「dom」の検索結果

Configure this repo for the engineering skills — set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering 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。

Live SEO data via DataForSEO MCP server. SERP analysis (Google, Bing, Yahoo, YouTube, Google Images), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", "image SERP", "google images", "image rankings", or "real search data".

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Ahrefs API analyst (extension). Reads referring domains, backlinks, organic keywords, and content explorer data via the official @ahrefs/mcp server. Pairs with seo-backlinks for multi-source confidence weighting.

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。

Conduct expert heuristic evaluations using Nielsen's heuristics and domain-specific criteria.

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Join and operate on tiny.place (the agent-to-agent social network): create a self-custodied wallet, fund it via MoonPay, buy/renew/transfer a @handle 'domain', publish a discovery card, discover and resolve other agents, manage your profile and social graph (follow/feed/reputation), send Signal end-to-end encrypted messages, earn and spend through the jobs/escrow economy and marketplace, audit the settlement ledger, and poll the platform for updates — all through the `tinyplace-agent` CLI.

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Use when an agent needs to join tiny.place, create or unlock a self-custodied Solana wallet, on-ramp or off-ramp USDC with MoonPay, buy a tiny.place @handle domain, publish an A2A directory card, poll inbox/messages/activity, or register a Hermes cron loop for platform updates.

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Builds end-to-end predictive modeling pipelines on tabular datasets—data exploration, preprocessing, baseline modeling, and mandatory optimization—producing valid saved predictions in the required output format. Use for any supervised machine learning or data modeling task, including classification and regression on tabular data, Kaggle-style competitions or submission-file challenges, training a model to predict a target from features, or requests to fit/tune/evaluate models with scikit-learn, XGBoost, Random Forest, Logistic Regression, and similar non-deep-learning methods. Runs fully automated end-to-end without asking the user for intermediate decisions.

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Computes a numeric or categorical answer to a quantitative data-science question by cleaning and analyzing local data files (CSV, Excel, TSV, and scientific formats .npz/.fits/.h5) with pandas, numpy, and scipy. Use whenever a task ships its own dataset (in whatever local directory it provides) and asks you to derive a value via statistical, geospatial, temporal, ratio, percentage, growth-rate, correlation, signal-processing, orbital, or forecasting analysis. Spans domains including archeology (demographics, paleoclimate proxies, radiocarbon dating, historical conflicts); biomedical/bioinformatics (tumor histology, genes, proteins, peptides, genomic variants); environmental (water quality, bacterial/Enterococcus exceedance, rainfall correlations, environmental justice); legal/consumer (fraud, identity theft, consumer complaints by state/metro); wildfire (incidents, causes, suppression costs, acres burned, fatalities, geospatial intersections); and astronomy/heliophysics/space weather (satellite telemetry TLE/Swarm/SP3, indices OMNI/GOES, solar activity, atmospheric density, orbital mechanics, forecasting).

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Use when 王建硕 gives a list of movies and wants them turned into Relisten-ready files — find a LEGAL source for each (public-domain / openly licensed copies on Internet Archive, or a copy he already owns on this Mac), get English subtitles (and Chinese if possible) via OpenSubtitles or Whisper+translation fallback, compress to a small high-quality HEVC mp4 with labeled embedded subtitle tracks, and file them under ~/Movies/Relisten/. Triggers — "找电影", "下载这几部电影", "给 relisten 准备片子", "电影列表", "/wjs-fetching-movie".

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Check product and brand names for conflicts across trademarks, domains, social handles, and package registries. Returns a risk level and Proceed/Modify/Abandon verdict. Don't use for name brainstorming, logo design, or trademark filings.

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Domain-Driven Design architecture for claude-flow v3. Implements modular, bounded context architecture with clean separation of concerns and microkernel pattern.

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15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.

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Core module implementation for claude-flow v3. Implements DDD domains, clean architecture patterns, dependency injection, and modular TypeScript codebase with comprehensive testing.

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This skill applies the analytical frameworks of Ronald C. Kessler, psychiatric epidemiologist at Harvard University, to problems in precision psychiatry, clinical trial design, and healthcare machine learning. Use this skill whenever you are designing clinical trials, evaluating mental health interventions, analyzing epidemiological survey data, or building clinical decision support algorithms. It is highly relevant when the user asks about sample sizes, treatment matching, patient attrition, suicide prevention, or predictive modeling in healthcare. Trigger this skill to prioritize massive observational data, tiered predictive models, and human-in-the-loop AI over underpowered randomized trials and expensive, unscalable biomarkers.

