analyze creative patterns (hooks, formats, CTAs), reverse-engineer landing page funnels, and produce a strategic teardown with vulnerability analysis and counter-play recommendations. Use when you need to understand the competitive ad landscape, find new creative directions, or identify weaknesses in a competitor's paid strategy.
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。
Extract competitor and customer intelligence from any company's landing page HTML. Discovers tech stack, analytics tools, ad pixels, customer logos, SEO metadata, CTAs, hidden elements, and more. No API keys required.
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 narrated-UGC "stitch reply" ad from a config — a single spoken VO carries a verbatim testimonial while ~30 per-cut i2v clips (one creator across ~5 wardrobes in ~3 worlds, plus product B-roll) are each trimmed to their EDL window built from the VO's Whisper word boundaries and hard-concatenated via filter_complex concat (never the demuxer, which drops audio on a duration mismatch), the VO mixed over an optional sidechain-ducked instrumental bed (−20dB, 20 to 1) so the VO stays on top, karaoke-pop captions burned on every word throughout (VEED Whisper preset, re-spelled against the locked script), a landing-page scroll rendered as FFmpeg zoompan over a Playwright PNG (not i2v), and closed on the brand's real end-card PNG — never AI-rendered text. This is the FREE deterministic assembly stage (trim-to-EDL + filter_complex concat + VO and music mix + karaoke captions + landing-page zoompan + end-card append), run with the shared stitch-videos-ffmpeg montage.py helper that installs with this package; the VO, creator, start-frames, and clips come from create-vo-elevenlabs / create-image-gpt-image-fal / create-image-fal / create-video-fal. Use for the narrated-ugc-wardrobe-stitch 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。
Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.
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。
High-converting landing pages — campaign pages, collection pages, seasonal promos, A/B testing
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。
Build the actual product — from landing page to deployed MVP with payment integration, QA testing, and post-deploy canary monitoring. Handles code generation, deployment, database setup, authentication, Stripe/payment integration, systematic QA protocol, and production health scoring. Use when the user needs to build something, deploy a product, set up payments, create a landing page, or says 'build this', 'deploy', 'create MVP', 'set up payments', or 'ship 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。
New Zealand business English writing style for professional communications — warm, inclusive, EN-NZ spelling (colour, organise, centre). Use whenever the user is writing for a New Zealand audience: emails, chat messages, proposals, client communications, blog posts, web copy, or any business writing. Apply to drafting, editing, and tone-checking professional text.
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。
When the user wants to plan paid ads strategy, allocate ad budget, or choose paid channels. Also use when the user mentions "paid ads," "paid media," "PPC," "SEM," "web ads," "app ads," "TV ads," "CTV," "OOH," "banner ads," "ad network," "ad alliance," "Taaft ads," "Shopify App Store ads," "Google Ads," "Meta Ads," "PMF testing," "PMF validation," "test product-market fit with ads," "ad spend," "ad budget," "ROAS," "paid acquisition," "Quality Score," or "ad-to-page alignment." For Google Ads execution, use google-ads. For Meta Ads execution, use meta-ads. For landing page alignment, use landing-page-generator.
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。
When the user wants to set up, optimize, or manage Meta (Facebook/Instagram) Ads. Also use when the user mentions "Meta Ads," "Facebook Ads," "Instagram Ads," "Meta Pixel," "Conversions API," "Advantage+," "lookalike audience," or "Meta retargeting." For landing pages, use landing-page-generator.
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。
When the user wants to write or optimize short-form marketing copy—headlines, CTAs, ad copy, landing page copy, email copy. Also use when the user mentions "copywriting," "headline," "CTA copy," "ad copy," "landing page copy," "sales copy," "conversion copy," "PAS," "AIDA," "BAB," "copy formula," or "differentiation." For long-form article bodies (blog posts, guides), use article-content.
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。
When the user wants to plan, implement, or optimize referral program strategy. Also use when the user mentions "referral program," "referral marketing," "user referral," "refer-a-friend," "word-of-mouth growth," "referral rewards," "referral tracking," "referral code," "referral incentives," or "viral loop." For referral landing copy, use landing-page-generator.
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。
When the user wants to create SEO pages at scale using templates and data—including AI-assisted, grounded copy for per-URL differentiation (vs rigid mail-merge templates). Also use when the user mentions "programmatic SEO," "programmatic SEO pages," "template pages," "scale content," "location pages," "city pages," "comparison pages at scale," "X vs Y pages," "integration pages," "pages from data," "automated landing pages," or "programmatic landing pages." Uses a playbook matrix aligned to skills under skills/pages. For user-facing template galleries or marketplaces (browse → use), use template-page-generator.
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。
Analyze current trends and challenges in Japanese NLP for a topic. Surveys the existing awesome-japanese-nlp-resources dataset and augments it with up-to-the-minute web research to produce a combined trend + issue report. Use only when the user explicitly wants a trend/landscape report, a challenges/limitations report, or a general research overview of a Japanese NLP topic (this combines the bundled dataset with live web research). Trigger phrases include '日本語LLMの最新トレンド', '〜の動向をまとめて', '最近の日本語NLPの流れ', '日本語LLMの課題', '〜の問題点・限界', '未解決の論点', 'trend report on Japanese embeddings', 'latest Japanese speech models', 'challenges in Japanese NER', 'limitations of Japanese embeddings'. For a simple lookup use the search skill; this one runs web research.