[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-Whatsonyourmind-oraclaw-simulate-de":3,"guides-for-Whatsonyourmind-oraclaw-simulate":426,"similar-k17b3w0c5t3t6ksbg5p1dr8m0186m5s9-de":427},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":241,"isFallback":236,"parentExtension":246,"providers":247,"relations":253,"repo":256,"tags":422,"workflow":423},1778699169481.5479,"k17b3w0c5t3t6ksbg5p1dr8m0186m5s9",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Monte-Carlo-Simulation für KI-Agenten. Führen Sie Tausende von probabilistischen Szenarien aus, um Risiken zu modellieren, Umsätze zu prognostizieren, Projektzeitpläne abzuschätzen und Unsicherheiten zu quantifizieren. Unterstützt 6 Verteilungstypen.",{"claudeCode":12},"Whatsonyourmind/oraclaw","oraclaw-simulate","https://github.com/Whatsonyourmind/oraclaw",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":222,"workflow":239},1778699169481.548,"kn7f7qrwqcxxq4pbxkg2xxza6586nk9g","de",{"checks":20,"evaluatedAt":191,"extensionSummary":192,"features":193,"nonGoals":199,"promptVersionExtension":203,"promptVersionScoring":204,"purpose":205,"rationale":206,"score":207,"summary":208,"tags":209,"tier":216,"useCases":217},[21,26,29,32,36,39,43,47,50,53,57,61,64,68,71,74,77,80,83,86,90,94,98,102,106,109,113,117,121,124,127,130,133,136,139,143,147,150,153,157,160,163,166,169,173,176,179,182,185,188],{"category":22,"check":23,"severity":24,"summary":25},"Praktischer Nutzen","Problemrelevanz","pass","Die Beschreibung gibt klar das Problem der Modellierung von Unsicherheiten und der Quantifizierung von Risiken mittels Monte-Carlo-Simulation für KI-Agenten wieder.",{"category":22,"check":27,"severity":24,"summary":28},"Alleinstellungsmerkmal","Die Fähigkeit bietet mathematische Korrektheit und deterministische Optimierung durch Algorithmen wie Monte Carlo, was über die Standardfähigkeiten von LLMs hinausgeht und erheblichen Mehrwert bietet.",{"category":22,"check":30,"severity":24,"summary":31},"Produktionsreife","Die Fähigkeit stellt ein vollständig implementiertes Monte-Carlo-Simulationswerkzeug mit klarer Dokumentation, Beispielen und Preisgestaltung bereit, das für den sofortigen Einsatz in KI-Agenten-Workflows geeignet ist.",{"category":33,"check":34,"severity":24,"summary":35},"Umfang","Single Responsibility Principle","Die Fähigkeit konzentriert sich auf Monte-Carlo-Simulationen und verwandte quantitative Analysen, was mit ihrem Namen und ihrer Beschreibung übereinstimmt, ohne sich auf andere Domänen auszudehnen.",{"category":33,"check":37,"severity":24,"summary":38},"Qualität der Beschreibung","Die angezeigte Beschreibung spiegelt die Fähigkeiten der Fähigkeit zur Monte-Carlo-Simulation für KI-Agenten genau und prägnant wider.",{"category":40,"check":41,"severity":24,"summary":42},"Aufruf","Geltungsbereich von Werkzeugen","Die Fähigkeit stellt ein einziges, gut definiertes Werkzeug `simulate_montecarlo` mit einem strukturierten Eingabeschema bereit und hält sich an das Prinzip der engen Spezialisierung auf Nomen-Verb-Paare.",{"category":44,"check":45,"severity":24,"summary":46},"Dokumentation","Konfigurations- & Parameterreferenz","Die `SKILL.md` und README dokumentieren klar die Eingabevariablen, Verteilungen, Formeln, Iterationen und Preise für das Werkzeug `simulate_montecarlo`.",{"category":33,"check":48,"severity":24,"summary":49},"Benennung von Werkzeugen","Das einzige bereitgestellte Werkzeug `simulate_montecarlo` ist beschreibend und folgt der Kebab-Case-Konvention.",{"category":33,"check":51,"severity":24,"summary":52},"Minimale I/O-Oberfläche","Das Eingabeschema des Werkzeugs `simulate_montecarlo` ist mit spezifischen Parametern für Verteilungen und Iterationen gut definiert, und die Ausgabe verspricht strukturierte Ergebnisse (Mittelwert, Standardabweichung, Perzentile, Histogramm).",{"category":54,"check":55,"severity":24,"summary":56},"Lizenz","Nutzbarkeit der Lizenz","Das Projekt ist unter der MIT-Lizenz