[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-Whatsonyourmind-oraclaw-pathfind-de":3,"guides-for-Whatsonyourmind-oraclaw-pathfind":436,"similar-k1715wpw5twjg6fq3s1zy3cw9s86nxqv-de":437},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":249,"isFallback":244,"parentExtension":255,"providers":256,"relations":262,"repo":265,"tags":432,"workflow":433},1778699152744.1807,"k1715wpw5twjg6fq3s1zy3cw9s86nxqv",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"A* Pfadfindung und Aufgaben-Sequenzierung für KI-Agenten. Finden Sie den optimalen Pfad durch Workflows, Abhängigkeiten und Entscheidungsbäume. K-kürzeste Pfade über Yen's Algorithmus. Kosten/Zeit/Risiko-Aufschlüsselung.",{"claudeCode":12},"Whatsonyourmind/oraclaw","OraClaw Pathfind","https://github.com/Whatsonyourmind/oraclaw",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":230,"workflow":247},1778699152744.181,"kn71p5qqdyz9kfnfszpz8g2ycn86ncr1","de",{"checks":20,"evaluatedAt":191,"extensionSummary":192,"features":193,"nonGoals":199,"practices":203,"prerequisites":207,"promptVersionExtension":210,"promptVersionScoring":211,"purpose":212,"rationale":213,"score":214,"summary":215,"tags":216,"tier":224,"useCases":225},[21,26,29,32,36,39,43,47,50,53,57,61,65,69,72,75,78,81,84,87,91,95,99,103,107,110,113,116,120,123,126,129,132,135,138,142,146,150,153,157,160,163,166,169,173,176,179,182,185,188],{"category":22,"check":23,"severity":24,"summary":25},"Praktischer Nutzen","Problemrelevanz","pass","Die Beschreibung gibt klar das Problem der optimalen Pfadfindung durch Workflows und Abhängigkeiten für KI-Agenten wieder, wobei Aufgaben-Sequenzierung und A*-Pfadfindung spezifisch erwähnt werden.",{"category":22,"check":27,"severity":24,"summary":28},"Alleinstellungsmerkmal","Die Fähigkeit bietet deterministische Optimierungs- und Algorithmen für KI-Agenten und liefert mathematisch korrekte Antworten, die LLMs nicht können, was einen erheblichen Mehrwert über einfaches Prompting hinaus darstellt.",{"category":22,"check":30,"severity":24,"summary":31},"Produktionsreife","Die Fähigkeit stellt ein gut definiertes Werkzeug für Pfadfindung und Aufgaben-Sequenzierung bereit und deckt den Kernlebenszyklus von Planung und Analyse ab. Die Dokumentation enthält auch Preisangaben und Anwendungsbeispiele, was auf die Bereitschaft für reale Workflows hindeutet.",{"category":33,"check":34,"severity":24,"summary":35},"Umfang","Prinzip der einzigen Verantwortung","Die Fähigkeit konzentriert sich ausschließlich auf Pfadfindung und Aufgaben-Sequenzierung mittels A* und verwandter Algorithmen, was in einen einzigen, kohärenten Bereich passt.",{"category":33,"check":37,"severity":24,"summary":38},"Qualität der Beschreibung","Die angezeigte Beschreibung spiegelt die Fähigkeiten der Fähigkeit wie in SKILL.md beschrieben genau und prägnant wider und umfasst A*-Pfadfindung, Aufgaben-Sequenzierung und Aufschlüsselung von Kosten/Zeit/Risiko.",{"category":40,"check":41,"severity":24,"summary":42},"Aufruf","Geltungsbereich von Werkzeugen","Die Fähigkeit stellt ein einzelnes, klar definiertes Werkzeug `plan_pathfind` mit einem klaren, strukturierten Eingabeschema für die Definition von Knoten und Kanten bereit.",{"category":44,"check":45,"severity":24,"summary":46},"Dokumentation","Konfigurations- & Parameterreferenz","Das SKILL.md dokumentiert klar das Eingabeschema für `plan_pathfind`, einschließlich der Struktur von Knoten und Kanten, und erklärt die verfügbaren Heuristiken und Regeln.",{"category":33,"check":48,"severity":24,"summary":49},"Benennung von Werkzeugen","Das einzige bereitgestellte Werkzeug `plan_pathfind` ist beschreibend und gibt seine Funktion klar an.",{"category":33,"check":51,"severity":24,"summary":52},"Minimale I/O-Oberfläche","Das Werkzeug `plan_pathfind` akzeptiert eine strukturierte JSON-Eingabe, die den Graphen und die Parameter präzise definiert, und seine Ausgabe wird als optimaler Pfad und Kostenaufschlüsselung dokumentiert.",{"category":54,"check":55,"severity":24,"summary":56},"Lizenz","Nutzbarkeit der Lizenz","Das Projekt gibt ausdrücklich die MIT-Lizenz an und stellt eine LICENSE-Datei zur Verfügung.",{"category":58,"check":59,"severity":24,"summary":60},"Wartung","Aktualität der Commits","Der letzte Commit war am 2. Mai 2026, was innerhalb der letzten 3 Monate liegt.",{"category":58,"check":62,"severity":63,"summary":64},"Abhängigkeitsverwaltung","not_applicable","Die Fähigkeit selbst verwaltet scheinbar keine Drittanbieter-Abhängigkeiten direkt auf eine Weise, die Updates oder Zusammenführungen über die eigene Kernlogik hinaus erfordern würde.",{"category":66,"check":67,"severity":24,"summary":68},"Sicherheit","Geheimnisverwaltung","Die Fähigkeit erfordert einen `ORACLAW_API_KEY`, aber dieser wird über Umgebungsvariablen gehandhabt, und es gibt keine Anzeichen dafür, dass Geheimnisse an stdout/stderr weitergegeben werden.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","Die Fähigkeit arbeitet mit strukturierten Graphdaten und Algorithmen; es gibt keine Anzeichen für das Laden oder Ausführen von nicht vertrauenswürdigem externem Code oder Daten.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Lieferketten-Granaten","Die Fähigkeit stützt sich auf ihre eigene gebündelte Logik und Algorithmen und ruft zur Laufzeit keinen entfernten Code oder Daten ab.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox-Isolation","Der Betrieb der Fähigkeit beschränkt sich auf algorithmische Berechnungen und beinhaltet keine Dateisystemänderungen außerhalb ihres definierten Umfangs.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox-Umgehung primitiven","Es wurden keine Anzeichen für abgetrennte Prozesse, Wiederholungsschleifen bei verweigerten Aufrufen oder andere Sandbox-Umgehung primitive im bereitgestellten Code und der Dokumentation gefunden.",{"category":66,"check":82,"severity":24,"summary":83},"Datenexfiltration","Die Fähigkeit verarbeitet Graphdaten zur Pfadfindung und beinhaltet keine Übermittlung vertraulicher Daten an Dritte.",{"category":66,"check":85,"severity":24,"summary":86},"Tricks mit verstecktem Text","Der gebündelte Inhalt und die Dokumentation scheinen frei von versteckten Steuerungs-Tricks, Steuerzeichen oder verschleiertem Text zu sein.",{"category":88,"check":89,"severity":24,"summary":90},"Hooks","Undurchsichtige Codeausführung","Die Logik der Fähigkeit scheint in klarem, lesbarem Quellcode zu liegen, ohne Anzeichen von Verschleierung, Base64-Nutzdaten oder zur Laufzeit abgerufenen Skripten.",{"category":92,"check":93,"severity":24,"summary":94},"Portabilität","Strukturelle Annahme","Die Fähigkeit arbeitet mit als Eingabe bereitgestellten Datenstrukturen und trifft keine Annahmen über das Dateisystemlayout des Benutzers.",{"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","Die SKILL.md Frontmatter deklariert eine `version: 1.0.0`, was eine aussagekräftige semantische Version ist.",{"category":104,"check":105,"severity":24,"summary":106},"Codeausführung","Validierung","Das Eingabeschema des Werkzeugs, wie in SKILL.md beschrieben, impliziert die Validierung der Graphenstruktur (Knoten, Kanten, Kosten) vor der Verarbeitung.",{"category":66,"check":108,"severity":63,"summary":109},"Ungeschützte