[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-Whatsonyourmind-oraclaw-graph-de":3,"guides-for-Whatsonyourmind-oraclaw-graph":428,"similar-k178ewgs2mfetwctvf1p8pga2186mmvz-de":429},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":243,"isFallback":238,"parentExtension":248,"providers":249,"relations":255,"repo":258,"tags":424,"workflow":425},1778699140102.701,"k178ewgs2mfetwctvf1p8pga2186mmvz",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Netzwerkinformationen für KI-Agenten. PageRank, Community-Erkennung (Louvain), kritischer Pfad und Engpassanalyse für jeden Graphen verbundener Elemente.",{"claudeCode":12},"Whatsonyourmind/oraclaw","oraclaw-graph","https://github.com/Whatsonyourmind/oraclaw",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":224,"workflow":241},1778699140102.7012,"kn70ajh4vmhdg7daaz4qhdr2c986mtex","de",{"checks":20,"evaluatedAt":193,"extensionSummary":194,"features":195,"nonGoals":201,"promptVersionExtension":205,"promptVersionScoring":206,"purpose":207,"rationale":208,"score":209,"summary":210,"tags":211,"tier":218,"useCases":219},[21,26,29,32,36,39,43,48,51,54,58,62,65,69,72,75,78,81,84,87,91,95,99,103,107,110,113,117,121,124,127,130,133,136,139,143,147,151,154,158,161,164,167,170,174,177,181,184,187,190],{"category":22,"check":23,"severity":24,"summary":25},"Praktischer Nutzen","Relevanz des Problems","pass","Die Beschreibung erläutert klar das Problem der Notwendigkeit von Netzwerkinformationen und liefert konkrete Beispiele wie PageRank und Community-Erkennung.",{"category":22,"check":27,"severity":24,"summary":28},"Alleinstellungsmerkmal","Die Erweiterung bietet deterministische Optimierungs- und Analysefunktionen (PageRank, Louvain usw.), die über das hinausgehen, was ein Standard-LLM bieten kann, und adressiert direkt komplexe Netzwerkanalyseaufgaben.",{"category":22,"check":30,"severity":24,"summary":31},"Produktionsreife","Die Fähigkeit bietet ein vollständig implementiertes Werkzeug für die Graphenanalyse mit klaren Eingaben, Ausgaben und Preisen, das sich für reale Arbeitsabläufe eignet.",{"category":33,"check":34,"severity":24,"summary":35},"Umfang","Prinzip der einzigen Verantwortung","Die Erweiterung konzentriert sich speziell auf Netzwerkinformationen und Graphenanalyse mit einem einzigen Werkzeug (`analyze_decision_graph`) für diesen Bereich.",{"category":33,"check":37,"severity":24,"summary":38},"Qualität der Beschreibung","Die angezeigte Beschreibung spiegelt die im SKILL.md und README beschriebenen Fähigkeiten genau und prägnant wider.",{"category":40,"check":41,"severity":24,"summary":42},"Aufruf","Abgegrenzte Werkzeuge","Die Erweiterung stellt ein einziges, gut abgegrenztes Werkzeug (`analyze_decision_graph`) für die Graphenanalyse bereit.",{"category":44,"check":45,"severity":46,"summary":47},"Dokumentation","Konfiguration & Parameterreferenz","warning","Das SKILL.md listet einen `ORACLAW_API_KEY` als erforderliche Umgebungsvariable auf, gibt aber nicht an, wie dieser erhalten werden kann oder welche Geltungsbereiche er erfordert, noch werden andere Konfigurationsparameter detailliert beschrieben.",{"category":33,"check":49,"severity":24,"summary":50},"Benennung der Werkzeuge","Das einzelne Werkzeug `analyze_decision_graph` ist beschreibend und relevant für den Bereich der Fähigkeit.",{"category":33,"check":52,"severity":24,"summary":53},"Minimale I/O-Oberfläche","Die Eingabeparameter des Werkzeugs (Knoten, Kanten, optionale Quelle/Ziel) sind gut definiert und dokumentiert, und die Ausgabe liefert spezifische Analysewerte und Zuweisungen.",{"category":55,"check":56,"severity":24,"summary":57},"Lizenz","Lizenznutzbarkeit","Die Erweiterung enthält eine LICENSE-Datei und deklariert die MIT-Lizenz, eine permissive Open-Source-Lizenz.",{"category":59,"check":60,"severity":24,"summary":61},"Wartung","Aktualität der Commits","Der letzte Commit war am 2. Mai 2026, was innerhalb der letzten 90 Tage liegt.",{"category":59,"check":63,"severity":24,"summary":64},"Abhängigkeitsverwaltung","Die Existenz einer Lock-Datei (`package-lock.json` oder `yarn.lock`, impliziert durch `npm install` und das `npm`-Badge) deutet darauf hin, dass die Abhängigkeitsverwaltung gehandhabt wird.",{"category":66,"check":67,"severity":46,"summary":68},"Sicherheit","Geheimnisverwaltung","Das SKILL.md gibt `ORACLAW_API_KEY` als erforderliche Umgebungsvariable an, aber das README und SKILL.md beschreiben nicht, wie dieser Schlüssel zu erhalten ist oder welche Geltungsbereiche er hat, was das Risiko von Fehlkonfigurationen oder unsachgemäßer Handhabung erhöht.