[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-Whatsonyourmind-oraclaw-calibrate-en":3,"guides-for-Whatsonyourmind-oraclaw-calibrate":428,"similar-k177gnp7tvr9phd9psfw21zgcs86ndx2-en":429},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":246,"isFallback":241,"parentExtension":251,"providers":252,"relations":257,"repo":259,"tags":424,"workflow":425},1778698837670.7988,"k177gnp7tvr9phd9psfw21zgcs86ndx2",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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.",{"claudeCode":12},"Whatsonyourmind/oraclaw","oraclaw-calibrate","https://github.com/Whatsonyourmind/oraclaw",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":227,"workflow":244},1778698906461.1497,"kn78bragfp4h3bsjsxbb30xw6h86n72c","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":200,"promptVersionExtension":204,"promptVersionScoring":205,"purpose":206,"rationale":207,"score":208,"summary":209,"tags":210,"targetMarket":220,"tier":221,"useCases":222},[21,26,29,32,36,39,43,47,50,53,57,61,64,68,71,74,77,80,83,86,90,94,98,102,106,109,113,117,121,124,127,130,133,136,139,143,147,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem of scoring AI agent prediction quality, mentioning specific metrics like Brier score and log score.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill provides deterministic algorithms for optimization and analysis, offering value beyond what a general LLM can provide for mathematical tasks.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill is presented as ready for production with multiple integrations, SDKs, and a clear API, covering the lifecycle of prediction scoring.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses on prediction quality scoring and related mathematical/statistical analysis, with a clear domain.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately reflects the skill's capabilities for scoring prediction quality and analyzing convergence.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The skill exposes two narrow, well-defined tools: `score_calibration` and `score_convergence`.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The SKILL.md frontmatter and tool descriptions clearly list parameters and the required ORACLAW_API_KEY environment variable.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","Tool names like `score_calibration` and `score_convergence` are descriptive and indicate their function.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","Tool inputs are structured arrays, and outputs are well-defined JSON objects containing only the promised scoring and analysis results.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is licensed under MIT, a permissive open-source license, clearly indicated in the LICENSE file and README.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit 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does not appear to perform file system operations outside of its designated scope.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox escape primitives","No evidence of detached processes or deny-retry loops is found in the provided source.",{"category":65,"check":81,"severity":24,"summary":82},"Data Exfiltration","The skill processes numerical data for scoring and does not appear to read or submit confidential user data.",{"category":65,"check":84,"severity":24,"summary":85},"Hidden Text Tricks","The bundled content appears free of hidden steering tricks and uses clean, printable ASCII and standard Unicode.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque code execution","The skill's logic is based on defined algorithms and API calls, not obfuscated code or runtime execution.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The skill functions as a service and does not make assumptions about user project 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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",[376],{"path":273,"priority":274},{"basePath":378,"description":379,"displayName":380,"installMethods":381,"license":242,"rationale":382,"selectedPaths":383,"source":275,"sourceLanguage":18,"type":393},"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":380},"server.json with namespace/server name at mission-control/packages/mcp-server/server.json",[384,386,388,390],{"path":385,"priority":274},"server.json",{"path":387,"priority":274},"package.json",{"path":389,"priority":274},"README.md",{"path":391,"priority":392},"src/index.ts","low","mcp",{"sources":395},[396],"manual",{"closedIssues90d":235,"description":398,"forks":236,"homepage":399,"license":242,"openIssues90d":8,"pushedAt":238,"readmeSize":233,"stars":239,"topics":400},"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. 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Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet analysis.",{"claudeCode":437},"alirezarezvani/claude-skills","Financial 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Forecast",{"basePath":330,"githubOwner":248,"githubRepo":249,"locale":18,"slug":332,"type":250},{"evaluate":470,"extract":477},{"promptVersionExtension":204,"promptVersionScoring":205,"score":447,"tags":471,"targetMarket":220,"tier":221},[212,472,213,473,474,475,476],"time-series","arima","holt-winters","analytics","data-science",{"commitSha":256,"license":242},{"repoId":258},[475,473,476,212,474,213,472],{"evaluatedAt":481,"extractAt":426,"updatedAt":482},1778698975269,1778699187952,{"_creationTime":484,"_id":485,"community":486,"display":487,"identity":493,"providers":497,"relations":503,"tags":505,"workflow":506},1778675659559.518,"k17bjgfyvbnt05kpma9n0gn4j186n152",{"reviewCount":8},{"description":488,"installMethods":489,"name":491,"sourceUrl":492},"Applies prompt repetition to improve accuracy for non-reasoning 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