destruktive Operationen","Die Fähigkeit ist rein analytisch und führt keine destruktiven Operationen durch.",{"category":104,"check":111,"severity":24,"summary":112},"Fehlerbehandlung","Das SKILL.md beschreibt das Verhalten für nicht existierende Pfade (leerer Pfad mit unendlichen Kosten) und impliziert damit eine Fehlerbehandlung für ungültige Graphzustände.",{"category":104,"check":114,"severity":63,"summary":115},"Protokollierung","Die Fähigkeit ist analytisch und führt keine destruktiven Aktionen oder ausgehenden Aufrufe durch, die eine Audit-Protokollierung erfordern würden.",{"category":117,"check":118,"severity":63,"summary":119},"Compliance","DSGVO","Die Fähigkeit verarbeitet Graphdaten und interagiert nicht mit personenbezogenen Daten.",{"category":117,"check":121,"severity":24,"summary":122},"Zielmarkt","Die Funktionalität der Fähigkeit basiert auf Algorithmen und hat keine regionalen oder jurisdiktionellen Einschränkungen, was sie global anwendbar macht.",{"category":92,"check":124,"severity":24,"summary":125},"Laufzeitstabilität","Die Fähigkeit arbeitet mit abstrakten Datenstrukturen und Algorithmen und ist daher über verschiedene Laufzeitumgebungen hinweg portierbar, ohne Annahmen über das Betriebssystem oder die Shell zu treffen.",{"category":44,"check":127,"severity":24,"summary":128},"README","Das README bietet einen umfassenden Überblick über den Zweck, die Werkzeuge und die Marktposition von OraClaw und ergänzt das SKILL.md.",{"category":33,"check":130,"severity":63,"summary":131},"Größe der Werkzeugoberfläche","Diese Erweiterung stellt nur ein einziges Werkzeug, `plan_pathfind`, bereit.",{"category":40,"check":133,"severity":63,"summary":134},"Sich überschneidende Werkzeuge mit ähnlicher Bedeutung","Da nur ein Werkzeug bereitgestellt wird, gibt es keine sich überschneidenden Werkzeuge mit ähnlicher Bedeutung.",{"category":44,"check":136,"severity":24,"summary":137},"Phantom-Funktionen","Alle beworbenen Funktionen wie A*-Pfadfindung und K-kürzeste Pfade sind implementiert und im SKILL.md beschrieben.",{"category":139,"check":140,"severity":24,"summary":141},"Installation","Installationsanleitung","Das README bietet klare Installationsanweisungen für den MCP-Server und enthält kopierbare JSON-Konfigurationen und Beispiel-Agenten-Prompts.",{"category":143,"check":144,"severity":24,"summary":145},"Fehler","Aktionsfähige Fehlermeldungen","Das SKILL.md gibt an, dass bei nicht existierenden Pfaden ein leerer Pfad mit unendlichen Kosten zurückgegeben wird, was ein definiertes Verhalten für einen Fehlerfall anzeigt.",{"category":147,"check":148,"severity":63,"summary":149},"Ausführung","Angepinnte Abhängigkeiten","Die Fähigkeit selbst scheint keine gebündelten Skripte mit direkten Abhängigkeiten zu haben, die angepinnt werden müssten.",{"category":33,"check":151,"severity":63,"summary":152},"Vorschau im Trockenlauf","Die Fähigkeit ist analytisch und führt keine zustandsverändernden Operationen durch, daher ist eine Vorschau im Trockenlauf nicht anwendbar.",{"category":154,"check":155,"severity":63,"summary":156},"Protokoll","Idempotente Wiederholung & Timeouts","Die Operation der Fähigkeit ist eine zustandslose Berechnung basierend auf bereitgestellten Graphdaten, wodurch Wiederholungen und Timeouts nicht direkt auf ihre Kernlogik anwendbar sind.",{"category":117,"check":158,"severity":63,"summary":159},"Telemetry-Opt-in","Es gibt keine Anzeichen dafür, dass diese Fähigkeit Telemetriedaten sendet.",{"category":40,"check":161,"severity":24,"summary":162},"Präziser Zweck","Das SKILL.md definiert klar den Zweck als A*-Pfadfindung