[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-Whatsonyourmind-oraclaw-graph-en":3,"guides-for-Whatsonyourmind-oraclaw-graph":426,"similar-k174fcjz3srj8dbkh0972g1stn86nb3t-en":427},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":245,"isFallback":240,"parentExtension":250,"providers":251,"relations":256,"repo":258,"tags":422,"workflow":423},1778698837670.8003,"k174fcjz3srj8dbkh0972g1stn86nb3t",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Network intelligence for AI agents. PageRank, community detection (Louvain), critical path, and bottleneck analysis for any graph of connected things.",{"claudeCode":12},"Whatsonyourmind/oraclaw","oraclaw-graph","https://github.com/Whatsonyourmind/oraclaw",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":226,"workflow":243},1778698992613.17,"kn75zem8m0q1fcanyyem82b32h86n2a4","en",{"checks":20,"evaluatedAt":194,"extensionSummary":195,"features":196,"nonGoals":202,"promptVersionExtension":206,"promptVersionScoring":207,"purpose":208,"rationale":209,"score":210,"summary":211,"tags":212,"targetMarket":219,"tier":220,"useCases":221},[21,26,29,32,36,39,43,48,51,54,58,62,65,69,72,75,78,81,84,87,91,95,99,103,107,110,113,117,121,124,127,130,133,136,139,143,147,151,154,158,161,164,167,170,174,177,181,184,187,191],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem of needing network intelligence and provides concrete examples like PageRank and community detection.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The extension offers deterministic optimization and analysis (PageRank, Louvain, etc.) beyond what a standard LLM can provide, directly addressing complex network analysis tasks.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill offers a fully implemented tool for graph analysis with clear inputs, outputs, and pricing, suitable for real-world workflows.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The extension focuses specifically on network intelligence and graph analysis, with a single tool (`analyze_decision_graph`) for this domain.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately and concisely reflects the capabilities described in the SKILL.md and README.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The extension exposes a single, well-scoped tool (`analyze_decision_graph`) for graph analysis.",{"category":44,"check":45,"severity":46,"summary":47},"Documentation","Configuration & parameter reference","warning","The SKILL.md lists an `ORACLAW_API_KEY` as a required environment variable but does not specify how to obtain it or its required scopes, nor does it detail other configuration parameters.",{"category":33,"check":49,"severity":24,"summary":50},"Tool naming","The single tool `analyze_decision_graph` is descriptive and relevant to the skill's domain.",{"category":33,"check":52,"severity":24,"summary":53},"Minimal I/O surface","The tool's input parameters (nodes, edges, optional sourceGoal, targetGoal) are well-defined and documented, and the output provides specific analysis scores and assignments.",{"category":55,"check":56,"severity":24,"summary":57},"License","License usability","The extension includes a LICENSE file and declares the MIT license, which is a permissive open-source license.",{"category":59,"check":60,"severity":24,"summary":61},"Maintenance","Commit recency","The last commit was on May 2, 2026, which is within the last 90 days.",{"category":59,"check":63,"severity":24,"summary":64},"Dependency Management","The presence of a lockfile (`package-lock.json` or `yarn.lock` implied by `npm install` and the `npm` badge) suggests dependency management is handled.",{"category":66,"check":67,"severity":46,"summary":68},"Security","Secret Management","The SKILL.md specifies `ORACLAW_API_KEY` as a required environment variable, but the README and SKILL.md do not detail how to obtain this key or its scope, increasing the risk of misconfiguration or mishandling.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","The extension appears to operate on structured data (nodes, edges) and does not indicate any mechanism for loading or executing untrusted third-party code or data.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","The extension does not appear to fetch external content at runtime; all logic and data seem to be bundled or processed internally.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","The extension operates on graph data and analysis; there are no indications of file system modifications or operations outside its intended scope.