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概要と使いどころ
Evaluate research rigor. Assess methodology, experimental design, statistical validity, biases, confounding, evidence quality (GRADE, Cochrane ROB), for critical analysis of scientific claims.
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Use when choosing or evaluating a startup revenue model, pricing/value metric, packaging/tier design, or calculating unit economics (LTV, CAC, payback, gross margin, NRR), including usage-based/credit/AI pricing and variable compute/COGS constraints.
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Deep-dive analysis of GitHub projects. Use when the user mentions a GitHub repo/project name and wants to understand it — triggered by phrases like "help me look at this project", "learn about XXX", "what is this project like", "analyze this repo", or any request to explore/evaluate a GitHub project. Covers architecture, community health, competitive landscape, and cross-platform knowledge sources.
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Use when evaluating business model viability, analyzing profitability per customer/product/transaction, validating startup metrics (CAC, LTV, payback period), making pricing decisions, assessing scalability, comparing business models, or when user mentions unit economics, CAC/LTV ratio, contribution margin, customer profitability, break-even analysis, or needs to determine if a business can be profitable at scale.
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。
Analytical thinking patterns for comprehensive evaluation, code audits, security analysis, and performance reviews. Provides structured templates for thorough investigation with extended thinking 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。
Comprehensive UX audit using IxDF's 7 factors, 5 usability characteristics, and 5 interaction dimensions. Holistic evaluation with redesign proposals based on user-centered design principles.
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Build AI applications using Azure AI Projects SDK for JavaScript (@azure/ai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.
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Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations. Triggers: "AIProjectClient java", "azure ai projects java", "Foundry project java", "ConnectionsClient", "DatasetsClient", "IndexesClient".
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 AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This is the high-level Foundry SDK - for low-level agent operations, use azure-ai-agents-python skill.
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。
Azure Resource Manager SDK for Arize AI Observability and Evaluation (.NET). Use when managing Arize AI organizations on Azure via Azure Marketplace, creating/updating/deleting Arize resources, or integrating Arize ML observability into .NET applications. Triggers: "Arize AI", "ML observability", "ArizeAIObservabilityEval", "Arize organization".
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。
Review and create distinctive, production-grade frontend interfaces with high design quality and design system compliance. Evaluates using three pillars: frictionless insight-to-action, quality craft, and trustworthy building. USE FOR: PR reviews, design reviews, accessibility audits, design system compliance checks, creative frontend design, UI code review, component reviews, responsive design checks, theme testing, and creating memorable UI. DO NOT USE FOR: Backend API reviews, database schema reviews, infrastructure or DevOps work, pure business logic without UI, or non-frontend code.
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。
VLM evaluation suite for video anomaly detection in smart home camera footage
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。
LLM & VLM evaluation suite for home security AI applications
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Build, debug, or review Cloudflare Workers invoked by the Forward action in Gateway HTTP policies, including the GatewayMiddleware entrypoint, Gateway types, context, and continuation.
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。
Quality control and assessment for proteomics data. Use when evaluating proteomics data quality before downstream analysis. Covers sample metrics, missing value patterns, replicate correlation, batch effects, and intensity distributions.
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Predict CRISPR off-target sites using Cas-OFFinder and CFD scoring algorithms. Identify potential unintended cleavage sites genome-wide and assess guide specificity. Use when evaluating guide RNA specificity or selecting guides with minimal off-target risk.
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Analyze Stockbee-style Day 1 Episodic Pivot candidates from earnings, guidance raises, M&A, FDA/regulatory approvals, analyst actions, major contracts, product launches, short-squeeze catalysts, or theme/story events. Scores catalyst quality together with gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low. Use when the user asks for EP candidates, episodic pivots, Day 1 catalyst trades, game-changing news reactions, delayed EP watchlists, or handoffs into PEAD monitoring.
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Autonomously screen NYSE, Nasdaq, and NYSE American operating-company stocks for undervalued-growth/GARP opportunities using forward same-basis valuation, driver-derived EPS/FCF forecasts, primary-source financial verification, SBC and dilution controls, sector and cycle normalization, auditable candidate-pool coverage, and fail-closed final reporting. Use when asked to find, screen, rank, or refresh US undervalued-growth stocks, including minimal requests with no ticker list or parameters.
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Evaluate account-level drawdown circuit breaker rules from trader-memory-core state and decide whether new trade risk is allowed today. Uses realized P&L, losing-streak cooldowns, and weekly/monthly drawdown limits without any external API.
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Screen US equities for parabolic exhaustion patterns and generate conditional pre-market short plans, then evaluate intraday trigger fires from live 5-min bars. Phase 1 daily 5-factor scorer (MA extension / acceleration / volume climax / range expansion / liquidity), Phase 2 per-candidate plans for ORL break / first-red 5-min / VWAP fail with explicit borrow / SSR / manual-confirmation gating, Phase 3 one-shot intraday FSM that detects trigger fires and resolves concrete share counts. Covers Phase 1 + Phase 2 + Phase 3.
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Comprehensive portfolio analysis using Alpaca MCP Server integration to fetch holdings and positions, then analyze asset allocation, risk metrics, individual stock positions, diversification, and generate rebalancing recommendations. Use when user requests portfolio review, position analysis, risk assessment, performance evaluation, or rebalancing suggestions for their brokerage account.
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。
Evaluate a local pre-trade checklist before manual order entry, blocking planless, oversized, revenge-risk, market-regime-blocked, or circuit-breaker-blocked entries while journaling the decision for trader-memory-core review.
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Separate a strategy return series into declared baseline exposure and residual edge with returns-based OLS attribution, HAC inference, rolling stability, alternate-baseline sensitivity, and regime breakdowns. Use when evaluating whether backtest, out-of-sample, or live returns contain independent alpha beyond market, equal-weight, momentum, sector, or user-supplied factor returns; when explaining whether a drawdown came from baseline exposure or strategy-specific behavior; or when a strategy needs an attribution quality gate after backtesting. Do not use for holdings-based Brinson attribution, feature-level Shapley explanations, or analysis from summary metrics without a dated return series.
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Use when the user asks to "plan the participation ramp before we promote", "how much account history or karma do we need in this community", or "design entry incentives and member lifecycle for our own Discord"; produces the per-community pre-promotion warming plan — account-history/tenure expectations (Estimated, named sources), a give-before-ask ledger spec, a per-community etiquette + rule digest with last-verified dates, and the warming → active graduation criteria that channel-registry requires as state-transition evidence — plus the owned-community variant (entry paths + member lifecycle for your own Discord/Slack/forum/企业微信私域). Not for launch-day submissions or T-0 threads — use community-launch-runner. 社区预热/先给后取/账号养成/毕业标准
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。