und Aufgaben-Sequenzierung für KI-Agenten, die auf Aufgabenabhängigkeitsgraphen operieren, und gibt an, wann sie verwendet werden soll.",{"category":40,"check":164,"severity":24,"summary":165},"Prägnante Frontmatter","Die Frontmatter ist prägnant und in sich geschlossen und fasst die Kernfähigkeit der A*-Pfadfindung und Aufgaben-Sequenzierung zusammen.",{"category":44,"check":167,"severity":24,"summary":168},"Prägnanter Body","Das SKILL.md ist prägnant und beschreibt das Werkzeug, die Heuristiken, Regeln und Preise ohne unnötige Ausführlichkeit.",{"category":170,"check":171,"severity":63,"summary":172},"Kontext","Progressive Offenlegung","Die Fähigkeit ist unkompliziert und beinhaltet keine langwierigen Verfahren oder große Mengen an Drittmaterial, die eine progressive Offenlegung erfordern würden.",{"category":170,"check":174,"severity":63,"summary":175},"Forked Exploration","Diese Fähigkeit ist eine direkte Berechnung und beinhaltet keine tiefgreifende Erkundung oder Code-Überprüfung, daher ist `context: fork` nicht anwendbar.",{"category":22,"check":177,"severity":24,"summary":178},"Anwendungsbeispiele","Das SKILL.md liefert ein klares, kopierbares JSON-Beispiel für die Eingabestruktur des Werkzeugs `plan_pathfind`.",{"category":22,"check":180,"severity":24,"summary":181},"Randfälle","Das SKILL.md dokumentiert Randfälle wie nicht existierende Pfade, die unendliche Kosten zurückgeben, und spezifiziert Regeln für Kantenkosten.",{"category":104,"check":183,"severity":63,"summary":184},"Werkzeug-Fallback","Die Fähigkeit ist eigenständig und hängt nicht von externen MCP-Servern oder Werkzeugen ab.",{"category":66,"check":186,"severity":24,"summary":187},"Abbruch bei unerwartetem Zustand","Die Dokumentation beschreibt das Verhalten für einen ungültigen Graphzustand (kein Pfad gefunden), was auf einen kontrollierten Abbruch mit einem Bericht hindeutet.",{"category":92,"check":189,"severity":24,"summary":190},"Cross-Skill-Kopplung","Die Fähigkeit ist eigenständig und basiert nicht auf oder koppelt implizit mit anderen Fähigkeiten.",1778699008503,"Diese Fähigkeit bietet A*-Pfadfindungs- und Aufgaben-Sequenzierungsfunktionen für KI-Agenten und ermöglicht das Finden optimaler Routen durch Workflows, Abhängigkeiten und Entscheidungsbäume. Sie unterstützt K-kürzeste Pfade über Yen's Algorithmus und bietet Aufschlüsselungen von Kosten, Zeit und Risiko.",[194,195,196,197,198],"A* Pfadfindung für optimale Routen","K-kürzeste Pfade über Yen's Algorithmus","Aufschlüsselung von Kosten, Zeit und Risiko","Konfigurierbare Heuristiken (Kosten, Zeit, Risiko, gewichtet, null)","Aufgaben-Sequenzierung und Workflow-Navigation",[200,201,202],"LLM-basiertes Denken oder Halluzinationen","Echtzeit-Ausführung von Aufgaben","Komplexe Simulationen jenseits der Pfadanalyse",[204,205,206],"Algorithmenauswahl","Optimierung","Aufgabenplanung",[208,209],"ORACLAW_API_KEY Umgebungsvariable (für Premium-Funktionen/höhere Ratenlimits)","Zugriff auf die OraClaw API oder den MCP Server","3.0.0","4.4.0","KI-Agenten mit deterministischen mathematischen Werkzeugen zur optimalen Pfadfindung und Aufgaben-Sequenzierung auszustatten und damit die Grenzen von LLMs bei algorithmischem Denken zu überwinden.","Die Fähigkeit ist außerordentlich gut dokumentiert und robust, ohne signifikante Funde in irgendeiner Kategorie. Die einzigen geringfügigen Punkte, die nicht zutreffen, sind auf die in sich geschlossene, analytische Natur des Tools zurückzuführen.",99,"Eine robuste und gut dokumentierte Fähigkeit zur optimalen Pfadfindung und Aufgaben-Sequenzierung mit