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","Die Erweiterung arbeitet mit strukturierten Daten (Knoten, Kanten) und weist keine Mechanismen zum Laden oder Ausführen von nicht vertrauenswürdigem Drittanbieter-Code oder -Daten auf.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Lieferketten-Granaten","Die Erweiterung scheint zur Laufzeit keine externen Inhalte abzurufen; alle Logik und Daten scheinen intern gebündelt oder verarbeitet zu werden.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox-Isolation","Die Erweiterung arbeitet mit Graph-Daten und -Analysen; es gibt keine Hinweise auf Dateisystemänderungen oder Operationen außerhalb ihres beabsichtigten Umfangs.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox-Escape-Primitive","Es sind keine getrennten Prozesse oder Deny-Retry-Schleifen in der bereitgestellten Skill-Beschreibung und Metadaten ersichtlich.",{"category":66,"check":82,"severity":24,"summary":83},"Datenexfiltration","Die Erweiterung analysiert Graph-Daten und scheint keine vertraulichen Informationen zu exfiltrieren. Ausgehende Aufrufe sind auf den API-Endpunkt beschränkt.",{"category":66,"check":85,"severity":24,"summary":86},"Versteckte Texttricks","Der gebündelte Inhalt scheint frei von versteckten Lenkungstricks zu sein, mit sauberem ASCII und erwarteten Unicode-Zeichen.",{"category":88,"check":89,"severity":24,"summary":90},"Hooks","Undurchsichtige Codeausführung","Die Skill-Beschreibung und Metadaten deuten nicht auf verschleierte Codeausführung oder das Abrufen von Skripten zur Laufzeit hin.",{"category":92,"check":93,"severity":24,"summary":94},"Portabilität","Strukturelle Annahme","Die Fähigkeit arbeitet mit bereitgestellten Graph-Daten und trifft keine Annahmen über die Struktur des Benutzerprojekts.",{"category":96,"check":97,"severity":24,"summary":98},"Vertrauen","Aufmerksamkeit für Issues","Es gibt 0 offene und 44 geschlossene Issues in den letzten 90 Tagen, was auf aktive Wartung und Reaktionsfähigkeit hindeutet.",{"category":100,"check":101,"severity":24,"summary":102},"Versionierung","Release-Management","Der Frontmatter im SKILL.md deklariert Version 1.0.0 und es gibt eine CHANGELOG.md, was auf eine klare Versionierung hindeutet.",{"category":104,"check":105,"severity":24,"summary":106},"Codeausführung","Validierung","Das Eingabeschema des Werkzeugs (Knoten, Kanten, Typen) impliziert eine strukturierte Validierung, und die Ausgabe wird als strukturierter JSON beschrieben.",{"category":66,"check":108,"severity":24,"summary":109},"Ungeschützte destruktive Operationen","Die Erweiterung führt Analysen durch, keine destruktiven Operationen, daher ist diese Prüfung nicht anwendbar.",{"category":104,"check":111,"severity":24,"summary":112},"Fehlerbehandlung","Das Werkzeug wird als Rückgabe von strukturiertem JSON beschrieben, was eine angemessene Fehlerbehandlung für die Verarbeitung durch den Agenten impliziert.",{"category":104,"check":114,"severity":115,"summary":116},"Protokollierung","not_applicable","Dies ist eine analytische Fähigkeit 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 Erweiterung analysiert Netzwerkdiagramme und scheint keine personenbezogenen Daten zu verarbeiten.",{"category":118,"check":122,"severity":24,"summary":123},"Zielmarkt","Es wird keine regionale oder juristische Logik erkannt; die Erweiterung ist global anwendbar.",{"category":92,"check":125,"severity":24,"summary":126},"Laufzeitstabilität","Die