[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-Whatsonyourmind-oraclaw-simulate-en":3,"guides-for-Whatsonyourmind-oraclaw-simulate":424,"similar-k1706ed747sfrsfzvmdj4fzckh86nnw3-en":425},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":243,"isFallback":238,"parentExtension":248,"providers":249,"relations":254,"repo":256,"tags":420,"workflow":421},1778698837670.801,"k1706ed747sfrsfzvmdj4fzckh86nnw3",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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.",{"claudeCode":12},"Whatsonyourmind/oraclaw","oraclaw-simulate","https://github.com/Whatsonyourmind/oraclaw",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":224,"workflow":241},1778699039921.4949,"kn78j8v532x525wkd3vmyw0z7h86nz0w","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":217,"tier":218,"useCases":219},[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 modeling uncertainty and quantifying risk using Monte Carlo simulation for AI agents.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers mathematical correctness and deterministic optimization via algorithms like Monte Carlo, which goes beyond standard LLM capabilities and provides significant value.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill provides a fully implemented Monte Carlo simulation tool with clear documentation, examples, and pricing, suitable for immediate use in AI agent workflows.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill is focused on Monte Carlo simulation and related quantitative analysis, aligning with its name and description without expanding into unrelated domains.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately and concisely reflects the skill's capabilities in Monte Carlo simulation for AI agents.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The skill exposes a single, well-defined tool `simulate_montecarlo` with a structured input schema, adhering to the principle of narrow verb-noun specialization.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The `SKILL.md` and README clearly document the input variables, distributions, formula, iterations, and pricing for the `simulate_montecarlo` tool.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","The single exposed tool `simulate_montecarlo` is descriptive and follows kebab-case convention.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","The `simulate_montecarlo` tool's input schema is well-defined with specific parameters for distributions and iterations, and the output promises structured results (mean, stdDev, percentiles, histogram).",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The project is licensed under the MIT license, as indicated by the LICENSE file and README, which is a permissive open-source license.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The latest commit was on May 2, 2026, which is within the last 3 months, indicating active maintenance.",{"category":58,"check":62,"severity":24,"summary":63},"Dependency Management","The project uses npm and appears to have lockfiles, indicating good dependency management practices.",{"category":65,"check":66,"severity":24,"summary":67},"Security","Secret Management","The skill requires an `ORACLAW_API_KEY` environment variable for premium features, but this is documented and handled as an environment variable, not hardcoded.",{"category":65,"check":69,"severity":24,"summary":70},"Injection","The skill's code is primarily focused on mathematical calculations and does not appear to load or execute untrusted third-party data as instructions.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Supply-Chain Grenades","The skill does not fetch external files at runtime or execute code from remote sources; all necessary logic is bundled.",{"category":65,"check":75,"severity":24,"summary":76},"Sandbox Isolation","The skill performs calculations and does not appear to interact with or modify files outside its intended scope.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox escape primitives","There are no indications of detached-process spawns or deny-retry loops in the provided code.",{"category":65,"check":81,"severity":24,"summary":82},"Data Exfiltration","The skill performs calculations and does not submit any user data or confidential information to third parties.",{"category":65,"check":84,"severity":24,"summary":85},"Hidden Text Tricks","The bundled content and descriptions appear to be free of hidden-steering tricks, invisible Unicode characters, or other obfuscation methods.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque code execution","The skill's logic is implemented in readable TypeScript and does not involve obfuscated code, base64 payloads, or runtime script fetching.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The skill performs in-memory calculations and does not make assumptions about the user's project file structure.",{"category":95,"check":96,"severity":24,"summary":97},"Trust","Issues Attention","The project has 0 open issues and 44 closed issues in the last 90 days, indicating a high closure rate and active maintenance.