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","No detached processes or deny-retry loops are apparent in the provided skill description and metadata.",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","The extension analyzes graph data and does not appear to exfiltrate any confidential information. Outbound calls are limited to the API endpoint.",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","The bundled content appears to be free of hidden steering tricks, with clean ASCII and expected Unicode characters.",{"category":88,"check":89,"severity":24,"summary":90},"Hooks","Opaque code execution","The skill description and metadata do not suggest any obfuscated code execution or runtime script fetching.",{"category":92,"check":93,"severity":24,"summary":94},"Portability","Structural Assumption","The skill operates on provided graph data and does not make assumptions about user project structure.",{"category":96,"check":97,"severity":24,"summary":98},"Trust","Issues Attention","There are 0 open issues and 44 closed issues in the last 90 days, indicating active maintenance and responsiveness.",{"category":100,"check":101,"severity":24,"summary":102},"Versioning","Release Management","The SKILL.md frontmatter declares version 1.0.0, and there is a CHANGELOG.md, indicating clear versioning.",{"category":104,"check":105,"severity":24,"summary":106},"Code Execution","Validation","The tool's input schema (nodes, edges, types) implies structured validation, and the output is described as structured JSON.",{"category":66,"check":108,"severity":24,"summary":109},"Unguarded Destructive Operations","The extension performs analysis, not destructive operations, making this check not applicable.",{"category":104,"check":111,"severity":24,"summary":112},"Error Handling","The tool is described as returning structured JSON, implying proper error handling for the agent to process.",{"category":104,"check":114,"severity":115,"summary":116},"Logging","not_applicable","This is an analytical skill and does not perform destructive actions or outbound calls that would require local audit logging.",{"category":118,"check":119,"severity":24,"summary":120},"Compliance","GDPR","The extension analyzes network graphs and does not appear to handle personal data.",{"category":118,"check":122,"severity":24,"summary":123},"Target market","No regional or jurisdictional logic is detected; the extension is globally applicable.",{"category":92,"check":125,"severity":24,"summary":126},"Runtime stability","The skill relies on standard graph analysis algorithms and is not tied to specific OS or shell environments.",{"category":44,"check":128,"severity":24,"summary":129},"README","The README file exists and clearly states the extension's purpose and capabilities.",{"category":33,"check":131,"severity":115,"summary":132},"Tool surface size","The extension exposes a single tool (`analyze_decision_graph`), so the tool surface size is not applicable.",{"category":40,"check":134,"severity":115,"summary":135},"Overlapping near-synonym tools","The extension exposes only one tool, so there are no overlapping near-synonym tools.",{"category":44,"check":137,"severity":24,"summary":138},"Phantom features","All advertised capabilities, such as PageRank and community detection, are reflected in the tool description and metadata.",{"category":140,"check":141,"severity":46,"summary":142},"Install","Installation instruction","While installation instructions for the MCP server and REST API are provided, the requirement for an `ORACLAW_API_KEY` is mentioned without clear steps on how to obtain it or its necessary scopes.",{"category":144,"check":145,"severity":24,"summary":146},"Errors","Actionable error messages","The tool is described as returning structured JSON, which implies actionable error reporting for the agent.",{"category":148,"check":149,"severity":24,"summary":150},"Execution","Pinned dependencies","The presence of `npm install` and the mention of an npm SDK suggest dependency pinning via lockfiles is likely.",{"category":33,"check":152,"severity":115,"summary":153},"Dry-run preview","The extension performs analysis and does not have state-changing operations, making a dry-run capability not applicable.",{"category":155,"check":156,"severity":24,"summary":157},"Protocol","Idempotent retry & timeouts","The extension is analytical and does not involve remote mutating operations, thus timeouts and idempotency are not applicable.",{"category":118,"check":159,"severity":24,"summary":160},"Telemetry opt-in","No telemetry collection is mentioned, thus this check is not applicable.",{"category":40,"check":162,"severity":24,"summary":163},"Precise Purpose","The purpose clearly states the artifact (graph of connected things) and the user intent (network intelligence, analysis of influence, clusters, paths, bottlenecks).",{"category":40,"check":165,"severity":24,"summary":166},"Concise Frontmatter","The frontmatter is concise, provides a clear summary of core capabilities, and includes relevant tags.",{"category":44,"check":168,"severity":24,"summary":169},"Concise Body","The SKILL.md body is concise and directly describes the tool and its usage, avoiding excessive preamble.",{"category":171,"check":172,"severity":115,"summary":173},"Context","Progressive Disclosure","The skill is straightforward with a single tool and does not require complex procedures that would necessitate progressive disclosure.",{"category":171,"check":175,"severity":115,"summary":176},"Forked