Fähigkeit basiert auf Standard-Algorithmen zur Graphenanalyse und ist nicht an spezifische Betriebssystem- oder Shell-Umgebungen gebunden.",{"category":44,"check":128,"severity":24,"summary":129},"README","Die README-Datei existiert und beschreibt klar den Zweck und die Fähigkeiten der Erweiterung.",{"category":33,"check":131,"severity":115,"summary":132},"Größe der Werkzeugoberfläche","Die Erweiterung stellt ein einziges Werkzeug (`analyze_decision_graph`) bereit, daher ist die Größe der Werkzeugoberfläche nicht anwendbar.",{"category":40,"check":134,"severity":115,"summary":135},"Sich überschneidende, fast synonyme Werkzeuge","Die Erweiterung stellt nur ein Werkzeug bereit, daher gibt es keine sich überschneidenden, fast synonymen Werkzeuge.",{"category":44,"check":137,"severity":24,"summary":138},"Phantomfunktionen","Alle beworbenen Fähigkeiten, wie PageRank und Community-Erkennung, spiegeln sich in der Werkzeugbeschreibung und den Metadaten wider.",{"category":140,"check":141,"severity":46,"summary":142},"Installation","Installationsanleitung","Obwohl Installationsanweisungen für den MCP-Server und die REST-API bereitgestellt werden, wird die Anforderung eines `ORACLAW_API_KEY` ohne klare Schritte zur Beschaffung oder dessen erforderliche Geltungsbereiche erwähnt.",{"category":144,"check":145,"severity":24,"summary":146},"Fehler","Handhabbare Fehlermeldungen","Das Werkzeug wird als Rückgabe von strukturiertem JSON beschrieben, was handhabbare Fehlermeldungen für den Agenten impliziert.",{"category":148,"check":149,"severity":24,"summary":150},"Ausführung","Angepinnte Abhängigkeiten","Die Anwesenheit von `npm install` und die Erwähnung eines npm SDK deuten darauf hin, dass Abhängigkeiten wahrscheinlich über Lock-Dateien angepinnt werden.",{"category":33,"check":152,"severity":115,"summary":153},"Dry-Run-Vorschau","Die Erweiterung führt Analysen durch und hat keine zustandsverändernden Operationen, daher ist eine Dry-Run-Funktionalität nicht anwendbar.",{"category":155,"check":156,"severity":24,"summary":157},"Protokoll","Idempotente Wiederholung & Timeouts","Die Erweiterung ist analytisch und beinhaltet keine externen mutierenden Operationen, daher sind Timeouts und Idempotenz nicht anwendbar.",{"category":118,"check":159,"severity":24,"summary":160},"Telemetry-Opt-in","Es wird keine Telemetrie-Erfassung erwähnt, daher ist diese Prüfung nicht anwendbar.",{"category":40,"check":162,"severity":24,"summary":163},"Präziser Zweck","Der Zweck gibt klar das Artefakt (Graph verbundener Elemente) und die Absicht des Benutzers (Netzwerkinformationen, Analyse von Einfluss, Cluster, Pfaden, Engpässen) an.",{"category":40,"check":165,"severity":24,"summary":166},"Prägnanter Frontmatter","Der Frontmatter ist prägnant, gibt eine klare Zusammenfassung der Kernfähigkeiten und enthält relevante Tags.",{"category":44,"check":168,"severity":24,"summary":169},"Prägnanter Körper","Der Körper des SKILL.md ist prägnant und beschreibt direkt das Werkzeug und seine Verwendung, ohne übermäßiges Vorwort.",{"category":171,"check":172,"severity":115,"summary":173},"Kontext","Progressive Offenlegung","Die Fähigkeit ist unkompliziert mit einem einzigen Werkzeug und erfordert keine komplexen Verfahren, die eine progressive Offenlegung erfordern würden.",{"category":171,"check":175,"severity":115,"summary":176},"Verzweigte Erkundung","Die Fähigkeit führt eine direkte Analyse durch und beinhaltet keine tiefe Erkundung, die eine Verzweigung des Kontexts erfordern würde.",{"category":22,"check":178,"severity":179,"summary":180},"Nutzungsbeispiele","info","Obwohl das README Beispiele für die REST-API und SDKs liefert und das SKILL.md Knoten-/Kanten-Typen auflistet, gibt es keine End-to-End, kopierbaren Beispiele für das Werkzeug `analyze_decision_graph` im Skill-Kontext.",{"category":22,"check":182,"severity":179,"summary":183},"Randfälle","Das SKILL.md erwähnt Knoten- und Kantentypen sowie grundlegende