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Use this skill whenever you are reasoning about nutritional epidemiology, dietary guidelines, chronic disease prevention, or public health policy. It channels the thinking of Meir J. Stampfer, epidemiologist and nutrition expert at Harvard University. Trigger this skill when evaluating lifestyle factors, comparing observational data versus randomized controlled trials (RCTs), analyzing macronutrient substitutions, or assessing long-term health outcomes. Stampfer's approach is essential when users ask about "good" vs. "bad" foods, seek longevity advice, or need to interpret complex, population-level dietary data without falling for the "magic bullet" fallacy.

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Use this skill whenever reasoning about immunology, host-pathogen interactions, innate immunity, Toll-like receptors (TLRs), or experimental design in molecular biology. It is also highly relevant for scientific career strategy, particularly when discussing independence, pivoting research domains, or using reverse genetics (like knockout mice) for discovery. Channel Shizuo Akira, immunologist and pioneer of pattern-recognition receptors, to apply rigorous experimental standards (e.g., demanding chemically synthesized mimics over purified extracts) and to view innate immunity as a highly specific, compartmentalized system rather than a primitive defense.

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Applies the reasoning, principles, and mental models of Oprah Winfrey (media mogul, founder of Harpo Productions and OWN network). Use this skill whenever the user is facing career trade-offs, seeking life purpose, struggling with failure or adversity, navigating difficult human connections, or making high-stakes decisions where intuition and intention matter. Trigger this for queries about finding one's calling, overcoming self-doubt, managing personal responsibility, or seeking authentic leadership advice. Channel her focus on intention, inner voice, and turning wounds into wisdom.

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Use this skill to reason like JoAnn E. Manson, epidemiologist at Harvard Medical School and women's health expert. Trigger this skill whenever you are evaluating hormone replacement therapy (HRT), menopause management, nutritional epidemiology, dietary supplements, or clinical trial methodology. Apply her frameworks when users ask about the risks and benefits of HRT, bioidentical hormones, or targeted supplementation. Reach for this skill to differentiate between symptom management and chronic disease prevention, or when assessing observational data versus randomized clinical trials (RCTs) in public health contexts.

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Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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Use this skill to apply the rigorous epidemiological reasoning of Julie E. Buring (epidemiologist and women's health trialist) to study design, clinical trial critiques, cardiovascular risk assessment, and evidence-based integrative medicine. Trigger this skill whenever you are evaluating medical literature, designing cohort studies or RCTs, assessing conflicting research findings, or analyzing cardiovascular risk factors (especially in women). Reach for this when users ask about the validity of supplements, the discrepancy between observational and randomized data, or how to future-proof a clinical registry.

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Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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This skill channels the reasoning of Hannah Arendt, 20th-century political theorist and author of 'The Origins of Totalitarianism'. Use this skill whenever you are evaluating moral responsibility, analyzing systemic evil, distinguishing between power and violence, or assessing the nature of political action and freedom. Trigger it when discussing bureaucracy, thoughtlessness, the public vs. private realm, truth in politics, or when a user faces unprecedented moral dilemmas requiring 'thinking without a banister'. Apply her frameworks to critique historical determinism, defend factual truth, and analyze human plurality, even if the user does not explicitly name her.

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Applies the reasoning style of Kaiming He, computer vision pioneer and creator of ResNet. Use this skill whenever you are designing deep learning architectures, debugging neural network optimization, formulating generative AI problems, or bridging AI with other scientific domains. Trigger this skill for discussions on network depth, weight initialization, residual learning, flow matching, or when reframing discriminative tasks as conditional generation. It emphasizes simplicity in complex visual problems, end-to-end optimization, and viewing AI as a universal language for science.

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Suede AI blunt A-F ship grade for a code change across correctness, security and permissions, data and state, domain truth, UX and release behavior, tests and verification, and deploy readiness, with Instant-F triggers and evidence-based grade caps on auth, payment, migration, and public-API surfaces. Use when asked to grade this, give it a letter, is this an A, how ready is this to ship, or should this merge: when the caller wants the verdict without a findings list. NOT FOR: findings, evidence, and fix briefs (use suede-code-review, or suede-code for findings plus grade); enforcing the verdict in CI (use suede-ci-gate); eval coverage for AI behavior (use suede-ai-eval).

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。