Results for “dsc”

Turn Chinese classical poems and ci into coherent vertical Chinese-art videos with poem-driven scene grouping, GPT ImageGen stills, Docker-only Gemini I2V, retained model-generated ambience, Gemini sparkle-watermark cleanup, brush-calligraphy captions revealed character by character, optional local BGM mixing, stitching, and final-frame QA. Supports verse-driven variation across ink landscape, gongbi bird-and-flower, colored figure-and-horse painting, blue-green landscape, xuan paper, silk, and related Chinese visual languages. Use when users ask for 古诗词动态视频、诗词逐句或两句一景、国风视频、毛笔字逐字出现、整首诗拼接成片，or want the established 月落乌啼霜满天 workflow applied to another poem.

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Use when a skill fails for a missing tool or the environment needs checking. Diagnoses Python, R, Node, Claude Code, Git, Zotero and configured MCP servers and prints a pass/fail table with links to the setup docs. Read-only; installs nothing.

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Use when the user describes a research goal without naming a skill, or the task spans several skills. Classifies the request, plans the order and routes to the right medsci-skills skill(s) instead of producing their output itself.

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Framework for competitive landscape analysis across any industry. Use when creating competitor analysis, market positioning assessments, investment memos, strategic reviews, or any analysis requiring systematic evaluation of competitive dynamics. Triggers include requests for competitive landscape decks, peer comparisons, market structure analysis, strategic positioning assessments, and investment recommendations.

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Map the big picture around a topic or software repository by identifying its boundaries, layers, actors, components, relationships, flows, history, fault lines, and open questions, then explain how the pieces fit together and where to look next. Use when a user asks to understand a whole field, domain, technology, ecosystem, industry, codebase, architecture, unfamiliar repo, or phrases such as "give me the big picture", "map the landscape", "how does this all fit together?", "help me get oriented", or "what am I missing?"

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Map any industry's competitive landscape using company databases, academic research, news coverage, LinkedIn activity, and job market signals. Produces industry overviews with key players, market segments, growth indicators, and emerging disruptors.

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Integrating local LLMs into Godot games using NobodyWho and other Godot-native solutionsUse when "godot llm, nobodywho, godot ai npc, gdscript llm, godot local llm, godot chatgpt, godot 4 ai, godot, llm, nobodywho, gdscript, game-ai, npc, local-llm" mentioned.

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Use when writing Godot 4 GDScript - static typing everywhere, scene composition with component nodes, call down/signal up, @onready caching, Resources as data duplicated per instance, autoloads only for services, state machines, pooling, physics in _physics_process, and threaded loading.

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Comprehensive competitor analysis, competitive intelligence, and comparison page creation. Use when analyzing competitive landscape, creating competitor comparison pages (alternative pages, vs pages, competitor A vs B pages), performing market research, building SWOT analysis, creating feature matrices, pricing comparisons, competitor teardowns, battle cards, or positioning maps. Covers research workflows, SEO-optimized comparison content, data-grounded reports with citations, 7-layer analysis framework, review mining, and sales enablement. Triggers include competitor analysis, competitive analysis, competitor teardown, vs page, alternative page, competitor comparison, market research, competitive intelligence, SWOT analysis, battle card, competitor pricing, market landscape, feature comparison, positioning map.

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Comprehensive market research and analysis skill. Use when sizing markets, conducting competitive analysis, generating professional market research reports, analyzing consumer behavior, or identifying market opportunities. Activates for: market research, market analysis, market report, TAM SAM SOM, market sizing, industry analysis, market landscape, competitive landscape, market trends, consumer research, Porter's Five Forces, PESTLE, SWOT, BCG Matrix, McKinsey-style report, consulting report, market opportunity, industry report, due diligence, M&A analysis, GTM market analysis, product-market fit validation.

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Use this skill when building Solana-enabled games or apps in Godot 4 with the Godot Solana SDK. Triggers include: Solana transactions in GDScript, SPL tokens or NFTs in a Godot project, connecting browser or mobile wallets (Phantom, Solflare, Backpack) in a Godot game, calling Anchor smart contracts from GDScript, working with Candy Machine in Godot, or any mention of SolanaClient, Transaction, Pubkey, Keypair, WalletAdapter, or AnchorProgram in a Godot context. Do NOT use for non-Godot Solana SDKs (solana-py, solana-go, solana-web3.js).

