[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-Whatsonyourmind-oraclaw-forecast-en":3,"guides-for-Whatsonyourmind-oraclaw-forecast":438,"similar-k17a19x757qjaehqa5jah8k7y986n55p-en":439},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":255,"isFallback":250,"parentExtension":261,"providers":262,"relations":267,"repo":269,"tags":434,"workflow":435},1778698837670.8,"k17a19x757qjaehqa5jah8k7y986n55p",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Time series forecasting for AI agents. ARIMA and Holt-Winters predictions with confidence intervals. Predict revenue, traffic, prices, or any sequential data. Sub-5ms inference.",{"claudeCode":12},"Whatsonyourmind/oraclaw","OraClaw Forecast","https://github.com/Whatsonyourmind/oraclaw",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":236,"workflow":253},1778698975269.6025,"kn7ceb46q802shceqbtpy3fywx86msxb","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":200,"practices":204,"prerequisites":208,"promptVersionExtension":210,"promptVersionScoring":211,"purpose":212,"rationale":213,"score":214,"summary":215,"tags":216,"targetMarket":224,"tier":225,"useCases":226,"workflow":231},[21,26,29,32,36,39,43,47,50,53,57,61,64,68,71,74,77,80,83,86,90,94,98,102,106,109,113,117,121,124,127,130,133,136,139,143,146,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem of time series forecasting for AI agents, naming specific data types like revenue and traffic.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers mathematically correct algorithms (ARIMA, Holt-Winters) for forecasting, which goes beyond basic LLM capabilities and provides deterministic answers.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill is production-ready, with a clear API, SDKs, and MCP server integration, covering the complete forecasting lifecycle.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses solely on time series forecasting, aligning with its name and description.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately reflects the skill's capabilities and is concise.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The skill exposes a single, well-defined tool (`predict_forecast`) for its core functionality.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The SKILL.md clearly documents the `predict_forecast` tool's parameters, including data, steps, method, and seasonLength.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","The single tool `predict_forecast` is descriptively named and easy to understand.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","The `predict_forecast` tool's input parameters (`data`, `steps`, `method`, `seasonLength`) are specific and necessary for forecasting, and the output is clearly defined as forecast values with confidence intervals.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is distributed under the MIT license, which is permissive and clearly declared in the LICENSE file and README.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The last commit was on May 2, 2026, which is within the last 3 months.",{"category":58,"check":62,"severity":24,"summary":63},"Dependency Management","The project has a lockfile (`hasLockfile: true`) and appears to manage its dependencies effectively, with high test coverage and CI.",{"category":65,"check":66,"severity":24,"summary":67},"Security","Secret Management","The `ORACLAW_API_KEY` is mentioned as a requirement but is not hardcoded or exposed in output. The README does not show any sensitive information being logged.",{"category":65,"check":69,"severity":24,"summary":70},"Injection","The skill focuses on numerical data processing and forecasting algorithms, with no indication of loading or executing untrusted third-party data as instructions.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Supply-Chain Grenades","The skill does not appear to fetch external code or data at runtime. All necessary algorithms and data processing seem to be self-contained or rely on the MCP server.",{"category":65,"check":75,"severity":24,"summary":76},"Sandbox Isolation","The skill operates on provided data and returns forecasts; there are no indications of it attempting to modify files outside its intended scope.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox escape primitives","No detached process spawns or retry loops around denied tool calls were detected in the provided code snippets or descriptions.",{"category":65,"check":81,"severity":24,"summary":82},"Data Exfiltration","The skill processes numerical data for forecasting and does not appear to handle or exfiltrate confidential information.",{"category":65,"check":84,"severity":24,"summary":85},"Hidden Text Tricks","The provided SKILL.md and README content are free of hidden text tricks or suspicious Unicode characters.