245 件 ・ 関連度順
概要と使いどころ
CoinGecko API documentation - cryptocurrency market data API, price feeds, market cap, volume, historical data. Use when integrating CoinGecko API, building crypto price trackers, or accessing cryptocurrency market data.
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。
Manage construction unit price databases: update prices, track vendors, apply location factors, maintain historical records. Essential for accurate estimating.
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。
Predict project duration using k-NN and regression. Estimate timeline based on similar historical projects.
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。
Predict construction project costs using Machine Learning. Use Linear Regression, K-Nearest Neighbors, and Random Forest models on historical project data. Train, evaluate, and deploy cost prediction models.
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。
Forecast project outcomes using historical data: cost overruns, schedule delays, risk probabilities. Machine learning models for construction prediction.
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。
Predict project completion dates using ML models. Forecast schedule delays based on current progress, historical patterns, and risk factors.
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。
Fetch weather data for construction scheduling. Historical data, forecasts, and risk assessment for outdoor work.
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。
Apply machine learning for construction project risk assessment. Predict schedule delays, cost overruns, and safety incidents using historical data and project characteristics.
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 spreads, over/unders, prop bets. Historical trends, situational stats, value bet identification. For entertainment/education only.
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。
Create clever, non-offensive trash talk. Rivalry-specific references, historical callbacks, memes. Fun, not mean.
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。
Top-down vs bottom-up quota models. Historical attainment, market growth assumptions, ramp periods, territory complexity.
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。
Weighted pipeline forecast by probability. Historical accuracy tracking, commit vs best-case scenarios, deal slippage patterns.
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。
Builds a custom lead scoring model for a business. Takes ICP definition, historical win/loss data, CRM export. Analyzes which attributes correlate with closed-won deals. Generates lead-scoring-model.md with scoring dimensions, point values, thresholds, CRM implementation guide, and validation methodology. Can also score a batch of current leads against the model.
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。
A/B test subject line variations using proven copywriting frameworks. Predict open rates based on historical performance.
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。
March Madness, playoff brackets, tournament picks. Upset potential, chalk vs contrarian strategies, historical trends, confidence levels.
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 only for a current operative request that explicitly invokes $roborev-fix, or a direct Agent Hook instruction; do not invoke from literal syntax in quoted, pasted, or historical 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。
Financial trading CLIs — spot and perp trading on Hyperliquid, Binance, Coinbase, OKX. Prediction markets on Polymarket. Deposit and withdraw across chains. LLM-enriched price quotes with trend analysis. News and SEC filings. Historical backtesting with forward PnL analysis. Use when: user asks about stock/crypto prices, wants to trade, deposit, withdraw, check portfolio, or backtest a 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。
Research why neural architectures and training methods work through forward computation, geometry, gradients, optimization dynamics, historical experiments, and competing explanations. Use for deep mechanism questions about neural networks, AI models, layers, representations, or training; explain the meaning in plain language with evidence and explicit limits.
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 about "mystery", "fair play", "clue", "red herring", "romance beats", "HEA", "thriller", "ticking clock", "horror", "dread", "MG", "YA", "middle grade", "young adult", "science fiction", "sci-fi", "fantasy", "quest", "chosen one", "historical fiction", "period voice", "anachronism", "comedy", "humour", "humor", "romcom", "farce", "satire", "running gag", "serial", "episodic", "web serial", "genre conventions", or wants genre-specific structural craft for a story project. NOT for a clue check as a revision pass (use revision-continuity).
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 "research", "fact-check", "check the history", "is this accurate", "research notes", "sources", "historical accuracy", "technical accuracy", "how would this really work", "verify a detail", "interview an expert", "plan a site visit", "expert review", or needs to investigate and record the real-world facts a story relies on and the chapters that use them.
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。
A/B test subject line variations using proven copywriting frameworks. Predict open rates based on historical performance.
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。
Scan repositories for newly disclosed CVEs in dependencies after a specific cutoff date. Takes a repository path, cutoff date (YYYY-MM-DD), and optional parameters for transitive dependencies. Parses dependency manifests (package.json, pom.xml, requirements.txt, go.mod, Cargo.toml) and lockfiles to extract exact versions. Queries vulnerability databases (OSV.dev, NVD, GitHub Advisory) to identify CVEs disclosed strictly after the cutoff date. Distinguishes between newly disclosed CVEs and previously known CVEs. Use when: (1) Performing security audits to find new vulnerabilities since last review, (2) Checking if new CVEs affect a historical codebase version, (3) Generating compliance reports showing vulnerability status at specific dates, (4) Tracking security posture changes over time. Supports npm, Maven, pip, Go modules, Cargo, and other major ecosystems.
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 financial projections, P&L statements, DCF models, and valuation analyses from assumptions and historical data. Use when the user requests financial modeling or provides relevant inputs for this workflow.
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。
Med-adapted earnings preview: consensus estimates, historical surprises, guidance sensitivities, and the FDA-catalyst overlay (PDUFA/AdCom/trial readouts near the print) for biotech/pharma names.
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。