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","The skill has a `version: 1.0.0` in its frontmatter, and the `pushedAt` timestamp indicates recent activity, establishing a clear version signal.",{"category":103,"check":104,"severity":24,"summary":105},"Execution","Validation","The input schema for `simulate_montecarlo` is clearly defined, and the expected output structure is documented, implying proper 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},"Code Execution","Error Handling","While specific error handling isn't detailed, the nature of the skill as a calculation engine suggests that standard error reporting would apply, and the clear tool definition implies structured error handling.",{"category":110,"check":114,"severity":115,"summary":116},"Logging","not_applicable","The skill is analytical and does not perform destructive actions or outbound calls that would necessitate local audit logging.",{"category":118,"check":119,"severity":24,"summary":120},"Compliance","GDPR","The skill performs mathematical simulations and does not process personal data.",{"category":118,"check":122,"severity":24,"summary":123},"Target market","The skill's functionality is mathematical and not tied to any specific geographic or legal jurisdiction, making it globally applicable.",{"category":91,"check":125,"severity":24,"summary":126},"Runtime stability","The skill is based on standard TypeScript/JavaScript and mathematical algorithms, with no apparent assumptions about specific operating systems or shells.",{"category":44,"check":128,"severity":24,"summary":129},"README","The README is comprehensive, details the extension's purpose, provides installation instructions, and highlights key features and implementations.",{"category":33,"check":131,"severity":24,"summary":132},"Tool surface size","The extension exposes a single tool, `simulate_montecarlo`, 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, including the `simulate_montecarlo` tool, have clear implementations documented in the SKILL.md and README.",{"category":140,"check":141,"severity":24,"summary":142},"Install","Installation instruction","The README provides clear installation instructions for the MCP server, REST API, and npm SDK, along with usage examples.",{"category":144,"check":145,"severity":24,"summary":146},"Errors","Actionable error messages","While specific error message examples are not provided, the structured tool definition and API documentation imply that errors would be actionable.",{"category":103,"check":148,"severity":24,"summary":149},"Pinned dependencies","The project uses npm and lists SDK packages, implying dependency management with lockfiles, and the SKILL.md frontmatter specifies required environment variables.",{"category":33,"check":151,"severity":115,"summary":152},"Dry-run preview","The skill is analytical and does not perform state-changing operations or send data outward, making a dry-run preview not applicable.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The skill performs in-memory calculations and does not involve remote calls or state-changing operations, thus idempotency and timeouts are not applicable in the traditional sense.",{"category":118,"check":158,"severity":24,"summary":159},"Telemetry opt-in","There is no mention of telemetry being collected by this skill; any potential telemetry would be handled by the broader MCP framework, and this skill itself does not emit telemetry.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The skill's purpose is precisely defined as Monte Carlo simulation for AI agents to model uncertainty and quantify risk, with clear use cases and a specific tool.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The SKILL.md frontmatter is concise and effectively summarizes the core capability of Monte Carlo simulation for AI agents.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md body is concise, outlining the tool and rules, and delegates deeper material like examples and pricing to clear sections.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","The SKILL.md provides a concise overview and links to a clear example, adhering to progressive disclosure principles.",{"category":170,"check":174,"severity":115,"summary":175},"Forked exploration","This skill is a focused calculation tool and does not involve deep exploration or code review, making `context: fork` not applicable.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","A clear, ready-to-use JSON example for a revenue forecast is provided, demonstrating input, invocation, and expected output structure.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The rules section implicitly handles edge cases by recommending specific distributions for certain types of data (e.g., lognormal for positive values) and specifying minimum iterations for reliability.",{"category":110,"check":183,"severity":115,"summary":184},"Tool Fallback","This skill does not appear to rely on an external MCP server; it functions as a standalone calculation tool.