Anforderungen, dokumentiert jedoch keine Fehlerfälle oder Wiederherstellungsschritte für fehlerhafte Eingaben oder nicht erfüllte Abhängigkeiten.",{"category":104,"check":185,"severity":115,"summary":186},"Werkzeug-Fallback","Diese Fähigkeit verwendet ihr eigenes internes Werkzeug und ist nicht auf einen externen MCP-Server mit Fallback-Pfaden angewiesen.",{"category":66,"check":188,"severity":24,"summary":189},"Halt bei unerwartetem Zustand","Die Fähigkeit arbeitet mit bereitgestellten Daten und führt keine Aktionen aus, die eine Überprüfung auf unerwartete VZustände in einem Arbeitsverzeichnis erfordern würden.",{"category":92,"check":191,"severity":24,"summary":192},"Skill-übergreifende Kopplung","Die Fähigkeit ist in sich geschlossen und konzentriert sich auf die Graphenanalyse ohne implizite Abhängigkeit von anderen Fähigkeiten.",1778698992500,"Diese Fähigkeit bietet Netzwerkinformationen für KI-Agenten und führt Analysen wie PageRank, Louvain-Community-Erkennung, kritischen Pfad und Engpassanalyse für Graphendaten durch. Für volle Funktionalität ist ein API-Schlüssel erforderlich.",[196,197,198,199,200],"PageRank-Score-Berechnung","Louvain-Community-Erkennung","Kritische Pfadanalyse","Identifizierung von Engpassknoten","Analyse von Entscheidungsdiagrammen",[202,203,204],"Allgemeine Datenvisualisierung","Echtzeit-Netzwerküberwachung","Graph-Manipulation (Hinzufügen/Löschen von Knoten/Kanten)","3.0.0","4.4.0","KI-Agenten mit fortgeschrittenen Netzwerkanalysefähigkeiten auszustatten, damit sie die Struktur, den Einfluss und die kritischen Komponenten verbundener Daten verstehen können.","Die Suche nach Konfigurations- und Parameterreferenzen hat eine Warnmeldung und Secret Management ebenfalls eine Warnmeldung. 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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",[271],{"path":272,"priority":273},"SKILL.md","mandatory","rule","en",{"basePath":277,"description":278,"displayName":279,"installMethods":280,"rationale":281,"selectedPaths":282,"source":274,"sourceLanguage":275,"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",[283],{"path":272,"priority":273},{"basePath":285,"description":286,"displayName":287,"installMethods":288,"rationale":289,"selectedPaths":290,"source":274,"sourceLanguage":275,"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",[291],{"path":272,"priority":273},{"basePath":293,"description":294,"displayName":295,"installMethods":296,"rationale":297,"selectedPaths":298,"source":274,"sourceLanguage":275,"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",[299],{"path":272,"priority":273},{"basePath":301,"description":302,"displayName":303,"installMethods":304,"rationale":305,"selectedPaths":306,"source":274,"sourceLanguage":275,"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",[307],{"path":272,"priority":273},{"basePath":309,"description":310,"displayName":311,"installMethods":312,"rationale":313,"selectedPaths":314,"source":274,"sourceLanguage":275,"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",[315],{"path":272,"priority":273},{"basePath":317,"description":318,"displayName":319,"installMethods":320,"rationale":321,"selectedPaths":322,"source":274,"sourceLanguage":275,"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",[323],{"path":272,"priority":273},{"basePath":325,"description":326,"displayName":327,"installMethods":328,"rationale":329,"selectedPaths":330,"source":274,"sourceLanguage":275,"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",[331],{"path":272,"priority":273},{"basePath":333,"description":334,"displayName":335,"installMethods":336,"rationale":337,"selectedPaths":338,"source":274,"sourceLanguage":275,"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",[339],{"path":272,"priority":273},{"basePath":244,"description":341,"displayName":13,"installMethods":342,"rationale":343,"selectedPaths":344,"source":274,"sourceLanguage":275,"type":247},"Network intelligence for AI agents. PageRank, community