exploration","The skill performs direct analysis and does not involve deep exploration that would require forking the context.",{"category":22,"check":178,"severity":179,"summary":180},"Usage examples","info","While the README provides examples for the REST API and SDKs, and the SKILL.md lists node/edge types, there are no end-to-end, copy-pasteable examples for the `analyze_decision_graph` tool within the skill context.",{"category":22,"check":182,"severity":179,"summary":183},"Edge cases","The SKILL.md mentions node and edge types and basic requirements, but does not explicitly document failure modes or recovery steps for malformed input or unmet dependencies.",{"category":104,"check":185,"severity":115,"summary":186},"Tool Fallback","This skill uses its own internal tool and does not rely on an external MCP server with fallback paths.",{"category":188,"check":189,"severity":24,"summary":190},"Safety","Halt on unexpected state","The skill operates on provided data and does not perform actions that would require checking for unexpected pre-states in a working tree.",{"category":92,"check":192,"severity":24,"summary":193},"Cross-skill coupling","The skill is self-contained and focuses on graph analysis without implicit reliance on other skills.",1778698992500,"This skill provides network intelligence for AI agents, performing analyses such as PageRank, Louvain community detection, critical path, and bottleneck analysis on graph data. It requires an API key for full functionality.",[197,198,199,200,201],"PageRank score calculation","Louvain community detection","Critical path analysis","Bottleneck node identification","Analysis of decision graphs",[203,204,205],"General-purpose data visualization","Real-time network monitoring","Graph manipulation (adding/deleting nodes/edges)","3.0.0","4.4.0","To equip AI agents with advanced network analysis capabilities, enabling them to understand the structure, influence, and critical components of connected data.","Configuration & parameter reference finding has a warning severity, and Secret Management also has a warning severity. Installation instruction is also a warning.",75,"A robust network analysis skill with clear documentation and a single, well-scoped tool.",[213,214,215,216,217,218],"graph-analytics","network-analysis","pagerank","community-detection","critical-path","bottleneck-analysis","global","community",[222,223,224,225],"Finding most influential nodes in a network","Clustering related items into groups","Determining critical paths between points","Identifying bottleneck nodes in workflows",{"codeQuality":227,"collectedAt":229,"documentation":230,"maintenance":233,"security":239,"testCoverage":242},{"hasLockfile":228},true,1778698975622,{"descriptionLength":231,"readmeSize":232},150,9472,{"closedIssues90d":234,"forks":235,"hasChangelog":228,"manifestVersion":236,"openIssues90d":8,"pushedAt":237,"stars":238},44,2,"1.0.0",1777714123000,8,{"hasNpmPackage":240,"license":241,"smitheryVerified":240},false,"MIT",{"hasCi":228,"hasTests":228},{"updatedAt":244},1778698992613,{"basePath":246,"githubOwner":247,"githubRepo":248,"locale":18,"slug":13,"type":249},"mission-control/packages/clawhub-skills/oraclaw-graph","Whatsonyourmind","oraclaw","skill",null,{"evaluate":252,"extract":254},{"promptVersionExtension":206,"promptVersionScoring":207,"score":210,"tags":253,"targetMarket":219,"tier":220},[213,214,215,216,217,218],{"commitSha":255},"HEAD",{"repoId":257},"kd76fmxm1ng903s4fmj0p7hxxs86ndkg",{"_creationTime":259,"_id":257,"identity":260,"providers":261,"workflow":418},1778698831609.0093,{"githubOwner":247,"githubRepo":248,"sourceUrl":14},{"classify":262,"discover":393,"github":396},{"commitSha":255,"extensions":263},[264,275,283,291,299,307,315,323,331,339,344,352,360,368,376],{"basePath":265,"description":266,"displayName":267,"installMethods":268,"rationale":269,"selectedPaths":270,"source":274,"sourceLanguage":18,"type":249},"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",[271],{"path":272,"priority":273},"SKILL.md","mandatory","rule",{"basePath":276,"description":277,"displayName":278,"installMethods":279,"rationale":280,"selectedPaths":281,"source":274,"sourceLanguage":18,"type":249},"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",[282],{"path":272,"priority":273},{"basePath":284,"description":285,"displayName":286,"installMethods":287,"rationale":288,"selectedPaths":289,"source":274,"sourceLanguage":18,"type":249},"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",[290],{"path":272,"priority":273},{"basePath":292,"description":293,"displayName":294,"installMethods":295,"rationale":296,"selectedPaths":297,"source":274,"sourceLanguage":18,"type":249},"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",[298],{"path":272,"priority":273},{"basePath":300,"description":301,"displayName":302,"installMethods":303,"rationale":304,"selectedPaths":305,"source":274,"sourceLanguage":18,"type":249},"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":272,"priority":