lizenziert, wie die LICENSE-Datei und die README angeben, was eine permissive Open-Source-Lizenz ist.",{"category":58,"check":59,"severity":24,"summary":60},"Wartung","Aktualität von Commits","Der letzte Commit war am 2. Mai 2026, was innerhalb der letzten 3 Monate liegt und auf eine aktive Wartung hindeutet.",{"category":58,"check":62,"severity":24,"summary":63},"Abhängigkeitsmanagement","Das Projekt verwendet npm und verfügt über Lockfiles, was auf gute Praktiken im Abhängigkeitsmanagement hindeutet.",{"category":65,"check":66,"severity":24,"summary":67},"Sicherheit","Geheimnisverwaltung","Die Fähigkeit erfordert eine Umgebungsvariable `ORACLAW_API_KEY` für Premium-Funktionen, dies ist jedoch dokumentiert und wird als Umgebungsvariable behandelt, nicht hartcodiert.",{"category":65,"check":69,"severity":24,"summary":70},"Injektion","Der Code der Fähigkeit konzentriert sich hauptsächlich auf mathematische Berechnungen und lädt oder führt keine nicht vertrauenswürdigen Drittanbieterdaten als Anweisungen aus.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Lieferketten-Granaten","Die Fähigkeit ruft zur Laufzeit keine externen Dateien ab oder führt Code von Remote-Quellen aus; die gesamte notwendige Logik ist gebündelt.",{"category":65,"check":75,"severity":24,"summary":76},"Sandbox-Isolation","Die Fähigkeit führt Berechnungen durch und interagiert scheinbar nicht mit oder modifiziert Dateien außerhalb ihres vorgesehenen Umfangs.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox-Escape-Primitive","Es gibt keine Anzeichen für das Starten getrennter Prozesse oder Retry-Schleifen im bereitgestellten Code.",{"category":65,"check":81,"severity":24,"summary":82},"Datenexfiltration","Die Fähigkeit führt Berechnungen durch und übermittelt keine Benutzerdaten oder vertraulichen Informationen an Dritte.",{"category":65,"check":84,"severity":24,"summary":85},"Versteckte Texttricks","Die gebündelten Inhalte und Beschreibungen scheinen frei von versteckten Steuerungs-Tricks, unsichtbaren Unicode-Zeichen oder anderen Verschleierungsmethoden zu sein.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque Codeausführung","Die Logik der Fähigkeit ist in lesbarem TypeScript implementiert und beinhaltet keine verschleierten Codes, Base64-Payloads oder Laufzeit-Skriptabrufe.",{"category":91,"check":92,"severity":24,"summary":93},"Portabilität","Strukturelle Annahme","Die Fähigkeit führt In-Memory-Berechnungen durch und trifft keine Annahmen über die Projektdateistruktur des Benutzers.",{"category":95,"check":96,"severity":24,"summary":97},"Vertrauen","Aufmerksamkeit für Issues","Das Projekt hat 0 offene Issues und 44 geschlossene Issues in den letzten 90 Tagen, was auf eine hohe Abschlussrate und aktive Wartung hindeutet.",{"category":99,"check":100,"severity":24,"summary":101},"Versionierung","Release-Management","Die Fähigkeit hat eine `version: 1.0.0` in ihrem Frontmatter und der `pushedAt`-Zeitstempel zeigt kürzliche Aktivität, was ein klares Versionssignal setzt.",{"category":103,"check":104,"severity":24,"summary":105},"Ausführung","Validierung","Das Eingabeschema für `simulate_montecarlo` ist klar definiert und die erwartete Ausgabestruktur ist dokumentiert, was eine ordnungsgemäße Validierung impliziert.",{"category":65,"check":107,"severity":24,"summary":108},"Ungeschützte destruktive Operationen","Die Fähigkeit ist rein analytisch und führt keine destruktiven Operationen durch.",{"category":110,"check":111,"severity":24,"summary":112},"Code-Ausführung","Fehlerbehandlung","Obwohl spezifische Fehlerbehandlungen nicht detailliert sind, lässt die Art der Fähigkeit als Berechnungs-Engine vermuten, dass Standard-Fehlerberichterstattung zutrifft, und die klare Werkzeugdefinition impliziert eine strukturierte Fehlerbehandlung.",{"category":110,"check":114,"severity":115,"summary":116},"Protokollierung","not_applicable","Die Fähigkeit ist analytisch und führt keine destruktiven Aktionen oder