detection (Louvain), critical path, and bottleneck analysis for any graph of connected things.",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-graph/SKILL.md",[345],{"path":272,"priority":273},{"basePath":347,"description":348,"displayName":349,"installMethods":350,"rationale":351,"selectedPaths":352,"source":274,"sourceLanguage":275,"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",[353],{"path":272,"priority":273},{"basePath":355,"description":356,"displayName":357,"installMethods":358,"rationale":359,"selectedPaths":360,"source":274,"sourceLanguage":275,"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",[361],{"path":272,"priority":273},{"basePath":363,"description":364,"displayName":365,"installMethods":366,"rationale":367,"selectedPaths":368,"source":274,"sourceLanguage":275,"type":247},"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",[369],{"path":272,"priority":273},{"basePath":371,"description":372,"displayName":373,"installMethods":374,"rationale":375,"selectedPaths":376,"source":274,"sourceLanguage":275,"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",[377],{"path":272,"priority":273},{"basePath":379,"description":380,"displayName":381,"installMethods":382,"license":239,"rationale":383,"selectedPaths":384,"source":274,"sourceLanguage":275,"type":394},"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":381},"server.json with namespace/server name at mission-control/packages/mcp-server/server.json",[385,387,389,391],{"path":386,"priority":273},"server.json",{"path":388,"priority":273},"package.json",{"path":390,"priority":273},"README.md",{"path":392,"priority":393},"src/index.ts","low","mcp",{"sources":396},[397],"manual",{"closedIssues90d":232,"description":399,"forks":233,"homepage":400,"license":239,"openIssues90d":8,"pushedAt":235,"readmeSize":230,"stars":236,"topics":401},"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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Finden Sie den optimalen Pfad durch Workflows, Abhängigkeiten und Entscheidungsbäume. K-kürzeste Pfade über Yen's Algorithmus. Kosten/Zeit/Risiko-Aufschlüsselung.",{"claudeCode":12},"OraClaw Pathfind",{"basePath":347,"githubOwner":245,"githubRepo":246,"locale":18,"slug":349,"type":247},{"evaluate":468,"extract":477},{"promptVersionExtension":205,"promptVersionScoring":206,"score":469,"tags":470,"targetMarket":252,"tier":450},99,[471,472,473,474,216,475,476],"pathfinding","routing","workflow","dependencies","task-sequencing","astar",{"commitSha":254,"license":239},{"repoId":256,"translatedFrom":479},"k173hqx1847hrmdc2dhpm1f6n586md97",[476,216,474,471,472,475,473],{"evaluatedAt":482,"extractAt":427,"updatedAt":483},1778699008623,1778699152744,{"_creationTime":485,"_id":486,"community":487,"display":488,"identity":494,"providers":499,"relations":508,"tags":511,"workflow":512},1778683460321.3816,"k17fd1dhmc76frn5d1xjjc23bx86mjba",{"reviewCount":8},{"description":489,"installMethods":490,"name":492,"sourceUrl":493},"Capture a full DevTools-protocol trace of any browser automation — CDP firehose, screenshots, and DOM dumps — then bisect the stream into per-page searchable buckets. 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Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.",{"claudeCode":573},"AlterLab-IEU/AlterLab-Academic-Skills","AlterLab NetworkX","https://github.com/AlterLab-IEU/AlterLab-Academic-Skills",{"basePath":577,"githubOwner":578,"githubRepo":579,"locale":275,"slug":580,"type":247},"skills/data-science/alterlab-networkx","AlterLab-IEU","AlterLab-Academic-Skills","alterlab-networkx",{"evaluate":582,"extract":586},{"promptVersionExtension":205,"promptVersionScoring":206,"score":447,"tags":583,"targetMarket":252,"tier":450},[584,449,213,556,585,403],"data-science","visualization",{"commitSha":254,"license":239},{"repoId":588},"kd7fqvj70pvyn4r3q9kctpnd7d86mfqd",[403,584,449,213,556,585],{"evaluatedAt":591,"extractAt":592,"updatedAt":591},1778676461652,1778675145461]