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","As a calculation tool, it is unlikely to encounter unexpected environmental states that would require halting; input validation would be the primary mechanism for handling unexpected pre-state.",{"category":91,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill is self-contained and focused on Monte Carlo simulation, with no implicit reliance on other skills.",1778699039806,"This skill enables AI agents to perform Monte Carlo simulations, supporting 6 distribution types to model risk, forecast revenue, estimate project timelines, and quantify uncertainty.",[195,196,197,198,199],"Run thousands of probabilistic scenarios","Model risk and forecast revenue","Estimate project timelines","Quantify uncertainty","Support for 6 distribution types",[201,202,203],"Providing real-time trading execution","Performing deterministic financial calculations without probabilistic inputs","Replacing core LLM reasoning capabilities","3.0.0","4.4.0","To provide AI agents with mathematically sound Monte Carlo simulation capabilities for quantitative analysis, risk modeling, and forecasting.","High score due to excellent documentation, clear problem-solving, and strong production readiness. No warnings or criticals found.",98,"A high-quality skill for Monte Carlo simulation, providing robust quantitative analysis for AI agents.",[211,212,213,214,215,216],"monte-carlo","simulation","risk-analysis","forecasting","probability","finance","global","verified",[220,221,222,223],"Estimate the probability of hitting a revenue target","Model project timelines with uncertainty","Calculate Value at Risk for a portfolio","Run sensitivity analysis on business assumptions",{"codeQuality":225,"collectedAt":227,"documentation":228,"maintenance":231,"security":237,"testCoverage":240},{"hasLockfile":226},true,1778699023744,{"descriptionLength":229,"readmeSize":230},196,9472,{"closedIssues90d":232,"forks":233,"hasChangelog":226,"manifestVersion":234,"openIssues90d":8,"pushedAt":235,"stars":236},44,2,"1.0.0",1777714123000,8,{"hasNpmPackage":238,"license":239,"smitheryVerified":238},false,"MIT",{"hasCi":226,"hasTests":226},{"updatedAt":242},1778699039921,{"basePath":244,"githubOwner":245,"githubRepo":246,"locale":18,"slug":13,"type":247},"mission-control/packages/clawhub-skills/oraclaw-simulate","Whatsonyourmind","oraclaw","skill",null,{"evaluate":250,"extract":252},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":251,"targetMarket":217,"tier":218},[211,212,213,214,215,216],{"commitSha":253},"HEAD",{"repoId":255},"kd76fmxm1ng903s4fmj0p7hxxs86ndkg",{"_creationTime":257,"_id":255,"identity":258,"providers":259,"workflow":416},1778698831609.0093,{"githubOwner":245,"githubRepo":246,"sourceUrl":14},{"classify":260,"discover":391,"github":394},{"commitSha":253,"extensions":261},[262,273,281,289,297,305,313,321,329,337,345,353,361,366,374],{"basePath":263,"description":264,"displayName":265,"installMethods":266,"rationale":267,"selectedPaths":268,"source":272,"sourceLanguage":18,"type":247},"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",[269],{"path":270,"priority":271},"SKILL.md","mandatory","rule",{"basePath":274,"description":275,"displayName":276,"installMethods":277,"rationale":278,"selectedPaths":279,"source":272,"sourceLanguage":18,"type":247},"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",[280],{"path":270,"priority":271},{"basePath":282,"description":283,"displayName":284,"installMethods":285,"rationale":286,"selectedPaths":287,"source":272,"sourceLanguage":18,"type":247},"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",[288],{"path":270,"priority":271},{"basePath":290,"description":291,"displayName":292,"installMethods":293,"rationale":294,"selectedPaths":295,"source":272,"sourceLanguage":18,"type":247},"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",[296],{"path":270,"priority":271},{"basePath":298,"description":299,"displayName":300,"installMethods":301,"rationale":302,"selectedPaths":303,"source":272,"sourceLanguage":18,"type":247},"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",[304],{"path":270,"priority":271},{"basePath":306,"description":307,"displayName":308,"installMethods":309,"rationale":310,"selectedPaths":311,"source":272,"sourceLanguage":18,"type":247},"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",[312],{"path":270,"priority":271},{"basePath":314,"description":315,"displayName":316,"installMethods":317,"rationale":318,"selectedPaths":319,"source":272,"sourceLanguage":18,"type":247},"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",[320],{"path":270,"priority":271},{"basePath":322,"description":323,"displayName":324,"installMethods":325,"rationale":326,"selectedPaths":327,"source":272,"sourceLanguage":18,"type":247},"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",[328],{"path":