ausgehenden Aufrufe durch, die eine lokale Audit-Protokollierung erfordern würden.",{"category":118,"check":119,"severity":24,"summary":120},"Compliance","DSGVO","Die Fähigkeit führt mathematische Simulationen durch und verarbeitet keine personenbezogenen Daten.",{"category":118,"check":122,"severity":24,"summary":123},"Zielmarkt","Die Funktionalität der Fähigkeit ist mathematisch und nicht an eine bestimmte geografische oder rechtliche Gerichtsbarkeit gebunden, was sie global anwendbar macht.",{"category":91,"check":125,"severity":24,"summary":126},"Laufzeitstabilität","Die Fähigkeit basiert auf Standard-TypeScript/JavaScript und mathematischen Algorithmen, ohne offensichtliche Annahmen über spezifische Betriebssysteme oder Shells.",{"category":44,"check":128,"severity":24,"summary":129},"README","Das README ist umfassend, beschreibt den Zweck der Erweiterung, enthält Installationsanweisungen und hebt wichtige Funktionen und Implementierungen hervor.",{"category":33,"check":131,"severity":24,"summary":132},"Größe der Werkzeugoberfläche","Die Erweiterung stellt ein einziges Werkzeug, `simulate_montecarlo`, bereit, was für ihre fokussierte Funktionalität angemessen ist.",{"category":40,"check":134,"severity":24,"summary":135},"Überlappende fast-Synonym-Werkzeuge","Die Erweiterung stellt nur ein Werkzeug bereit, daher gibt es keine überlappenden fast-Synonym-Werkzeuge.",{"category":44,"check":137,"severity":24,"summary":138},"Phantom-Funktionen","Alle beworbenen Funktionen, einschließlich des Werkzeugs `simulate_montecarlo`, haben klare Implementierungen, die in der SKILL.md und im README dokumentiert sind.",{"category":140,"check":141,"severity":24,"summary":142},"Installation","Installationsanleitung","Das README bietet klare Installationsanweisungen für den MCP-Server, die REST-API und das npm-SDK sowie Anwendungsbeispiele.",{"category":144,"check":145,"severity":24,"summary":146},"Fehler","Handhabbare Fehlermeldungen","Obwohl spezifische Beispiele für Fehlermeldungen nicht angegeben sind, impliziert die strukturierte Werkzeugdefinition und die API-Dokumentation, dass Fehler handhabbar wären.",{"category":103,"check":148,"severity":24,"summary":149},"Angeheftete Abhängigkeiten","Das Projekt verwendet npm und listet SDK-Pakete auf, was auf ein Abhängigkeitsmanagement mit Lockfiles hindeutet, und die SKILL.md-Frontmatter spezifiziert erforderliche Umgebungsvariablen.",{"category":33,"check":151,"severity":115,"summary":152},"Dry-Run-Vorschau","Die Fähigkeit ist analytisch und führt keine zustandsändernden Operationen durch oder sendet Daten nach außen, wodurch eine Dry-Run-Vorschau nicht zutreffend ist.",{"category":154,"check":155,"severity":24,"summary":156},"Protokoll","Idempotente Wiederholungsversuche & Timeouts","Die Fähigkeit führt In-Memory-Berechnungen durch und beinhaltet keine Remote-Aufrufe oder zustandsändernden Operationen, daher sind Idempotenz und Timeouts im herkömmlichen Sinne nicht anwendbar.",{"category":118,"check":158,"severity":24,"summary":159},"Telemetrie-Opt-in","Es gibt keine Erwähnung von Telemetrie, die von dieser Fähigkeit gesammelt wird; jegliche potenzielle Telemetrie würde vom breiteren MCP-Framework gehandhabt werden, und diese Fähigkeit selbst sendet keine Telemetrie.",{"category":40,"check":161,"severity":24,"summary":162},"Präziser Zweck","Der Zweck der Fähigkeit ist präzise als Monte-Carlo-Simulation für KI-Agenten zur Modellierung von Unsicherheiten und Quantifizierung von Risiken definiert, mit klaren Anwendungsfällen und einem spezifischen Werkzeug.",{"category":40,"check":164,"severity":24,"summary":165},"Prägnante Frontmatter","Die SKILL.md-Frontmatter ist prägnant und fasst die Kernfähigkeit der Monte-Carlo-Simulation für KI-Agenten effektiv zusammen.",{"category":44,"check":167,"severity":24,"summary":168},"Prägnanter Body","Der SKILL.md-Body ist prägnant, beschreibt das Werkzeug und die Regeln und verweist für tiefere Materialien