A*-Algorithmen.",[217,218,219,220,221,222,223],"pathfinding","routing","workflow","dependencies","critical-path","task-sequencing","astar","verified",[226,227,228,229],"Finden des schnellsten/günstigsten/sichersten Pfades durch einen Aufgabenabhängigkeitsgraphen","Optimale Sequenzierung von Aufgaben unter Berücksichtigung von Zeit, Kosten und Risiko","Navigation durch komplexe Workflows mit mehreren Routen zur Fertigstellung","Planung der Projektumsetzungsreihenfolge mit Abhängigkeitsbeschränkungen",{"codeQuality":231,"collectedAt":233,"documentation":234,"maintenance":237,"security":243,"testCoverage":246},{"hasLockfile":232},true,1778698992989,{"descriptionLength":235,"readmeSize":236},188,9472,{"closedIssues90d":238,"forks":239,"hasChangelog":232,"manifestVersion":240,"openIssues90d":8,"pushedAt":241,"stars":242},44,2,"1.0.0",1777714123000,8,{"hasNpmPackage":244,"license":245,"smitheryVerified":244},false,"MIT",{"hasCi":232,"hasTests":232},{"updatedAt":248},1778699152744,{"basePath":250,"githubOwner":251,"githubRepo":252,"locale":18,"slug":253,"type":254},"mission-control/packages/clawhub-skills/oraclaw-pathfind","Whatsonyourmind","oraclaw","oraclaw-pathfind","skill",null,{"evaluate":257,"extract":260},{"promptVersionExtension":210,"promptVersionScoring":211,"score":214,"tags":258,"targetMarket":259,"tier":224},[217,218,219,220,221,222,223],"global",{"commitSha":261,"license":245},"HEAD",{"repoId":263,"translatedFrom":264},"kd76fmxm1ng903s4fmj0p7hxxs86ndkg","k173hqx1847hrmdc2dhpm1f6n586md97",{"_creationTime":266,"_id":263,"identity":267,"providers":268,"workflow":428},1778698831609.0093,{"githubOwner":251,"githubRepo":252,"sourceUrl":14},{"classify":269,"discover":402,"github":405},{"commitSha":261,"extensions":270},[271,283,291,299,307,315,323,331,339,347,355,361,369,377,385],{"basePath":272,"description":273,"displayName":274,"installMethods":275,"rationale":276,"selectedPaths":277,"source":281,"sourceLanguage":282,"type":254},"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",[278],{"path":279,"priority":280},"SKILL.md","mandatory","rule","en",{"basePath":284,"description":285,"displayName":286,"installMethods":287,"rationale":288,"selectedPaths":289,"source":281,"sourceLanguage":282,"type":254},"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",[290],{"path":279,"priority":280},{"basePath":292,"description":293,"displayName":294,"installMethods":295,"rationale":296,"selectedPaths":297,"source":281,"sourceLanguage":282,"type":254},"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",[298],{"path":279,"priority":280},{"basePath":300,"description":301,"displayName":302,"installMethods":303,"rationale":304,"selectedPaths":305,"source":281,"sourceLanguage":282,"type":254},"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",[306],{"path":279,"priority":280},{"basePath":308,"description":309,"displayName":310,"installMethods":311,"rationale":312,"selectedPaths":313,"source":281,"sourceLanguage":282,"type":254},"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",[314],{"path":279,"priority":280},{"basePath":316,"description":317,"displayName":318,"installMethods":319,"rationale":320,"selectedPaths":321,"source":281,"sourceLanguage":282,"type":254},"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",[322],{"path":279,"priority":280},{"basePath":324,"description":325,"displayName":326,"installMethods":327,"rationale":328,"selectedPaths":329,"source":281,"sourceLanguage":282,"type":254},"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",[330],{"path":279,"priority":280},{"basePath":332,"description":333,"displayName":334,"installMethods":335