270,"priority":271},{"basePath":330,"description":331,"displayName":332,"installMethods":333,"rationale":334,"selectedPaths":335,"source":272,"sourceLanguage":18,"type":247},"mission-control/packages/clawhub-skills/oraclaw-forecast","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.","oraclaw-forecast",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-forecast/SKILL.md",[336],{"path":270,"priority":271},{"basePath":338,"description":339,"displayName":340,"installMethods":341,"rationale":342,"selectedPaths":343,"source":272,"sourceLanguage":18,"type":247},"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",[344],{"path":270,"priority":271},{"basePath":346,"description":347,"displayName":348,"installMethods":349,"rationale":350,"selectedPaths":351,"source":272,"sourceLanguage":18,"type":247},"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",[352],{"path":270,"priority":271},{"basePath":354,"description":355,"displayName":356,"installMethods":357,"rationale":358,"selectedPaths":359,"source":272,"sourceLanguage":18,"type":247},"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",[360],{"path":270,"priority":271},{"basePath":244,"description":10,"displayName":13,"installMethods":362,"rationale":363,"selectedPaths":364,"source":272,"sourceLanguage":18,"type":247},{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-simulate/SKILL.md",[365],{"path":270,"priority":271},{"basePath":367,"description":368,"displayName":369,"installMethods":370,"rationale":371,"selectedPaths":372,"source":272,"sourceLanguage":18,"type":247},"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",[373],{"path":270,"priority":271},{"basePath":375,"description":376,"displayName":377,"installMethods":378,"license":239,"rationale":379,"selectedPaths":380,"source":272,"sourceLanguage":18,"type":390},"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":377},"server.json with namespace/server name at mission-control/packages/mcp-server/server.json",[381,383,385,387],{"path":382,"priority":271},"server.json",{"path":384,"priority":271},"package.json",{"path":386,"priority":271},"README.md",{"path":388,"priority":389},"src/index.ts","low","mcp",{"sources":392},[393],"manual",{"closedIssues90d":232,"description":395,"forks":233,"homepage":396,"license":239,"openIssues90d":8,"pushedAt":235,"readmeSize":230,"stars":236,"topics":397},"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",[398,399,400,401,402,403,404,390,405,406,407,408,409,410,411,412,413,414,211,415],"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","pagerank",{"classifiedAt":417,"discoverAt":418,"extractAt":419,"githubAt":419,"updatedAt":417},1778698837409,1778698831609,1778698835357,[216,214,211,215,213,212],{"evaluatedAt":242,"extractAt":422,"updatedAt":423},1778698837670,1778699188779,[],[426,456,486,516,539,569],{"_creationTime":427,"_id":428,"community":429,"display":430,"identity":436,"providers":440,"relations":450,"tags":452,"workflow":453},1778688112811.7527,"k17enr6rktmxh0enswrmze6et186mq12",{"reviewCount":8},{"description":431,"installMethods":432,"name":434,"sourceUrl":435},"Model best-case, worst-case, and likely revenue scenarios with sensitivity analysis for strategic planning. Use when: building financial forecasts; presenting board scenarios; planning headcount around revenue uncertainty; modeling pricing changes impact; preparing investor updates with upside/downside ranges",{"claudeCode":433},"guia-matthieu/clawfu-skills","forecast-scenarios","https://github.com/guia-matthieu/clawfu-skills",{"basePath":437,"githubOwner":438,"githubRepo":439,"locale":18,"slug":434,"type":247},"skills/revops/forecast-scenarios","guia-matthieu","clawfu-skills",{"evaluate":441,"extract":449},{"promptVersionExtension":204,"promptVersionScoring":205,"score":442,"tags":443,"targetMarket":217,"tier":218},100,[216,214,444,445,446,447,448],"revenue","planning","strategy","sensitivity-analysis","mckinsey",{"commitSha":253},{"repoId":451},"kd72qvzyvm658ya7pbyh5ey47h86md53",[216,214,448,445,444,447,446],{"evaluatedAt":454,"extractAt":455,"updatedAt":454},1778690475880,1778688112811,{"_creationTime":457,"_id":458,"community":459,"display":460,"identity":466,"providers":471,"relations":479,"tags":482,"workflow":483},1778675056600.2537,"k17ask0fam6yfypdvf5562p15986m925",{"reviewCount":8},{"description":461,"installMethods":462,"name":464,"sourceUrl":465},"Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. 