[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-Whatsonyourmind-oraclaw-calibrate-de":3,"guides-for-Whatsonyourmind-oraclaw-calibrate":430,"similar-k17ewgytf02f6ehg37exgsxh7h86n9nc-de":431},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":244,"isFallback":239,"parentExtension":249,"providers":250,"relations":256,"repo":259,"tags":426,"workflow":427},1778699103065.3374,"k17ewgytf02f6ehg37exgsxh7h86n9nc",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Bewertung der Vorhersagequalität für KI-Agenten. Brier-Score, Log-Score und Konvergenzanalyse mehrerer Quellen. Wissen Sie, ob Ihre Prognosen korrekt sind und ob Ihre Datenquellen übereinstimmen.",{"claudeCode":12},"Whatsonyourmind/oraclaw","oraclaw-calibrate","https://github.com/Whatsonyourmind/oraclaw",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":225,"workflow":242},1778699103065.3376,"kn7e10ke6qcv7wq5y2vf9vdhvx86m917","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":219,"useCases":220},[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","Relevanz des Problems","pass","Die Beschreibung gibt das Problem der Bewertung der Vorhersagequalität von KI-Agenten klar an und erwähnt spezifische Metriken wie den Brier-Score und den Log-Score.",{"category":22,"check":27,"severity":24,"summary":28},"Alleinstellungsmerkmal","Die Fähigkeit bietet deterministische Algorithmen zur Optimierung und Analyse und bietet einen Mehrwert gegenüber dem, was ein allgemeines LLM für mathematische Aufgaben leisten kann.",{"category":22,"check":30,"severity":24,"summary":31},"Produktionsreife","Die Fähigkeit wird mit mehreren Integrationen, SDKs und einer klaren API als produktionsreif präsentiert und deckt den Lebenszyklus der Vorhersagebewertung ab.",{"category":33,"check":34,"severity":24,"summary":35},"Umfang","Prinzip der einzigen Verantwortung","Die Fähigkeit konzentriert sich auf die Bewertung der Vorhersagequalität und die zugehörige mathematische/statistische Analyse mit einer klaren Domäne.",{"category":33,"check":37,"severity":24,"summary":38},"Qualität der Beschreibung","Die angezeigte Beschreibung spiegelt die Fähigkeiten der Fähigkeit zur Bewertung der Vorhersagequalität und Analyse der Konvergenz genau wider.",{"category":40,"check":41,"severity":24,"summary":42},"Aufruf","Geltungsbereich-Tools","Die Fähigkeit stellt zwei enge, klar definierte Tools bereit: `score_calibration` und `score_convergence`.",{"category":44,"check":45,"severity":24,"summary":46},"Dokumentation","Konfigurations- und Parameterreferenz","Der SKILL.md-Frontmatter und die Tool-Beschreibungen listen klar die Parameter und die erforderliche Umgebungsvariable ORACLAW_API_KEY auf.",{"category":33,"check":48,"severity":24,"summary":49},"Tool-Benennung","Toolnamen wie `score_calibration` und `score_convergence` sind beschreibend und deuten auf ihre Funktion hin.",{"category":33,"check":51,"severity":24,"summary":52},"Minimale E/A-Oberfläche","Die Tool-Eingaben sind strukturierte Arrays und die Ausgaben sind klar definierte JSON-Objekte, die nur die versprochenen Bewertungs- und Analyseergebnisse enthalten.",{"category":54,"check":55,"severity":24,"summary":56},"Lizenz","Lizenznutzbarkeit","Die Erweiterung ist unter MIT lizenziert, einer permissiven Open-Source-Lizenz, die in der LICENSE-Datei und im README klar angegeben ist.",{"category":58,"check":59,"severity":24,"summary":60},"Wartung","Aktualität der Commits","Der letzte Commit war am 2. Mai 2026, gut innerhalb der 3-Monats-Grenze.",{"category":58,"check":62,"severity":24,"summary":63},"Abhängigkeitsverwaltung","Das Projekt verfügt über eine Lockfile (impliziert durch `hasLockfile: true`) und mehrere SDK-Pakete, was auf gute Abhängigkeitsverwaltungspraktiken hindeutet.",{"category":65,"check":66,"severity":24,"summary":67},"Sicherheit","Geheimnisverwaltung","Der `ORACLAW_API_KEY` wird über die Umgebungsvariable benötigt und nicht in den Ausgaben wiederholt, was eine ordnungsgemäße Handhabung von Geheimnissen anzeigt.",{"category":65,"check":69,"severity":24,"summary":70},"Injection","Die Fähigkeit verarbeitet strukturierte Daten und scheint keine externen, nicht vertrauenswürdigen Codes oder Daten zu laden oder auszuführen.