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Maps a startup's competitive landscape, scores moat strength across 6+ dimensions, and writes an investor-ready competition narrative with a positioning map. Use when the user asks for a competitive landscape, moat analysis, or positioning review and gives enough to work from: a deck, a list of competitors, or a clearly named startup. A full run takes about 30–40 minutes and searches the web, so for a passing question such as 'who are my competitors?' or 'is that a real moat?', answer briefly in chat and offer the analysis instead of starting it. When it runs, the competitor set and moat scores are checked adversarially, never recalled from memory.

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Maps competitive dynamics with market positioning, feature comparison, funding histories, and differentiation assessment. Use when analyzing startup competition, mapping market landscapes, or identifying competitive threats.

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PowerShell 7+ scripting and Windows system administration -- cross-platform automation, secure credential handling, Pester testing, DSC, and JEA patterns.

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5つの専門エージェントがディスカッション型で競合のビジネスモデル・機能・価格・弱点を網羅的に分析し、 差別化ポイントと参入戦略を導出するスキル。 エージェント同士が対話・批判し合いながら調査し、ca-data-critic がライブモデレーターとして矛盾を検出・収束を判定する。 Phase 1 で競合プロファイル・レビュー・機能をディスカッション型で調査し、 Phase 2 でポジショニング戦略家が統合。 Gate 1（中間合意形成）→ Gate 2（最終検証 + Independent Validation + Devil's Advocate）の2段階ゲートで品質を担保。 Use when: 競合を分析したい、差別化ポイントを見つけたい、 市場のプレイヤーを調べたい、ポジショニングを考えたい。 Triggers: "競合分析", "competitive analysis", "競合調査", "差別化", "ポジショニング", "competitor", "競合", "市場プレイヤー", "ライバル調査", "competitive landscape"

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Turn uploaded travel photos into full-canvas 3:4 pixel-dissolve landscapes with abundant paper white. Use for curated limited-palette pixel studies or when this skill is invoked.

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Turn uploaded photos into full-canvas 3:4 impasto miniature-landscape posters. Use for sculptural oil-paint dioramas, tactile impasto scenes, or when this skill is invoked.

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Map who is winning an SEO market with measured data — recurring SERP domains, organic footprints, winning content themes, backlink authority, and the gaps worth attacking. Use this skill for market-level questions ("who ranks for X", "how competitive is this space", "where are the SEO openings") before committing to keyword targets or content themes. For one named competitor, use openseo-competitor-analysis instead. Triggers: "competitive landscape seo", "who ranks for", "seo market map", "how competitive is", "serp competitors".

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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.

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Estimates bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).

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使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括：AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**：本 skill 走批量 SQL · 离线大数据（PostgreSQL）；v2 走实时 API · 单查询。两者互补：单条 NCT 实时状态用 v2，百万级历史挖掘用本 skill。支持云端公共 PostgreSQL（aact-db.ctti-clinicaltrials.org · 零部署）和每日 dump 本地还原（高性能 · 离线）两种连接方式，自动检测优先用本地。跨平台（macOS/Linux/Windows）参数化 SQL 防注入，read-only 强制保护。

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Multi-angle deep-research workflow over Ahmad's OpenMark knowledge graph (13k+ bookmarks). Use whenever Ahmad asks anything that requires more than one search to answer well — "what's the landscape of X in my saves", "do a deep dive on Y", "research X across my bookmarks", "compare what I saved about X vs Y", "give me everything on X". Also use when fast-search returned thin results and Ahmad pushed back ("dig deeper", "more results", "actually research it"). Pairs Neo4j graph search with WebFetch on top picks to bring back fresh page content. Triggers on "deep research", "dig deeper", "research", "landscape", "everything on", "thorough", "comprehensive".

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特許分析・知的財産戦略支援スキル。先行技術調査、特許性評価、特許ポートフォリオ分析、 侵害リスク評価、ライセンス戦略策定を支援。特許出願戦略の立案から権利化まで包括的にサポート。 Use when conducting prior art searches, evaluating patentability, analyzing patent portfolios, assessing infringement risks, or developing IP strategies. Triggers: "patent search", "prior art", "特許調査", "patentability", "FTO", "特許ポートフォリオ", "IP strategy", "patent landscape".

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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",...

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