,"rationale":336,"selectedPaths":337,"source":281,"sourceLanguage":282,"type":254},"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",[338],{"path":279,"priority":280},{"basePath":340,"description":341,"displayName":342,"installMethods":343,"rationale":344,"selectedPaths":345,"source":281,"sourceLanguage":282,"type":254},"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",[346],{"path":279,"priority":280},{"basePath":348,"description":349,"displayName":350,"installMethods":351,"rationale":352,"selectedPaths":353,"source":281,"sourceLanguage":282,"type":254},"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",[354],{"path":279,"priority":280},{"basePath":250,"description":356,"displayName":253,"installMethods":357,"rationale":358,"selectedPaths":359,"source":281,"sourceLanguage":282,"type":254},"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.",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-pathfind/SKILL.md",[360],{"path":279,"priority":280},{"basePath":362,"description":363,"displayName":364,"installMethods":365,"rationale":366,"selectedPaths":367,"source":281,"sourceLanguage":282,"type":254},"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",[368],{"path":279,"priority":280},{"basePath":370,"description":371,"displayName":372,"installMethods":373,"rationale":374,"selectedPaths":375,"source":281,"sourceLanguage":282,"type":254},"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",[376],{"path":279,"priority":280},{"basePath":378,"description":379,"displayName":380,"installMethods":381,"rationale":382,"selectedPaths":383,"source":281,"sourceLanguage":282,"type":254},"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",[384],{"path":279,"priority":280},{"basePath":386,"description":387,"displayName":388,"installMethods":389,"license":245,"rationale":390,"selectedPaths":391,"source":281,"sourceLanguage":282,"type":401},"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":388},"server.json with namespace/server name at mission-control/packages/mcp-server/server.json",[392,394,396,398],{"path":393,"priority":280},"server.json",{"path":395,"priority":280},"package.json",{"path":397,"priority":280},"README.md",{"path":399,"priority":400},"src/index.ts","low","mcp",{"sources":403},[404],"manual",{"closedIssues90d":238,"description":406,"forks":239,"homepage":407,"license":245,"openIssues90d":8,"pushedAt":241,"readmeSize":236,"stars":242,"topics":408},"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",[409,410,411,412,413,414,415,401,416,417,418,419,420,421,422,423,424,425,426,427],"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","monte-carlo","pagerank",{"classifiedAt":429,"discoverAt":430,"extractAt":431,"githubAt":431,"updatedAt":429},1778698837409,1778698831609,1778698835357,[223,221,220,217,218,222,219],{"evaluatedAt":434,"extractAt":435,"updatedAt":248},1778699008623,1778698837670,[],[438,466,495,525,555,581],{"_creationTime":439,"_id":440,"community":441,"display":442,"identity":448,"providers":452,"relations":460,"tags":462,"workflow":463},1778697652123.886,"k174rav3ndhd0xydpyp2k4nn8586nbvw",{"reviewCount":8},{"description":443,"installMethods":444,"name":446,"sourceUrl":447},"Route plain-language requests for Pi, Claude Code, Cursor, Copilot, OpenClaw ACP, OpenCode, Gemini CLI, Qwen, Kiro, Kimi, iFlow, Factory Droid, Kilocode, or explicit ACP harness work into either OpenClaw ACP runtime sessions or direct acpx-driven sessions (\"telephone game\" flow). For coding-agent thread requests, read this skill first, then use only `sessions_spawn` for thread creation. Codex chat binding defaults