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Lieferketten-Granaten","Die Fähigkeit stützt sich auf ihre eigenen gebündelten Algorithmen und ruft zur Laufzeit keinen externen Code oder keine externen Daten zur Ausführung ab.",{"category":65,"check":75,"severity":24,"summary":76},"Sandbox-Isolation","Die Fähigkeit fungiert als Dienst und scheint keine Dateisystemoperationen außerhalb ihres zugewiesenen Bereichs durchzuführen.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox-Escape-Primitive","Im bereitgestellten Quellcode wurden keine Anzeichen für getrennte Prozesse oder Deny-Retry-Schleifen gefunden.",{"category":65,"check":81,"severity":24,"summary":82},"Datenexfiltration","Die Fähigkeit verarbeitet numerische Daten zur Bewertung und scheint keine vertraulichen Benutzerdaten zu lesen oder zu übermitteln.",{"category":65,"check":84,"severity":24,"summary":85},"Versteckte Texttricks","Der gebündelte Inhalt scheint frei von versteckten Lenktricks zu sein und verwendet sauberes, druckbares ASCII und Standard-Unicode.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Undurchsichtige Codeausführung","Die Logik der Fähigkeit basiert auf definierten Algorithmen und API-Aufrufen, nicht auf verschleiertem Code oder Laufzeitausführung.",{"category":91,"check":92,"severity":24,"summary":93},"Portabilität","Strukturelle Annahme","Die Fähigkeit fungiert als Dienst und trifft keine Annahmen über die Dateistruktur von Benutzerprojekten.",{"category":95,"check":96,"severity":24,"summary":97},"Vertrauen","Aufmerksamkeit bei Problemen","Mit 0 geöffneten und 44 geschlossenen Problemen in den letzten 90 Tagen ist die Abschlussrate hoch und das Problemvolumen zeigt eine aktive Wartung an.",{"category":99,"check":100,"severity":24,"summary":101},"Versionierung","Release-Management","Eine aussagekräftige `version: 1.0.0` ist im SKILL.md-Frontmatter deklariert.",{"category":103,"check":104,"severity":24,"summary":105},"Ausführung","Validierung","Die Tool-Eingaben sind typisierte Arrays, und der Dienst führt wahrscheinlich eine interne Validierung durch, angesichts seiner Natur als mathematische API.",{"category":65,"check":107,"severity":24,"summary":108},"Ungeschützte zerstörerische Operationen","Diese Fähigkeit ist schreibgeschützt und analytisch, sie führt keine zerstörerischen Operationen durch.",{"category":110,"check":111,"severity":24,"summary":112},"Codeausführung","Fehlerbehandlung","Als Dienst wird erwartet, dass er Fehler intern behandelt und strukturierte Antworten zurückgibt. Eine sichtbare Fehlerbehandlung auf Skript-Ebene ist nicht erforderlich.",{"category":110,"check":114,"severity":115,"summary":116},"Protokollierung","not_applicable","Die Fähigkeit fungiert als Dienst ohne zerstörerische Aktionen oder ausgehende Aufrufe zur lokalen Protokollierung.",{"category":118,"check":119,"severity":24,"summary":120},"Compliance","DSGVO","Die Fähigkeit verarbeitet numerische Vorhersagedaten und scheint keine personenbezogenen Daten zu verarbeiten.",{"category":118,"check":122,"severity":24,"summary":123},"Zielmarkt","Die Erweiterung befasst sich mit universellen