273},{"basePath":308,"description":309,"displayName":310,"installMethods":311,"rationale":312,"selectedPaths":313,"source":274,"sourceLanguage":18,"type":249},"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":272,"priority":273},{"basePath":316,"description":317,"displayName":318,"installMethods":319,"rationale":320,"selectedPaths":321,"source":274,"sourceLanguage":18,"type":249},"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":272,"priority":273},{"basePath":324,"description":325,"displayName":326,"installMethods":327,"rationale":328,"selectedPaths":329,"source":274,"sourceLanguage":18,"type":249},"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":272,"priority":273},{"basePath":332,"description":333,"displayName":334,"installMethods":335,"rationale":336,"selectedPaths":337,"source":274,"sourceLanguage":18,"type":249},"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":272,"priority":273},{"basePath":246,"description":10,"displayName":13,"installMethods":340,"rationale":341,"selectedPaths":342,"source":274,"sourceLanguage":18,"type":249},{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-graph/SKILL.md",[343],{"path":272,"priority":273},{"basePath":345,"description":346,"displayName":347,"installMethods":348,"rationale":349,"selectedPaths":350,"source":274,"sourceLanguage":18,"type":249},"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",[351],{"path":272,"priority":273},{"basePath":353,"description":354,"displayName":355,"installMethods":356,"rationale":357,"selectedPaths":358,"source":274,"sourceLanguage":18,"type":249},"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",[359],{"path":272,"priority":273},{"basePath":361,"description":362,"displayName":363,"installMethods":364,"rationale":365,"selectedPaths":366,"source":274,"sourceLanguage":18,"type":249},"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",[367],{"path":272,"priority":273},{"basePath":369,"description":370,"displayName":371,"installMethods":372,"rationale":373,"selectedPaths":374,"source":274,"sourceLanguage":18,"type":249},"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":272,"priority":273},{"basePath":377,"description":378,"displayName":379,"installMethods":380,"license":241,"rationale":381,"selectedPaths":382,"source":274,"sourceLanguage":18,"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":273},"server.json",{"path":386,"priority":273},"package.json",{"path":388,"priority":273},"README.md",{"path":390,"priority":391},"src/index.ts","low","mcp",{"sources":394},[395],"manual",{"closedIssues90d":234,"description":397,"forks":235,"homepage":398,"license":241,"openIssues90d":8,"pushedAt":237,"readmeSize":232,"stars":238,"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. 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Use when the user wants to debug a failed run, audit network/console/DOM activity, attach a trace to an in-progress session, or feed structured per-page summaries back into an agent loop so its next iteration learns from the last one.",{"claudeCode":487},"browserbase/skills","Browser Trace","https://github.com/browserbase/skills",{"basePath":491,"githubOwner":492,"githubRepo":493,"locale":18,"slug":494,"type":249},"skills/browser-trace","browserbase","skills","browser-trace",{"evaluate":496,"extract":503},{"promptVersionExtension":206,"promptVersionScoring":207,"score":466,"tags":497,"targetMarket":219,"tier":448},[498,499,500,501,502,214],"browser-automation","debugging","tracing","cdp","developer-tools",{"commitSha":255,"license":241},{"parentExtensionId":505,"repoId":506},"k17bx77jb71yrwatep2nb43r0d86m6cb","kd77wvcdm5fq9xp8hk6ppm832s86myxr",[498,501,499,502,214,500],{"evaluatedAt":509,"extractAt":510,"updatedAt":509},1778683657573,1778683460321,{"_creationTime":512,"_id":513,"community":514,"display":515,"identity":519,"providers":521,"relations":529,"tags":530,"workflow":531},1778696691708.289,"k17874p2rhttzpn70wfmyx25ms86nd6f",{"reviewCount":8},{"description":516,"installMethods":517,"name":518,"sourceUrl":437},"Agent skill for quorum-manager - invoke with $agent-quorum-manager",{"claudeCode":435},"agent-quorum-manager",{"basePath":520,"githubOwner":440,"githubRepo":441,"locale":18,"slug":518,"type":249},".agents/skills/agent-quorum-manager",{"evaluate":522,"extract":528},{"promptVersionExtension":206,"promptVersionScoring":207,"score":445,"tags":523,"targetMarket":219,"tier":448},[524,525,526,527,214],"distributed-systems","consensus-protocols","quorum-management","fault-tolerance",{"commitSha":255,"license":241},{"repoId":451},[525,524,527,214,526],{"evaluatedAt":532,"extractAt":455,"updatedAt":532},1778698105521,{"_creationTime":534,"_id":535,"community":536,"display":537,"identity":543,"providers":548,"relations":556,"tags":558,"workflow":559},1778691799740.4827,"k17d4qep6a8727yy5y2bjeptcs86nevy",{"reviewCount":8},{"description":538,"installMethods":539,"name":541,"sourceUrl":542},"Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. 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