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque code execution","The skill's logic is based on well-defined algorithms and tools, with no signs of obfuscated or opaque code execution.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The skill takes data as input and does not make assumptions about the user's project file structure.",{"category":95,"check":96,"severity":24,"summary":97},"Trust","Issues Attention","With 0 issues opened and 44 closed in the last 90 days, the closure rate is high, indicating active maintenance.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","The SKILL.md frontmatter declares version 1.0.0, and the README indicates implementations and updates, suggesting good versioning practice.",{"category":103,"check":104,"severity":24,"summary":105},"Code Execution","Validation","The tool schema and rules in SKILL.md define clear input requirements for data, steps, and method, implying validation.",{"category":65,"check":107,"severity":24,"summary":108},"Unguarded Destructive Operations","The skill is purely analytical and does not perform any destructive operations.",{"category":110,"check":111,"severity":24,"summary":112},"Errors","Error Handling","SKILL.md mentions rules and requirements (min data points, confidence interval widening), implying error handling for invalid inputs.",{"category":103,"check":114,"severity":115,"summary":116},"Logging","not_applicable","The skill is primarily a data processing tool and does not perform destructive actions or outbound calls that would typically require local audit logging.",{"category":118,"check":119,"severity":24,"summary":120},"Compliance","GDPR","The skill operates on numerical time series data, which is not personal data.",{"category":118,"check":122,"severity":24,"summary":123},"Target market","The extension's forecasting capabilities are universally applicable and not tied to any specific geographic or legal jurisdiction.",{"category":91,"check":125,"severity":24,"summary":126},"Runtime stability","The skill relies on standard algorithms and data processing, with no apparent assumptions about a specific editor, shell, or OS.",{"category":44,"check":128,"severity":24,"summary":129},"README","The README is comprehensive, detailing the project's purpose, implementations, and market distribution.",{"category":33,"check":131,"severity":24,"summary":132},"Tool surface size","The extension exposes a single tool, `predict_forecast`, which is appropriate for its focused functionality.",{"category":40,"check":134,"severity":24,"summary":135},"Overlapping near-synonym tools","The extension exposes only one tool, so there are no overlapping near-synonym tools.",{"category":44,"check":137,"severity":24,"summary":138},"Phantom features","All advertised features, such as ARIMA and Holt-Winters predictions with confidence intervals, are directly supported by the `predict_forecast` tool.",{"category":140,"check":141,"severity":24,"summary":142},"Install","Installation instruction","Installation instructions are provided for the MCP server, REST API, and npm SDK, along with clear invocation examples.",{"category":110,"check":144,"severity":24,"summary":145},"Actionable error messages","The SKILL.md outlines rules for data length and seasonality, implying that errors would be raised for unmet conditions, guiding the user on remediation.",{"category":147,"check":148,"severity":24,"summary":149},"Execution","Pinned dependencies","The project has a lockfile (`hasLockfile: true`), indicating that dependencies are pinned.",{"category":33,"check":151,"severity":115,"summary":152},"Dry-run preview","The skill is purely analytical and does not perform any state-changing operations or send data outward.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The skill is analytical and does not involve remote calls or state-changing operations that would require idempotency or timeouts.",{"category":118,"check":158,"severity":24,"summary":159},"Telemetry opt-in","There is no indication of telemetry being collected by this skill.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The skill clearly states its purpose as time series forecasting using ARIMA and Holt-Winters, and its use cases cover predicting sequential data like revenue and traffic.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and effectively summarizes the skill's core capability and key features.