wie Beispiele und Preise auf klare Abschnitte.",{"category":170,"check":171,"severity":24,"summary":172},"Kontext","Progressive Offenlegung","Die SKILL.md bietet einen prägnanten Überblick und verlinkt zu einem klaren Beispiel, das den Prinzipien der progressiven Offenlegung entspricht.",{"category":170,"check":174,"severity":115,"summary":175},"Forked Exploration","Diese Fähigkeit ist ein fokussiertes Berechenwerkzeug und beinhaltet keine tiefgehende Erkundung oder Code-Überprüfung, was `context: fork` nicht zutreffend macht.",{"category":22,"check":177,"severity":24,"summary":178},"Anwendungsbeispiele","Ein klares, sofort verwendbares JSON-Beispiel für eine Umsatzprognose wird bereitgestellt, das Eingabe, Aufruf und die erwartete Ausgabestruktur demonstriert.",{"category":22,"check":180,"severity":24,"summary":181},"Randfälle","Der Regelabschnitt behandelt Randfälle implizit, indem er spezifische Verteilungen für bestimmte Datentypen empfiehlt (z. B. Lognormal für positive Werte) und minimale Iterationen für die Zuverlässigkeit angibt.",{"category":110,"check":183,"severity":115,"summary":184},"Werkzeug-Fallback","Diese Fähigkeit scheint nicht von einem externen MCP-Server abzuhängen; sie funktioniert als eigenständiges Berechnungs werkzeug.",{"category":65,"check":186,"severity":24,"summary":187},"Halt bei unerwartetem Zustand","Als Berechnungs werkzeug ist es unwahrscheinlich, dass es unerwartete Umgebungszustände antrifft, die ein Anhalten erfordern; die Eingabevalidierung wäre der primäre Mechanismus zur Behandlung unerwarteter Vorzustände.",{"category":91,"check":189,"severity":24,"summary":190},"Skill-übergreifende Kopplung","Die Fähigkeit ist in sich geschlossen und auf Monte-Carlo-Simulationen fokussiert, ohne implizite Abhängigkeit von anderen Fähigkeiten.",1778699039806,"Diese Fähigkeit ermöglicht es KI-Agenten, Monte-Carlo-Simulationen durchzuführen, die 6 Verteilungstypen zur Modellierung von Risiken, zur Umsatzprognose, zur Schätzung von Projektzeitplänen und zur Quantifizierung von Unsicherheiten unterstützen.",[194,195,196,197,198],"Führen Sie Tausende von probabilistischen Szenarien aus","Modellieren Sie Risiken und prognostizieren Sie Umsätze","Schätzen Sie Projektzeitpläne","Quantifizieren Sie Unsicherheiten","Unterstützung für 6 Verteilungstypen",[200,201,202],"Bereitstellung von Echtzeit-Handelsausführungen","Durchführung deterministischer Finanzberechnungen ohne probabilistische Eingaben","Ersetzung von Kern-LLM-Argumentationsfähigkeiten","3.0.0","4.4.0","KI-Agenten mit mathematisch fundierten Monte-Carlo-Simulationsfähigkeiten für quantitative Analysen, Risikomodellierung und Prognosen zur Verfügung zu stellen.","Hohe Bewertung aufgrund exzellenter Dokumentation, klarer Problemlösung und starker Produktionsreife. Keine Warnungen oder kritischen Fehler gefunden.",98,"Eine qualitativ hochwertige Fähigkeit für Monte-Carlo-Simulationen, die robuste quantitative Analysen für KI-Agenten bietet.",[210,211,212,213,214,215],"monte-carlo","simulation","risk-analysis","forecasting","probability","finance","verified",[218,219,220,221],"Schätzen Sie die Wahrscheinlichkeit, ein Umsatzziel zu erreichen","Modellieren Sie Projektzeitpläne mit Unsicherheiten","Berechnen Sie Value at Risk für ein Portfolio","Führen Sie Sensitivitätsanalysen für Geschäftsannahmen durch",{"codeQuality":223,"collectedAt":225,"documentation":226,"maintenance":229,"security":235,"testCoverage":238},{"hasLockfile":224},true,1778699023744,{"descriptionLength":227,"readmeSize":228},196,9472,{"closedIssues90d":230,"forks":231,"hasChangelog":224,"manifestVersion":232,"openIssues90d":8,"pushedAt":233,"stars":234},44,2,"1.0.0",1777714123000,8,{"hasNpmPackage":236,"license":237,"smitheryVerified":236},false,"MIT",{"hasCi":224,"hasTests":224},{"updatedAt":240},1778699169481,{"basePath":242,"githubOwner":243,"githubRepo":244,"locale":18,"slug":13,"type":245},"mission-control/packages/clawhub-skills/oraclaw-simulate","Whatsonyourmind","oraclaw","skill",null,{"evaluate":248,"extract":251},{"promptVersionExtension":203,"promptVersionScoring":204,"score":207,"tags":249,"targetMarket":250,"tier":216},[210,211,212,213,214,215],"global",{"commitSha":252},"HEAD",{"repoId":254,"translatedFrom":255},"kd76fmxm1ng903s4fmj0p7hxxs86ndkg","k1706ed747sfrsfzvmdj4fzckh86nnw3",{"_creationTime":257,"_id":254,"identity":258,"providers":259,"workflow":418},1778698831609.0093,{"githubOwner":243,"githubRepo":244,"sourceUrl":14},{"classify":260,"discover":393,"github":396},{"commitSha":252,"extensions":261},[262,274,282,290,298,306,314,322,330,338,346,354,362,368,376],{"basePath":263,"description":264,"displayName":265,"installMethods":266,"rationale":267,"selectedPaths":268,"source":272,"sourceLanguage":273,"type":245},"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","en",{"basePath":275,"description":276,"displayName":277,"installMethods":278,"rationale":279,"selectedPaths":280,"source":272,"sourceLanguage":273,"type":245},"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",[281],{"path":270,"priority":271},{"basePath":283,"description":284,"displayName":285,"installMethods":286,"rationale":287,"selectedPaths":288,"source":272,"sourceLanguage":273,"type":245},"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",[289],{"path":270,"priority":271},{"basePath":291,"description":292,"displayName":293,"installMethods":294,"rationale":295,"selectedPaths":296,"source":272,"sourceLanguage":273,"type":245},"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",[297],{"path":270,"priority":271},{"basePath":299,"description":300,"displayName":301,"installMethods":302,"rationale":303,"selectedPaths":304,"source":272,"sourceLanguage":273,"type":245},"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",[305],{"path":270,"priority":271},{"basePath":307,"description":308,"displayName":309,"installMethods":310,"rationale":311,"selectedPaths":312,"source":272,"sourceLanguage":273,"type":245},"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",[313],{"path":270,"priority":271},{"basePath":315,"description":316,"displayName":317,"installMethods":318,"rationale":319,"selectedPaths":320,"source":272,"sourceLanguage":273,"type":245},"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",[321],{"path":270,"priority":271},{"basePath":323,"description":324,"displayName":325,"installMethods":326,"rationale":327,"selectedPaths":328,"source":272,"sourceLanguage":273,"type":245},"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",[329],{"path":270,"priority":271},{"basePath":331,"description":332,"displayName":333,"installMethods":334,"rationale":335,"selectedPaths":336,"source":272,"sourceLanguage":273,"type":245},"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",[337],{"path":270,"priority":271},{"basePath":339,"description":340,"displayName":341,"installMethods":342,"rationale":343,"selectedPaths":344,"source":272,"sourceLanguage":273,"type":245},"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",[345],{"path":270,"priority":271},{"basePath":347,"description":348,"displayName":349,"installMethods":350,"rationale":351,"selectedPaths":352,"source":272,"sourceLanguage":273,"type":245},"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",[353],{"path":270,"priority":271},{"basePath":355,"description":356,"displayName":357,"installMethods":358,"rationale":359,"selectedPaths":360,"source":272,"sourceLanguage":273,"type":245},"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",[361],{"path":270,"priority":271},{"basePath":242,"description":363,"displayName":13,"installMethods":364,"rationale":365,"selectedPaths":366,"source":272,"sourceLanguage":273,"type":245},"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},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-simulate/SKILL.md",[367],{"path":270,"priority":271},{"basePath":369,"description":370,"displayName":371,"installMethods":372,"rationale":373,"selectedPaths":374,"source":272,"sourceLanguage":273,"type":245},"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",[375],{"path":270,"priority":271},{"basePath":377,"description":378,"displayName":379,"installMethods":380,"license":237,"rationale":381,"selectedPaths":382,"source":272,"sourceLanguage":273,"type":392},"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":379},"server.json