「landscape」の検索結果

This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.

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Expert startup business analyst specializing in market sizing, financial modeling, competitive analysis, and strategic planning for early-stage companies. Use PROACTIVELY when the user asks about market opportunity, TAM/SAM/SOM, financial projections, unit economics, competitive landscape, team planning, startup metrics, or business strategy for pre-seed through Series A startups.

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Clones and audits competitor repositories into evidence-based intelligence with file:line citations. Use to track competitors, add a competitor repo, review competitor code, compare capabilities, build a competitor landscape, or check for code updates: 竞品分析 / 竞品 / competitor scan / analyze competitor / compare with X / latest competitor code. Not for market research without code (use deep-research).

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Produce a structured competitive analysis for any product or market. Use when asked for a competitor analysis, competitive teardown, market comparison, SWOT, or positioning map. Generates a structured teardown with positioning map, feature comparison, messaging gaps, and strategic recommendations. For a full landscape doc with feature matrix and win/loss analysis use competitive-analysis instead.

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Run a fast, honest competitive scan — the dimension table built from public evidence (sites, docs, pricing pages, changelogs, reviews), the claims-vs-observed discipline, and the so-what synthesis that ends in moves, not a landscape mural. Use when asked what are competitors doing, quick scan of these three rivals, how does our pricing/feature set compare, or prep the competitive slide honestly. Produces the evidence-based comparison table, the marketing-vs-reality flags, the so-what synthesis, and the staleness date.

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Use when targeting Agriculture, Ecosystems & Environment or deciding whether an agroecosystem-environment manuscript fits this venue. Encodes the journal's fit, the field-to-landscape environmental-outcome bar, data-reporting expectations, house style, official-submission re-check, and desk-reject heuristics.

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Use when deciding whether a project fits Agricultural Systems (AgSy) and which article type to target. AgSy is a systems-science journal, so the test is a genuine systems question — interactions among components, across hierarchical levels (field → farm → landscape → region → food system), trade-offs, and emergent behavior — analyzed with integrated modelling, NOT a single-factor field trial. Helps frame the question; it does not collect data.

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Compare two or more companies, products, or platforms across pricing, features, positioning, and docs. Use this skill whenever the user says "compare X vs Y", "how does X stack up against Y", "alternatives to X", "competitive landscape of …", "X vs Y vs Z", or asks for a competitor matrix. Uses search to discover competitors when the user only names a category, then scrape for each competitor's homepage, pricing page, and features/docs. Returns a normalized comparison matrix as JSON.

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Reverse-engineer how competitors do programmatic SEO. Detects URL pattern clusters (vs/, integrations/, for-{industry}/), estimates page count per pattern, analyzes template quality, infers which patterns actually drive traffic, and identifies gaps you can exploit. Outputs a competitive pSEO landscape report.

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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.

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Discovers, classifies, and masks repetitive elements and transposable elements with RepeatModeler2 (de novo family library), RepeatMasker (masking against a library), EDTA (plant/structural TEs), or EarlGrey (auto-curating wrapper), and quantifies TE expression from RNA-seq with TEtranscripts/SQuIRE. Covers de-novo-library-as-curation-project, soft-vs-hard masking, the domesticated-gene over-masking massacre, Dfam-vs-RepBase, TE classification (Class I/II, family-vs-copy), Kimura repeat landscapes, LAI, and the RNA-seq multimapping problem. Use when masking repeats before gene prediction, building a TE library for a non-model genome, or analyzing transposable-element content or expression.

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Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.

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Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

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Design, validate, and plan a startup from scratch. Covers market research, competitive analysis, business model, brand identity, product definition, financial projections, and validation experiments. Trigger when the user has a startup idea to explore, wants to validate a business concept, needs a business plan or lean canvas, asks for market sizing or competitive landscape, wants brand positioning or go-to-market strategy, or says anything like "I have an idea for..." or "is this idea worth pursuing". Also handles resuming from a previous checkpoint.

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Full-stack e-commerce marketing strategy builder. Analyzes your product, market, and competitors, then builds a complete omnichannel marketing plan covering paid ads, SEO, email/SMS, content marketing, social media, influencer partnerships, and referral programs. Includes target audience persona, competitive landscape, channel prioritization with budget allocation, content direction, and a 90-day action plan. Works for any e-commerce platform — Shopify, Amazon, Etsy, WooCommerce, TikTok Shop, and more. No API key required.

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Discover profitable business ideas from scratch. Analyzes market gaps, trending niches, user skills, and competitive landscapes with a competitive intelligence protocol including 4-filter benchmark stress test and Blue Ocean differentiation grid. Use when the user has no idea what to build, wants to explore opportunities, needs market research, competitive benchmarking, or says 'find me a business idea', 'what should I build', 'market research', 'find opportunities', or 'competitive analysis'.

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Use when a UE5 task involves materials or textures: a master material and instances for a kit, Substrate or legacy, Blendable vs Adaptive GBuffer, glass, emissive screens, decals, edge wear and dirt, landscape layers and RVT, toon or cel shading, "too many shaders" or shader permutations, static switches, usage flags, texture compression (BC7, normal maps, ORM packing, sRGB), texture memory, or "is this material expensive".

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Use when producing a video ad with Scenario from a product shot or brand assets: a mobile 9:16 or landscape 16:9 commercial, a TikTok, Reels, Shorts, YouTube, or CTV spot, or campaign creative needing a creative brief, storyboard, cinematic camera work, brand-faithful product footage, music and voiceover, captions, ad variants, budget planning, or platform delivery specs. Keywords: video ad, commercial, product video, advert, hook, CTA, end card, campaign, ad creative.

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Use when turning a song into a kinetic-typography or motion-design music video, lyric video, visualizer or K-pop style MV with Scenario, landscape or vertical 9:16 for TikTok, Reels and Shorts: a generated subject sheet, style frames and footage, plus a deterministic JavaScript/three.js engine for lyric type, mattes and tracked graphics timed to sung onsets. Also for revising, recutting or cutting teasers from such a video.

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Map stakeholders, decision rights, influence and blockers from supplied evidence. Use when ownership is unclear or the stakeholder landscape changes.

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Run upstream brand discovery covering audience research, competitive landscape, category dynamics, problem space, and positioning territory exploration. Use this skill at the very start of a brand or website project when the user needs to understand who they're for, who they compete with, what the audience actually needs, and where the brand could plausibly stand. Triggers on brand discovery, audience research, market research, competitive scan, category research, customer research, who is this for, who are we, positioning research, intake, kickoff. Also triggers when a creative brief is requested but the upstream inputs (audience, competitors, problem space) are not yet clear.

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YC Office Hours — product idea diagnostic before writing code. Two modes: Startup (6 forcing questions with rigorous pushback) and Builder (generative brainstorming). Outputs design doc. Includes landscape research, premise challenge, alternatives generation, visual wireframe sketching, and optional cross-model second opinion.

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Create and maintain the foundational context doc (positioning, ICP, competitive landscape, brand voice, customer language) that other marketing skills read first. Use when setting up context, defining the ICP, or setting brand voice.

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When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.

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。