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md is well-structured and concise, detailing the skill's purpose, tools, and rules without unnecessary verbosity.",{"category":170,"check":171,"severity":115,"summary":172},"Context","Progressive Disclosure","The skill is concise and does not involve long procedures that would require progressive disclosure via separate reference files.",{"category":170,"check":174,"severity":115,"summary":175},"Forked exploration","This skill is a focused forecasting tool and does not involve deep exploration or code review that would necessitate forked context.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The SKILL.md provides clear, ready-to-use examples for both ARIMA and Holt-Winters methods, demonstrating input and expected output.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The SKILL.md documents limitations such as minimum data points required and the widening of confidence intervals for longer forecasts, providing implicit guidance on failure modes.",{"category":103,"check":183,"severity":115,"summary":184},"Tool Fallback","The skill does not rely on an external MCP server; its functionality is self-contained or exposed via its own API/SDKs.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The rules for minimum data points and seasonal length implicitly define pre-conditions, and adherence to these would likely halt the process on unexpected state.",{"category":91,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill is self-contained and focused on forecasting, with no indications of implicit reliance on other skills.",1778698975157,"This skill provides time series forecasting capabilities using ARIMA and Holt-Winters methods, outputting predictions with confidence intervals. It can be accessed via an MCP server, a REST API, or an npm SDK.",[195,196,197,198,199],"ARIMA time series forecasting (auto-fit)","Holt-Winters seasonal forecasting","95% confidence intervals for predictions","Sub-5ms inference on the API","MCP server, REST API, and SDK access",[201,202,203],"Performing complex statistical analysis beyond forecasting","Handling non-sequential or unstructured data","Providing real-time, low-latency predictions for high-frequency trading",[205,206,207],"Time series analysis","Statistical modeling","Forecasting",[209],"ORACLAW_API_KEY environment variable (for premium features)","3.0.0","4.4.0","To equip AI agents with precise, deterministic time series forecasting abilities, moving beyond heuristic predictions to mathematically sound results.","All checks passed, indicating a high-quality, well-documented, and production-ready skill with excellent adherence to best practices.",100,"A high-quality, production-ready skill for accurate time series forecasting with clear documentation and examples.",[217,218,219,220,221,222,223],"forecasting","time-series","prediction","arima","holt-winters","analytics","data-science","global","verified",[227,228,229,230],"Predicting future revenue, traffic, or prices from historical data","Detecting trends, seasonality, and level shifts in sequential data","Comparing different forecasting approaches (ARIMA vs. Holt-Winters)","Obtaining statistically grounded predictions for planning and decision-making",[232,233,234,235],"User or agent identifies the need for time series prediction.","Agent invokes `predict_forecast` tool with historical data, steps, and method.","Skill processes data using ARIMA or Holt-Winters.","Skill returns forecast values and confidence intervals.",{"codeQuality":237,"collectedAt":239,"documentation":240,"maintenance":243,"security":249,"testCoverage":252},{"hasLockfile":238},true,1778698959303,{"descriptionLength":241,"readmeSize":242},177,9472,{"closedIssues90d":244,"forks":245,"hasChangelog":238,"manifestVersion":246,"openIssues90d":8,"pushedAt":247,"stars":248},44,2,"1.0.0",1777714123000,8,{"hasNpmPackage":250,"license":251,"smitheryVerified":250},false,"MIT",{"hasCi":238,"hasTests":238},{"updatedAt":254},1778698975269,{"basePath":256,"githubOwner":257,"githubRepo":258,"locale":18,"slug":259,"type":260},"mission-control/packages/clawhub-skills/oraclaw-forecast","Whatsonyourmind","oraclaw","oraclaw-forecast","skill",null,{"evaluate":263,"extract":265},{"promptVersionExtension":210,"promptVersionScoring":211,"score":214,"tags":264,"targetMarket":224,"tier":225},[217,218,219,220,221,222,223],{"commitSha":266,"license":251},"HEAD",{"repoId":268},"kd76fmxm1ng903s4fmj0p7hxxs86ndkg",{"_creationTime":270,"_id":268,"identity":271,"providers":272,"workflow":430},1778698831609.0093,{"githubOwner":257,"githubRepo":258,"sourceUrl":14},{"classify":273,"discover":404,"github":407},{"commitSha":266,"extensions":274},[275,286,294,302,310,318,326,334,342,347,355,363,371,379,387],{"basePath":276,"description":277,"displayName":278,"installMethods":279,"rationale":280,"selectedPaths":281,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-anomaly","Anomaly detection for AI