with namespace/server name at mission-control/packages/mcp-server/server.json",[383,385,387,389],{"path":384,"priority":271},"server.json",{"path":386,"priority":271},"package.json",{"path":388,"priority":271},"README.md",{"path":390,"priority":391},"src/index.ts","low","mcp",{"sources":394},[395],"manual",{"closedIssues90d":230,"description":397,"forks":231,"homepage":398,"license":237,"openIssues90d":8,"pushedAt":233,"readmeSize":228,"stars":234,"topics":399},"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",[400,401,402,403,404,405,406,392,407,408,409,410,411,412,413,414,415,416,210,417],"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":419,"discoverAt":420,"extractAt":421,"githubAt":421,"updatedAt":419},1778698837409,1778698831609,1778698835357,[215,213,210,214,212,211],{"evaluatedAt":424,"extractAt":425,"updatedAt":240},1778699039921,1778698837670,[],[428,458,488,518,541,571],{"_creationTime":429,"_id":430,"community":431,"display":432,"identity":438,"providers":442,"relations":452,"tags":454,"workflow":455},1778688112811.7527,"k17enr6rktmxh0enswrmze6et186mq12",{"reviewCount":8},{"description":433,"installMethods":434,"name":436,"sourceUrl":437},"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":435},"guia-matthieu/clawfu-skills","forecast-scenarios","https://github.com/guia-matthieu/clawfu-skills",{"basePath":439,"githubOwner":440,"githubRepo":441,"locale":273,"slug":436,"type":245},"skills/revops/forecast-scenarios","guia-matthieu","clawfu-skills",{"evaluate":443,"extract":451},{"promptVersionExtension":203,"promptVersionScoring":204,"score":444,"tags":445,"targetMarket":250,"tier":216},100,[215,213,446,447,448,449,450],"revenue","planning","strategy","sensitivity-analysis","mckinsey",{"commitSha":252},{"repoId":453},"kd72qvzyvm658ya7pbyh5ey47h86md53",[215,213,450,447,446,449,448],{"evaluatedAt":456,"extractAt":457,"updatedAt":456},1778690475880,1778688112811,{"_creationTime":459,"_id":460,"community":461,"display":462,"identity":468,"providers":473,"relations":481,"tags":484,"workflow":485},1778675056600.2537,"k17ask0fam6yfypdvf5562p15986m925",{"reviewCount":8},{"description":463,"installMethods":464,"name":466,"sourceUrl":467},"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":465},"alirezarezvani/claude-skills","Financial Analyst","https://github.com/alirezarezvani/claude-skills",{"basePath":469,"githubOwner":470,"githubRepo":471,"locale":273,"slug":472,"type":245},"finance/skills/financial-analyst","alirezarezvani","claude-skills","financial-analyst",{"evaluate":474,"extract":480},{"promptVersionExtension":203,"promptVersionScoring":204,"score":444,"tags":475,"targetMarket":250,"tier":216},[215,476,477,213,478,479],"analysis","valuation","budgeting","python",{"commitSha":252,"license":237},{"parentExtensionId":482,"repoId":483},"k174nmf7jahgcsdnzenmdxfcbh86m85y","kd7ff9s1w43mfyy1n7hf87816186m6px",[476,478,215,213,479,477],{"evaluatedAt":486,"extractAt":487,"updatedAt":486},1778683964036,1778675056600,{"_creationTime":489,"_id":490,"community":491,"display":492,"identity":498,"providers":502,"relations":511,"tags":514,"workflow":515},1778695548458.402,"k179k5vddwcqrrr23r6hfavx5n86mf81",{"reviewCount":8},{"description":493,"installMethods":494,"name":496,"sourceUrl":497},"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":495},"pjt222/agent-almanac","simulate-stochastic-process","https://github.com/pjt222/agent-almanac",{"basePath":499,"githubOwner":500,"githubRepo":501,"locale":273,"slug":496,"type":245},"skills/simulate-stochastic