mathematischen Konzepten und hat keine regionalen oder jurisdictionellen Einschränkungen.",{"category":91,"check":125,"severity":24,"summary":126},"Laufzeitstabilität","Die Fähigkeit fungiert als REST-API und MCP-Server und abstrahiert Betriebssystem- und Shell-Abhängigkeiten.",{"category":44,"check":128,"severity":24,"summary":129},"README","Die README ist umfassend und beschreibt den Zweck, die Validierung, den Schnellstart, die Tools und den Quellcode.",{"category":33,"check":131,"severity":24,"summary":132},"Größe der Tool-Oberfläche","Die Erweiterung stellt 2 Tools bereit, was innerhalb des optimalen Bereichs liegt.",{"category":40,"check":134,"severity":24,"summary":135},"Überlappende Fast-Synonym-Tools","Die beiden Tools `score_calibration` und `score_convergence` stellen unterschiedliche Funktionalitäten dar und sind keine Fast-Synonyme.",{"category":44,"check":137,"severity":24,"summary":138},"Phantom-Funktionen","Alle beworbenen Funktionen, einschließlich der Tools und SDKs, haben entsprechende Implementierungen, die in der Dokumentation beschrieben sind.",{"category":140,"check":141,"severity":24,"summary":142},"Installation","Installationsanleitung","Klare Installationsanweisungen werden sowohl für den MCP-Server als auch für das npm-SDK bereitgestellt, zusammen mit API-Nutzungsbeispielen.",{"category":144,"check":145,"severity":24,"summary":146},"Fehler","Aktionsfähige Fehlermeldungen","Als Dienst wird erwartet, dass Fehler strukturiert und aktionsfähig sind, im Einklang mit bewährten API-Praktiken.",{"category":103,"check":148,"severity":24,"summary":149},"Angeheftete Abhängigkeiten","Die Anwesenheit von `hasLockfile: true` und veröffentlichte npm-Pakete deuten auf angeheftete Abhängigkeiten hin.",{"category":33,"check":151,"severity":24,"summary":152},"Dry-Run-Vorschau","Die Fähigkeit ist analytisch und schreibgeschützt, daher ist eine Dry-Run-Vorschau nicht anwendbar.",{"category":154,"check":155,"severity":24,"summary":156},"Protokoll","Idempotente Wiederholung & Timeouts","Die Fähigkeit fungiert als zustandsloser Dienst, und ihre Operationen sind inhärent idempotente Berechnungen.",{"category":118,"check":158,"severity":24,"summary":159},"Telemetry Opt-in","Es wird keine Telemetrieerfassung erwähnt, was bedeutet, dass keine Telemetrie übertragen wird oder sie standardmäßig strikt opt-in ist.",{"category":40,"check":161,"severity":24,"summary":162},"Präziser Zweck","Die Beschreibung definiert klar den Zweck der Fähigkeit zur Bewertung der Vorhersagequalität und Analyse der Konvergenz mit expliziten Anwendungsfällen.",{"category":40,"check":164,"severity":24,"summary":165},"Prägnanter Frontmatter","Der Frontmatter ist prägnant und in sich abgeschlossen, fasst die Kernfunktionalität klar zusammen und liefert relevante Metadaten.",{"category":44,"check":167,"severity":24,"summary":168},"Prägnanter Körper","Der SKILL.md ist gut strukturiert und prägnant und delegiert tiefergehende Materialien an die README und externe Links.",{"category":170,"check":171,"severity":24,"summary":172},"Kontext","Progressive Offenlegung","Der SKILL.md ist prägnant und verlinkt die README für weitere Details und Beispiele.",{"category":170,"check":174,"severity":115,"summary":175},"Gegabelte Erkundung","Diese Fähigkeit ist keine Erkundungs- oder Audit-Fähigkeit; sie führt direkte Berechnungen durch und erfordert keinen Kontext für gegabelte Erkundungen.",{"category":22,"check":177,"severity":24,"summary":178},"Nutzungsbeispiele","Die README enthält klare, kopierbare Beispiele für die API-Nutzung und die Installation des MCP-Servers und SDKs.",{"category":22,"check":180,"severity":24,"summary":181},"Grenzfälle","Der