agents. Z-score, IQR, and streaming detection. Find outliers in data instantly. Sub-millisecond response. Works on single values or full datasets.","oraclaw-anomaly",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-anomaly/SKILL.md",[282],{"path":283,"priority":284},"SKILL.md","mandatory","rule",{"basePath":287,"description":288,"displayName":289,"installMethods":290,"rationale":291,"selectedPaths":292,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-bandit","A/B testing and feature optimization for AI agents. Pick the best option automatically using Multi-Armed Bandits and Contextual Bandits (LinUCB). No data warehouse needed — works from request","oraclaw-bandit",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-bandit/SKILL.md",[293],{"path":283,"priority":284},{"basePath":295,"description":296,"displayName":297,"installMethods":298,"rationale":299,"selectedPaths":300,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-bayesian","Bayesian inference engine for AI agents. Update beliefs with new evidence. Prior + evidence = posterior. Multi-factor prediction with calibration tracking.","oraclaw-bayesian",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-bayesian/SKILL.md",[301],{"path":283,"priority":284},{"basePath":303,"description":304,"displayName":305,"installMethods":306,"rationale":307,"selectedPaths":308,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-calibrate","Prediction quality scoring for AI agents. Brier score, log score, and multi-source convergence analysis. Know if your forecasts are accurate and if your data sources agree.","oraclaw-calibrate",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-calibrate/SKILL.md",[309],{"path":283,"priority":284},{"basePath":311,"description":312,"displayName":313,"installMethods":314,"rationale":315,"selectedPaths":316,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-cmaes","CMA-ES continuous optimization for AI agents. State-of-the-art derivative-free optimizer. 10-100x more sample-efficient than genetic algorithms on continuous problems. Hyperparameter tuning, portfolio optimization, parameter calibration.","oraclaw-cmaes",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-cmaes/SKILL.md",[317],{"path":283,"priority":284},{"basePath":319,"description":320,"displayName":321,"installMethods":322,"rationale":323,"selectedPaths":324,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-decide","Decision intelligence for AI agents. Analyze options, map decision dependencies with PageRank, detect when information sources conflict, and find the choices that matter most.","oraclaw-decide",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-decide/SKILL.md",[325],{"path":283,"priority":284},{"basePath":327,"description":328,"displayName":329,"installMethods":330,"rationale":331,"selectedPaths":332,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-ensemble","Multi-model consensus for AI agents. Combine predictions from multiple LLMs, models, or sources into a mathematically optimal consensus. Auto-weights by historical accuracy.","oraclaw-ensemble",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-ensemble/SKILL.md",[333],{"path":283,"priority":284},{"basePath":335,"description":336,"displayName":337,"installMethods":338,"rationale":339,"selectedPaths":340,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-evolve","Genetic Algorithm optimizer for AI agents. Multi-objective Pareto optimization for portfolio weights, pricing, hyperparameters, marketing mix — any problem with multiple competing goals. Handles nonlinear search spaces that LP solvers cannot.","oraclaw-evolve",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-evolve/SKILL.md",[341],{"path":283,"priority":284},{"basePath":256,"description":10,"displayName":259,"installMethods":343,"rationale":344,"selectedPaths":345,"source":285,"sourceLanguage":18,"type":260},{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-forecast/SKILL.md",[346],{"path":283,"priority":284},{"basePath":348,"description":349,"displayName":350,"installMethods":351,"rationale":352,"selectedPaths":353,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-graph","Network intelligence for AI agents. PageRank, community detection (Louvain), critical path, and bottleneck analysis for any graph of connected things.","oraclaw-graph",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-graph/SKILL.md",[354],{"path":283,"priority":284},{"basePath":356,"description":357,"displayName":358,"installMethods":359,"rationale":360,"selectedPaths":361,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-pathfind","A* pathfinding and task sequencing for AI agents. Find the optimal path through workflows, dependencies, and decision