-process","pjt222","agent-almanac",{"evaluate":503,"extract":510},{"promptVersionExtension":203,"promptVersionScoring":204,"score":504,"tags":505,"targetMarket":250,"tier":216},97,[506,211,507,210,508,509],"stochastic-processes","mcmc","statistics","numerical-methods",{"commitSha":252},{"parentExtensionId":512,"repoId":513},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[507,210,509,211,508,506],{"evaluatedAt":516,"extractAt":517,"updatedAt":516},1778701677011,1778695548458,{"_creationTime":519,"_id":520,"community":521,"display":522,"identity":526,"providers":529,"relations":537,"tags":538,"workflow":539},1778695548458.3777,"k1720hbr4h69f4xs06yz2j8cd586mfam",{"reviewCount":8},{"description":523,"installMethods":524,"name":525,"sourceUrl":497},"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":495},"Model Markov Chain",{"basePath":527,"githubOwner":500,"githubRepo":501,"locale":273,"slug":528,"type":245},"skills/model-markov-chain","model-markov-chain",{"evaluate":530,"extract":536},{"promptVersionExtension":203,"promptVersionScoring":204,"score":504,"tags":531,"targetMarket":250,"tier":216},[532,533,534,535,214,211],"stochastic","markov-chain","transition-matrix","stationary-distribution",{"commitSha":252,"license":237},{"parentExtensionId":512,"repoId":513},[533,214,211,535,532,534],{"evaluatedAt":540,"extractAt":517,"updatedAt":540},1778699521772,{"_creationTime":542,"_id":543,"community":544,"display":545,"identity":551,"providers":556,"relations":564,"tags":567,"workflow":568},1778696691708.3274,"k170az7r02e9e2v47mpy80kx6n86nff3",{"reviewCount":8},{"description":546,"installMethods":547,"name":549,"sourceUrl":550},"Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy",{"claudeCode":548},"ruvnet/ruflo","Trader Regime","https://github.com/ruvnet/ruflo",{"basePath":552,"githubOwner":553,"githubRepo":554,"locale":273,"slug":555,"type":245},"plugins/ruflo-neural-trader/skills/trader-regime","ruvnet","ruflo","trader-regime",{"evaluate":557,"extract":563},{"promptVersionExtension":203,"promptVersionScoring":204,"score":444,"tags":558,"targetMarket":250,"tier":216},[215,559,560,561,408,562],"trading","market-analysis","ai","cli",{"commitSha":252,"license":237},{"parentExtensionId":565,"repoId":566},"k17drge8h1fgzchr0p4jaeg33n86mwmy","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[561,562,215,560,559,408],{"evaluatedAt":569,"extractAt":570,"updatedAt":569},1778701108877,1778696691708,{"_creationTime":572,"_id":573,"community":574,"display":575,"identity":581,"providers":585,"relations":590,"tags":594,"workflow":595},1778693819124.3687,"k177re651qqdxa2pxznqy4qzx186mgmm",{"reviewCount":8},{"description":576,"installMethods":577,"name":579,"sourceUrl":580},"Domänenwissen für die KI-Trading-Erinnerung – Outcome-Weighted Memory (OWM)-Architektur, 5 Speichertypen, Abrufbewertung und Verhaltensanalyse. Verwenden Sie dies beim Aufzeichnen von Trades, beim Abrufen ähnlicher Kontexte, bei der Leistungsanalyse oder bei der Überprüfung von Verhaltensabweichungen. Löst bei \"record trade\", \"remember trade\", \"recall\", \"similar trades\", \"performance\", \"behavioral\", \"disposition\", \"affective state\", \"confidence\" aus.",{"claudeCode":578},"mnemox-ai/tradememory-protocol","trading-memory","https://github.com/mnemox-ai/tradememory-protocol",{"basePath":582,"githubOwner":583,"githubRepo":584,"locale":18,"slug":579,"type":245},"tradememory-plugin/skills/trading-memory","mnemox-ai","tradememory-protocol",{"evaluate":586,"extract":589},{"promptVersionExtension":203,"promptVersionScoring":204,"score":444,"tags":587,"targetMarket":250,"tier":216},[559,561,588,215,479],"memory",{"commitSha":252},{"parentExtensionId":591,"repoId":592,"translatedFrom":593},"k170vxkqee48k2xq1v55a025nh86nzn7","kd73z11kfekksxyrs8ds0snacs86ncdy","k173a67a16bpq0e29wjd85v71986nx03",[561,215,588,479,559],{"evaluatedAt":596,"extractAt":597,"updatedAt":598},1778693719816,1778693539593,1778693819124]