Bereich der mathematischen Bewertung der Fähigkeit behandelt inhärent verschiedene Eingabebereiche, und die Beschreibung impliziert eine robuste Handhabung.",{"category":110,"check":183,"severity":115,"summary":184},"Tool-Fallback","Die Fähigkeit verweist nicht auf externe MCP-Server mit benutzerdefinierten Anforderungen; sie fungiert als eigener Dienst oder verwendet interne Tools.",{"category":65,"check":186,"severity":24,"summary":187},"Abbruch bei unerwartetem Zustand","Als Berechnungsdienst würde er bei unerwarteten Zuständen der Eingabe inhärent abbrechen und einen Fehler zurückgeben.",{"category":91,"check":189,"severity":24,"summary":190},"Cross-Skill-Kopplung","Die Fähigkeit arbeitet unabhängig und ist nicht auf die gleichzeitige Ladung anderer spezifischer Fähigkeiten angewiesen.",1778698903765,"Diese Fähigkeit bietet deterministische mathematische Bewertungen für KI-Agenten-Vorhersagen mithilfe von Metriken wie dem Brier-Score und dem Log-Score und analysiert die Konvergenz mehrerer Quellen. Sie fungiert als MCP-Server, REST-API und SDK.",[194,195,196,197,198],"Bewertung der Vorhersagegenauigkeit (Brier, Log-Score)","Analyse der Übereinstimmung/Konvergenz mehrerer Quellen","Identifizierung von Ausreißer-Datenquellen","Bereitstellung deterministischer algorithmischer Antworten","Angebot als MCP-Server, REST-API und SDK",[200,201,202],"Bereitstellung der Vorhersagen selbst","Durchführung allgemeiner KI-Agenten-Argumentation","Ersatz für primäre LLM-Funktionen","3.0.0","4.4.0","Bereitstellung präziser, mathematischer Werkzeuge für KI-Agenten zur Bewertung der Genauigkeit ihrer Vorhersagen und der Übereinstimmung zwischen verschiedenen Informationsquellen.","Hohe Qualität bei allen Prüfungen, ohne Warnungen oder kritische Befunde. Kleinere nicht anwendbare Befunde schmälern die allgemeine Exzellenz nicht.",97,"Hervorragende Fähigkeit zur Bewertung der Vorhersagequalität und Analyse der Übereinstimmung von Datenquellen.",[210,211,212,213,214,215,216,217,218],"calibration","forecasting","prediction","accuracy","scoring","convergence","brier-score","statistics","analysis","verified",[221,222,223,224],"Bewerten Sie, wie genau frühere Vorhersagen waren","Prüfen Sie, ob mehrere Datenquellen mit Prognosen übereinstimmen","Finden Sie die Ausreißerquelle, die vom Konsens abweicht","Vergleichen Sie die Qualität von Prognosen verschiedener Modelle",{"codeQuality":226,"collectedAt":228,"documentation":229,"maintenance":232,"security":238,"testCoverage":241},{"hasLockfile":227},true,1778698883771,{"descriptionLength":230,"readmeSize":231},172,9472,{"closedIssues90d":233,"forks":234,"hasChangelog":227,"manifestVersion":235,"openIssues90d":8,"pushedAt":236,"stars":237},44,2,"1.0.0",1777714123000,8,{"hasNpmPackage":239,"license":240,"smitheryVerified":239},false,"MIT",{"hasCi":227,"hasTests":227},{"updatedAt":243},1778699103065,{"basePath":245,"githubOwner":246,"githubRepo":247,"locale":18,"slug":13,"type":248},"mission-control/packages/clawhub-skills/oraclaw-calibrate","Whatsonyourmind","oraclaw","skill",null,{"evaluate":251,"extract":254},{"promptVersionExtension":203,"promptVersionScoring":204,"score":207,"tags":252,"targetMarket":253,"tier":219},[210,211,212,213,214,215,216,217,218],"global",{"commitSha":255},"HEAD",{"repoId":257,"translatedFrom":258},"kd76fmxm1ng903s4fmj0p7hxxs86ndkg","k177gnp7tvr9phd9psfw21zgcs86ndx2",{"_creationTime":260,"_id":257,"identity":261,"providers":262,"workflow":422},1778698831609.0093,{"githubOwner":246,"githubRepo":247,"sourceUrl":14},{"classify":263,"discover":396,"github":399},{"commitSha":255,"extensions":264},[265,277,285,293,299,307,315,323,331