trees. K-shortest paths via Yen's algorithm. Cost/time/risk breakdown.","oraclaw-pathfind",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-pathfind/SKILL.md",[362],{"path":283,"priority":284},{"basePath":364,"description":365,"displayName":366,"installMethods":367,"rationale":368,"selectedPaths":369,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-risk","Risk assessment engine for AI agents. Value at Risk (VaR), CVaR, stress testing, and multi-factor risk scoring. Monte Carlo powered. Built for trading agents, lending agents, and portfolio managers.","oraclaw-risk",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-risk/SKILL.md",[370],{"path":283,"priority":284},{"basePath":372,"description":373,"displayName":374,"installMethods":375,"rationale":376,"selectedPaths":377,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-simulate","Monte Carlo simulation for AI agents. Run thousands of probabilistic scenarios to model risk, forecast revenue, estimate project timelines, and quantify uncertainty. Supports 6 distribution types.","oraclaw-simulate",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-simulate/SKILL.md",[378],{"path":283,"priority":284},{"basePath":380,"description":381,"displayName":382,"installMethods":383,"rationale":384,"selectedPaths":385,"source":285,"sourceLanguage":18,"type":260},"mission-control/packages/clawhub-skills/oraclaw-solver","Industrial-grade scheduling and resource optimization for AI agents. Solve task scheduling with energy matching, budget allocation, and any LP/MIP constraint problem in milliseconds.","oraclaw-solver",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-solver/SKILL.md",[386],{"path":283,"priority":284},{"basePath":388,"description":389,"displayName":390,"installMethods":391,"license":251,"rationale":392,"selectedPaths":393,"source":285,"sourceLanguage":18,"type":403},"mission-control/packages/mcp-server","OraClaw Decision Intelligence — 17 MCP tools for AI agents (6 premium API-key tools + 11 free). Full input/output schemas + MCP behavior annotations on every tool. Optimization (bandit/CMA-ES/genetic/LP-MIP), simulation (Monte Carlo/scenarios), prediction (ARIMA/Holt-Winters/Bayesian/ensemble), scoring (convergence/calibration), graph analytics, anomaly detection, pathfinding, scheduling.","@oraclaw/mcp-server",{"npm":390},"server.json with namespace/server name at mission-control/packages/mcp-server/server.json",[394,396,398,400],{"path":395,"priority":284},"server.json",{"path":397,"priority":284},"package.json",{"path":399,"priority":284},"README.md",{"path":401,"priority":402},"src/index.ts","low","mcp",{"sources":405},[406],"manual",{"closedIssues90d":244,"description":408,"forks":245,"homepage":409,"license":251,"openIssues90d":8,"pushedAt":247,"readmeSize":242,"stars":248,"topics":410},"Deterministic decision-intelligence MCP server for AI agents — 17 tools, 21 algorithms (LinUCB, HiGHS LP/MIP, PageRank, Monte Carlo, CMA-ES, conformal). Sub-25ms. Zero LLM cost. AAA on Glama. Field-validated in 12+ OSS projects.","https://web-olive-one-89.vercel.app",[411,412,413,414,415,416,417,403,418,419,420,421,422,423,424,425,426,427,428,429],"ai-agents","algorithms","api","bandits","decision-intelligence","fastify","machine-learning","optimization","typescript","agent-tools","anthropic","claude-mcp","contextual-bandit","deterministic-tools","linear-programming","llm-tools","model-context-protocol","monte-carlo","pagerank",{"classifiedAt":431,"discoverAt":432,"extractAt":433,"githubAt":433,"updatedAt":431},1778698837409,1778698831609,1778698835357,[222,220,223,217,221,219,218],{"evaluatedAt":254,"extractAt":436,"updatedAt":437},1778698837670,1778699187952,[],[440,469,499,523,542,573],{"_creationTime":441,"_id":442,"community":443,"display":444,"identity":450,"providers":455,"relations":463,"tags":465,"workflow":466},1778691799740.4976,"k1719vgzsxtv8exr684y5ww47s86mzqh",{"reviewCount":8},{"description":445,"installMethods":446,"name":448,"sourceUrl":449},"Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.",{"claudeCode":447},"K-Dense-AI/claude-scientific-skills","TimesFM Forecasting","https://github.com/K-Dense-AI/claude-scientific-skills",{"basePath":451,"githubOwner":452,"githubRepo":453,"locale":18,"slug":454,"type":260},"scientific-skills/timesfm-forecasting","K-Dense-AI","claude-scientific-skills","timesfm-forecasting",{"evaluate":456,"extract":462},{"promptVersionExtension":210,"promptVersionScoring":211,"score":214,"tags":457,"targetMarket":224,"tier":225},[218,217,458,459,460,417,461],"univariate","foundation-model","timesfm","python",{"commitSha":266,"license":251},{"repoId":464},"kd79rphh5gexy91xmpxc05h5mh86mm9r",[217,459,417,461,218,460,458],{"evaluatedAt":467,"extractAt":468,"updatedAt":467},1778694590335,1778691799740,{"_creationTime":470,"_id":471,"community":472,"display":473,"identity":479,"providers":483,"relations":493,"tags":495,"workflow":496},1778675145461.8716,"k173knhqazsd87a0kmz3jp3tmn86nty4",{"reviewCount":8},{"description":474,"installMethods":475,"name":477,"sourceUrl":478},"Part