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":463},"alirezarezvani/claude-skills","Financial Analyst","https://github.com/alirezarezvani/claude-skills",{"basePath":467,"githubOwner":468,"githubRepo":469,"locale":18,"slug":470,"type":247},"finance/skills/financial-analyst","alirezarezvani","claude-skills","financial-analyst",{"evaluate":472,"extract":478},{"promptVersionExtension":204,"promptVersionScoring":205,"score":442,"tags":473,"targetMarket":217,"tier":218},[216,474,475,214,476,477],"analysis","valuation","budgeting","python",{"commitSha":253,"license":239},{"parentExtensionId":480,"repoId":481},"k174nmf7jahgcsdnzenmdxfcbh86m85y","kd7ff9s1w43mfyy1n7hf87816186m6px",[474,476,216,214,477,475],{"evaluatedAt":484,"extractAt":485,"updatedAt":484},1778683964036,1778675056600,{"_creationTime":487,"_id":488,"community":489,"display":490,"identity":496,"providers":500,"relations":509,"tags":512,"workflow":513},1778695548458.402,"k179k5vddwcqrrr23r6hfavx5n86mf81",{"reviewCount":8},{"description":491,"installMethods":492,"name":494,"sourceUrl":495},"Simulate stochastic processes (Markov chains, random walks, SDEs, MCMC) with convergence diagnostics, variance reduction, and visualization. Use when generating sample paths for estimation, prediction, or visualization; when analytical solutions are intractable; running Monte Carlo estimation needing convergence guarantees; validating analytical results against empirical simulation; or sampling from complex posteriors via MCMC.\n",{"claudeCode":493},"pjt222/agent-almanac","simulate-stochastic-process","https://github.com/pjt222/agent-almanac",{"basePath":497,"githubOwner":498,"githubRepo":499,"locale":18,"slug":494,"type":247},"skills/simulate-stochastic-process","pjt222","agent-almanac",{"evaluate":501,"extract":508},{"promptVersionExtension":204,"promptVersionScoring":205,"score":502,"tags":503,"targetMarket":217,"tier":218},97,[504,212,505,211,506,507],"stochastic-processes","mcmc","statistics","numerical-methods",{"commitSha":253},{"parentExtensionId":510,"repoId":511},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[505,211,507,212,506,504],{"evaluatedAt":514,"extractAt":515,"updatedAt":514},1778701677011,1778695548458,{"_creationTime":517,"_id":518,"community":519,"display":520,"identity":524,"providers":527,"relations":535,"tags":536,"workflow":537},1778695548458.3777,"k1720hbr4h69f4xs06yz2j8cd586mfam",{"reviewCount":8},{"description":521,"installMethods":522,"name":523,"sourceUrl":495},"Build and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.\n",{"claudeCode":493},"Model Markov Chain",{"basePath":525,"githubOwner":498,"githubRepo":499,"locale":18,"slug":526,"type":247},"skills/model-markov-chain","model-markov-chain",{"evaluate":528,"extract":534},{"promptVersionExtension":204,"promptVersionScoring":205,"score":502,"tags":529,"targetMarket":217,"tier":218},[530,531,532,533,215,212],"stochastic","markov-chain","transition-matrix","stationary-distribution",{"commitSha":253,"license":239},{"parentExtensionId":510,"repoId":511},[531,215,212,533,530,532],{"evaluatedAt":538,"extractAt":515,"updatedAt":538},1778699521772,{"_creationTime":540,"_id":541,"community":542,"display":543,"identity":549,"providers":554,"relations":562,"tags":565,"workflow":566},1778696691708.3274,"k170az7r02e9e2v47mpy80kx6n86nff3",{"reviewCount":8},{"description":544,"installMethods":545,"name":547,"sourceUrl":548},"Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy",{"claudeCode":546},"ruvnet/ruflo","Trader Regime","https://github.com/ruvnet/ruflo",{"basePath":550,"githubOwner":551,"githubRepo":552,"locale":18,"slug":553,"type":247},"plugins/ruflo-neural-trader/skills/trader-regime","ruvnet","ruflo","trader-regime",{"evaluate":555,"extract":561},{"promptVersionExtension":204,"promptVersionScoring":205,"score":442,"tags":556,"targetMarket":217,"tier":218},[216,557,558,559,406,560],"trading","market-analysis","ai","cli",{"commitSha":253,"license":239},{"parentExtensionId":563,"repoId":564},"k17drge8h1fgzchr0p4jaeg33n86mwmy","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[559,560,216,558,557,406],{"evaluatedAt":567,"extractAt":568,"updatedAt":567},1778701108877,1778696691708,{"_creationTime":570,"_id":571,"community":572,"display":573,"identity":579,"providers":583,"relations":588,"tags":591,"workflow":592},1778693539593.1863,"k173a67a16bpq0e29wjd85v71986nx03",{"reviewCount":8},{"description":574,"installMethods":575,"name":577,"sourceUrl":578},"Domain knowledge for AI trading memory — Outcome-Weighted Memory (OWM) architecture, 5 memory types, recall scoring, and behavioral analysis. Use when recording trades, recalling similar contexts, analyzing performance, or checking behavioral drift. Triggers on \"record trade\", \"remember trade\", \"recall\", \"similar trades\", \"performance\", \"behavioral\", \"disposition\", \"affective state\", \"confidence\".",{"claudeCode":576},"mnemox-ai/tradememory-protocol","trading-memory","https://github.com/mnemox-ai/tradememory-protocol",{"basePath":580,"githubOwner":581,"githubRepo":582,"locale":18,"slug":577,"type":247},"tradememory-plugin/skills/trading-memory","mnemox-ai","tradememory-protocol",{"evaluate":584,"extract":587},{"promptVersionExtension":204,"promptVersionScoring":205,"score":442,"tags":585,"targetMarket":217,"tier":218},[557,559,586,216,477],"memory",{"commitSha":253},{"parentExtensionId":589,"repoId":590},"k170vxkqee48k2xq1v55a025nh86nzn7","kd73z11kfekksxyrs8ds0snacs86ncdy",[559,216,586,477,557],{"evaluatedAt":593,"extractAt":594,"updatedAt":595},1778693719816,1778693539593,1778693833320]