to the native Codex app-server plugin unless ACP is explicit or background spawn needs ACP.",{"claudeCode":445},"steipete/clawdis","acp-router","https://github.com/steipete/clawdis",{"basePath":449,"githubOwner":450,"githubRepo":451,"locale":282,"slug":446,"type":254},"extensions/acpx/skills/acp-router","steipete","clawdis",{"evaluate":453,"extract":459},{"promptVersionExtension":210,"promptVersionScoring":211,"score":454,"tags":455,"targetMarket":259,"tier":224},100,[218,456,457,219,458],"acp","coding-assistants","automation",{"commitSha":261},{"repoId":461},"kd738npxg9yh3xf3vddzy9fyfh86nhng",[456,458,457,218,219],{"evaluatedAt":464,"extractAt":465,"updatedAt":464},1778698053003,1778697652123,{"_creationTime":467,"_id":468,"community":469,"display":470,"identity":476,"providers":480,"relations":488,"tags":491,"workflow":492},1778696691708.3306,"k172evhhmbzzyp7g0t2caf4hfh86nsp9",{"reviewCount":8},{"description":471,"installMethods":472,"name":474,"sourceUrl":475},"First-run setup for ruvector@0.2.25 — installs ONNX/Brain/SONA add-ons, registers the MCP server, and verifies the install via `doctor`",{"claudeCode":473},"ruvnet/ruflo","vector-setup","https://github.com/ruvnet/ruflo",{"basePath":477,"githubOwner":478,"githubRepo":479,"locale":282,"slug":474,"type":254},"plugins/ruflo-ruvector/skills/vector-setup","ruvnet","ruflo",{"evaluate":481,"extract":487},{"promptVersionExtension":210,"promptVersionScoring":211,"score":454,"tags":482,"targetMarket":259,"tier":224},[483,484,485,486,220],"setup","installation","ruvector","npm",{"commitSha":261},{"parentExtensionId":489,"repoId":490},"k17710fw96s8hs1y3j2cye3aa586n523","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[220,484,486,485,483],{"evaluatedAt":493,"extractAt":494,"updatedAt":493},1778701365160,1778696691708,{"_creationTime":496,"_id":497,"community":498,"display":499,"identity":505,"providers":509,"relations":519,"tags":521,"workflow":522},1778696052276.0203,"k17bgxxgryq8edg32egypsvqtn86m1h7",{"reviewCount":8},{"description":500,"installMethods":501,"name":503,"sourceUrl":504},"Detect and untangle circular dependencies. Runs madge/skott (TS), pycycle (Py), or compiler-only checks (Go/Rust). Auto-fixes leaf-extractable cycles; reports core cycles for human review. Use when the user asks to find circular imports, fix dependency cycles, or untangle module graph. Example queries — \"find circular imports\", \"fix dependency cycles\", \"untangle our module graph\", \"why is madge complaining\".",{"claudeCode":502},"raintree-technology/claude-starter","cleanup-cycles","https://github.com/raintree-technology/claude-starter",{"basePath":506,"githubOwner":507,"githubRepo":508,"locale":282,"slug":503,"type":254},"templates/.claude/skills/cleanup-cycles","raintree-technology","claude-starter",{"evaluate":510,"extract":518},{"promptVersionExtension":210,"promptVersionScoring":211,"score":454,"tags":511,"targetMarket":259,"tier":224},[512,220,513,514,417,515,516,517],"code-quality","javascript","python","go","rust","refactoring",{"commitSha":261},{"repoId":520},"kd78ywakatnz4sjfx781sy14vh86mtty",[512,220,515,513,514,517,516,417],{"evaluatedAt":523,"extractAt":524,"updatedAt":523},1778696977114,1778696052276,{"_creationTime":526,"_id":527,"community":528,"display":529,"identity":535,"providers":539,"relations":548,"tags":551,"workflow":552},1778695548458.3328,"k17cyw0d6mk1vdgew2xmncx1f186npdm",{"reviewCount":8},{"description":530,"installMethods":531,"name":533,"sourceUrl":534},"Audit project dependencies for version staleness, security vulnerabilities, and compatibility issues. Covers lock file analysis, upgrade path planning, and breaking change assessment. Use before a release to ensure dependencies are current and secure, during periodic maintenance reviews, after receiving a security advisory, when upgrading to a new language version, before submitting to CRAN or npm, or when inheriting a project to assess its dependency health.