of the AlterLab Academic Skills suite. This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.",{"claudeCode":476},"AlterLab-IEU/AlterLab-Academic-Skills","alterlab-aeon","https://github.com/AlterLab-IEU/AlterLab-Academic-Skills",{"basePath":480,"githubOwner":481,"githubRepo":482,"locale":18,"slug":477,"type":260},"skills/domain-specific/alterlab-aeon","AlterLab-IEU","AlterLab-Academic-Skills",{"evaluate":484,"extract":492},{"promptVersionExtension":210,"promptVersionScoring":211,"score":485,"tags":486,"targetMarket":224,"tier":225},98,[218,417,487,488,217,489,490,491],"classification","regression","anomaly-detection","clustering","scikit-learn",{"commitSha":266},{"repoId":494},"kd7fqvj70pvyn4r3q9kctpnd7d86mfqd",[489,487,490,217,417,488,491,218],{"evaluatedAt":497,"extractAt":498,"updatedAt":497},1778678143254,1778675145461,{"_creationTime":500,"_id":501,"community":502,"display":503,"identity":505,"providers":506,"relations":518,"tags":519,"workflow":520},1778698837670.7988,"k177gnp7tvr9phd9psfw21zgcs86ndx2",{"reviewCount":8},{"description":304,"installMethods":504,"name":305,"sourceUrl":14},{"claudeCode":12},{"basePath":303,"githubOwner":257,"githubRepo":258,"locale":18,"slug":305,"type":260},{"evaluate":507,"extract":517},{"promptVersionExtension":210,"promptVersionScoring":211,"score":508,"tags":509,"targetMarket":224,"tier":225},97,[510,217,219,511,512,513,514,515,516],"calibration","accuracy","scoring","convergence","brier-score","statistics","analysis",{"commitSha":266},{"repoId":268},[511,516,514,510,513,217,219,512,515],{"evaluatedAt":521,"extractAt":436,"updatedAt":522},1778698906461,1778699186995,{"_creationTime":524,"_id":525,"community":526,"display":527,"identity":530,"providers":533,"relations":538,"tags":539,"workflow":540},1778675145461.859,"k17bkmbgyytbmdsdn8rfyyzwwd86n5h5",{"reviewCount":8},{"description":528,"installMethods":529,"name":448,"sourceUrl":478},"Part of the AlterLab Academic Skills suite. Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.",{"claudeCode":476},{"basePath":531,"githubOwner":481,"githubRepo":482,"locale":18,"slug":532,"type":260},"skills/data-science/alterlab-timesfm","alterlab-timesfm",{"evaluate":534,"extract":537},{"promptVersionExtension":210,"promptVersionScoring":211,"score":535,"tags":536,"targetMarket":224,"tier":225},96,[217,218,459,461,223],{"commitSha":266,"license":251},{"repoId":494},[223,217,459,461,218],{"evaluatedAt":541,"extractAt":498,"updatedAt":541},1778676882391,{"_creationTime":543,"_id":544,"community":545,"display":546,"identity":552,"providers":556,"relations":566,"tags":569,"workflow":570},1778695548458.3625,"k17d4591dpyfqfybnac81wp9y586nh7n",{"reviewCount":8},{"description":547,"installMethods":548,"name":550,"sourceUrl":551},"Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.\n",{"claudeCode":549},"pjt222/agent-almanac","forecast-operational-metrics","https://github.com/pjt222/agent-almanac",{"basePath":553,"githubOwner":554,"githubRepo":555,"locale":18,"slug":550,"type":260},"skills/forecast-operational-metrics","pjt222","agent-almanac",{"evaluate":557,"extract":565},{"promptVersionExtension":210,"promptVersionScoring":211,"score":558,"tags":559,"targetMarket":224,"tier":225},95,[217,218,560,561,562,563,564],"prophet","statsmodels","capacity-planning","grafana","mlops",{"commitSha":266},{"parentExtensionId":567,"repoId":568},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[562,217,563,564,560,561,218],{"evaluatedAt":571,"extractAt":572,"updatedAt":571},1778698282903,1778695548458,{"_creationTime":574,"_id":575,"community":576,"display":577,"identity":581,"providers":584,"relations":590,"tags":591,"workflow":592},1778691799740.4673,"k178b4tn4gxjqbpqfzkces5qm186m0z3",{"reviewCount":8},{"description":578,"installMethods":579,"name":580,"sourceUrl":449},"This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.",{"claudeCode":447},"Aeon Time Series Machine Learning",{"basePath":582,"githubOwner":452,"githubRepo":453,"locale":18,"slug":583,"type":260},"scientific-skills/aeon","aeon",{"evaluate":585,"extract":588},{"promptVersionExtension":210,"promptVersionScoring":211,"score":558,"tags":586,"targetMarket":224,"tier":225},[218,417,217,487,488,461,587],"data-analysis",{"commitSha":266,"license":589},"BSD-3-Clause",{"repoId":464},[487,587,217,417,461,488,218],{"evaluatedAt":593,"extractAt":468,"updatedAt":593},1778691874025]