,339,347,355,363,371,379],{"basePath":266,"description":267,"displayName":268,"installMethods":269,"rationale":270,"selectedPaths":271,"source":275,"sourceLanguage":276,"type":248},"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",[272],{"path":273,"priority":274},"SKILL.md","mandatory","rule","en",{"basePath":278,"description":279,"displayName":280,"installMethods":281,"rationale":282,"selectedPaths":283,"source":275,"sourceLanguage":276,"type":248},"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",[284],{"path":273,"priority":274},{"basePath":286,"description":287,"displayName":288,"installMethods":289,"rationale":290,"selectedPaths":291,"source":275,"sourceLanguage":276,"type":248},"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",[292],{"path":273,"priority":274},{"basePath":245,"description":294,"displayName":13,"installMethods":295,"rationale":296,"selectedPaths":297,"source":275,"sourceLanguage":276,"type":248},"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},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-calibrate/SKILL.md",[298],{"path":273,"priority":274},{"basePath":300,"description":301,"displayName":302,"installMethods":303,"rationale":304,"selectedPaths":305,"source":275,"sourceLanguage":276,"type":248},"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",[306],{"path":273,"priority":274},{"basePath":308,"description":309,"displayName":310,"installMethods":311,"rationale":312,"selectedPaths":313,"source":275,"sourceLanguage":276,"type":248},"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",[314],{"path":273,"priority":274},{"basePath":316,"description":317,"displayName":318,"installMethods":319,"rationale":320,"selectedPaths":321,"source":275,"sourceLanguage":276,"type":248},"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",[322],{"path":273,"priority":274},{"basePath":324,"description":325,"displayName":326,"installMethods":327,"rationale":328,"selectedPaths":329,"source":275,"sourceLanguage":276,"type":248},"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",[330],{"path":273,"priority":274},{"basePath":332,"description":333,"displayName":334,"installMethods":335,"rationale":336,"selectedPaths":337,"source":275,"sourceLanguage":276,"type":248},"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",[338],{"path":273,"priority":274},{"basePath":340,"description":341,"displayName":342,"installMethods":343,"rationale":344,"selectedPaths":345,"source":275,"sourceLanguage":276,"type":248},"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",[346],{"path":273,"priority":274},{"basePath":348,"description":349,"displayName":350,"installMethods":351,"rationale":352,"selectedPaths":353,"source":275,"sourceLanguage":276,"type":248},"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",[354],{"path":273,"priority":274},{"basePath":356,"description":357,"displayName":358,"installMethods":359,"rationale":360,"selectedPaths":361,"source":275,"sourceLanguage":276,"type":248},"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",[362],{"path":273,"priority":274},{"basePath":364,"description":365,"displayName":366,"installMethods":367,"rationale":368,"selectedPaths":369,"source":275,"sourceLanguage":276,"type":248},"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",[370],{"path":273,"priority":274},{"basePath":372,"description":373,"displayName":374,"installMethods":375,"rationale":376,"selectedPaths":377,"source":275,"sourceLanguage":276,"type":248},"mission-control/packages/clawhub-skills/oraclaw-solver","Industrial-grade scheduling and resource optimization for AI agents. 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