\n",{"claudeCode":532},"pjt222/agent-almanac","audit-dependency-versions","https://github.com/pjt222/agent-almanac",{"basePath":536,"githubOwner":537,"githubRepo":538,"locale":282,"slug":533,"type":254},"skills/audit-dependency-versions","pjt222","agent-almanac",{"evaluate":540,"extract":547},{"promptVersionExtension":210,"promptVersionScoring":211,"score":454,"tags":541,"targetMarket":259,"tier":224},[220,542,543,544,545,546],"auditing","security","upgrades","versioning","maintenance",{"commitSha":261},{"parentExtensionId":549,"repoId":550},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[542,220,546,543,544,545],{"evaluatedAt":553,"extractAt":554,"updatedAt":553},1778696062378,1778695548458,{"_creationTime":556,"_id":557,"community":558,"display":559,"identity":564,"providers":568,"relations":574,"tags":577,"workflow":578},1778699234184.6174,"k174zww66m804nhr89ttra7r6d86nwyg",{"reviewCount":8},{"description":560,"installMethods":561,"name":483,"sourceUrl":563},"Use first for install/update routing — sends setup, doctor, or MCP requests to the correct OMC setup flow",{"claudeCode":562},"Yeachan-Heo/oh-my-claudecode","https://github.com/Yeachan-Heo/oh-my-claudecode",{"basePath":565,"githubOwner":566,"githubRepo":567,"locale":282,"slug":483,"type":254},"skills/setup","Yeachan-Heo","oh-my-claudecode",{"evaluate":569,"extract":573},{"promptVersionExtension":210,"promptVersionScoring":211,"score":454,"tags":570,"targetMarket":259,"tier":224},[483,218,571,572,401],"configuration","cli",{"commitSha":261},{"parentExtensionId":575,"repoId":576},"k17brg5egdw1jbncj1j4wfv3fh86n639","kd74zv63fryf9prygtq7gf4es986n22y",[572,571,401,218,483],{"evaluatedAt":579,"extractAt":580,"updatedAt":579},1778699724286,1778699234184,{"_creationTime":582,"_id":583,"community":584,"display":585,"identity":591,"providers":595,"relations":603,"tags":606,"workflow":607},1778675056600.2566,"k1749wefszncghc6rgh3g0cdks86mem5",{"reviewCount":8},{"description":586,"installMethods":587,"name":589,"sourceUrl":590},"Deprecated redirect skill that routes legacy 'content creator' requests to the correct specialist. Use when a user invokes 'content creator', asks to write a blog post, article, guide, or brand voice analysis (routes to content-production), or asks to plan content, build a topic cluster, or create a content calendar (routes to content-strategy). Does not handle requests directly — identifies user intent and redirects to content-production for writing/SEO/brand-voice tasks or content-strategy for planning tasks.",{"claudeCode":588},"alirezarezvani/claude-skills","content-creator","https://github.com/alirezarezvani/claude-skills",{"basePath":592,"githubOwner":593,"githubRepo":594,"locale":282,"slug":589,"type":254},"marketing-skill/skills/content-creator","alirezarezvani","claude-skills",{"evaluate":596,"extract":602},{"promptVersionExtension":210,"promptVersionScoring":211,"score":454,"tags":597,"targetMarket":259,"tier":224},[598,599,600,601,218],"marketing","content","redirect","deprecation",{"commitSha":261},{"parentExtensionId":604,"repoId":605},"k170sws65f0ebecn36z3q8c2z186m477","kd7ff9s1w43mfyy1n7hf87816186m6px",[599,601,598,600,218],{"evaluatedAt":608,"extractAt":609,"updatedAt":608},1778684296105,1778675056600]