[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-Whatsonyourmind-oraclaw-solver-en":3,"guides-for-Whatsonyourmind-oraclaw-solver":423,"similar-k179wx4phqshs3khsdvgw86k4d86mjag-en":424},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":243,"isFallback":238,"parentExtension":248,"providers":249,"relations":254,"repo":256,"tags":419,"workflow":420},1778698837670.8013,"k179wx4phqshs3khsdvgw86k4d86mjag",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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.",{"claudeCode":12},"Whatsonyourmind/oraclaw","oraclaw-solver","https://github.com/Whatsonyourmind/oraclaw",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":224,"workflow":241},1778699052902.8213,"kn73w1trg8epk1dvtpq6gmm0mh86ntzs","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":200,"promptVersionExtension":204,"promptVersionScoring":205,"purpose":206,"rationale":207,"score":208,"summary":209,"tags":210,"targetMarket":217,"tier":218,"useCases":219},[21,26,29,32,36,39,43,47,50,53,57,61,64,68,71,74,77,80,83,86,90,94,98,102,106,109,113,117,121,124,127,130,133,136,139,143,147,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem of AI agent scheduling and resource optimization, specifying tasks and constraints.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers significant value over a simple prompt by providing deterministic, mathematically correct optimization solutions for complex problems, moving beyond LLM heuristics.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill provides complete lifecycle coverage for optimization tasks, with clear tools and pricing, suitable for immediate workflow integration.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses on optimization and mathematical problem-solving, adhering to a single domain.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately reflects the skill's capabilities in optimization and scheduling.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","Tools are specifically named (e.g., `solve_schedule`, `solve_constraints`) and focused on distinct optimization tasks.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The SKILL.md provides clear examples of parameters for `solve_schedule` and `solve_constraints`, with default behavior implied by the examples.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","Tool names like `solve_schedule` and `solve_constraints` are descriptive and aligned with the domain.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","Input parameters for tools are structured and specific, and outputs are focused on optimization results, not extraneous data.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The project is licensed under MIT, a permissive open-source license, with a dedicated LICENSE file.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The last commit was on May 2, 2026, indicating recent maintenance.",{"category":58,"check":62,"severity":24,"summary":63},"Dependency Management","The project uses npm and lockfiles, indicating good dependency management practices.",{"category":65,"check":66,"severity":24,"summary":67},"Security","Secret Management","The skill requires an `ORACLAW_API_KEY` which is handled via environment variables, not hardcoded.",{"category":65,"check":69,"severity":24,"summary":70},"Injection","The skill's tools are well-defined and do not appear to execute arbitrary code or load untrusted external instructions.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Supply-Chain Grenades","The skill does not fetch external code or content at runtime; all dependencies appear to be bundled or installed via standard package managers.",{"category":65,"check":75,"severity":24,"summary":76},"Sandbox Isolation","The skill operates within defined scopes (MCP server, API) and does not show signs of attempting to modify files outside its designated project folder.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox escape primitives","No evidence of detached processes or retry loops around denied tool calls was found.",{"category":65,"check":81,"severity":24,"summary":82},"Data Exfiltration","The skill does not instruct the agent to read or submit confidential data to third parties. Outbound calls appear documented.",{"category":65,"check":84,"severity":24,"summary":85},"Hidden Text Tricks","Bundled content and documentation appear free of hidden-steering tricks, control characters, or invisible Unicode tags.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque code execution","The MCP server and SDK packages are implemented in standard TypeScript/JavaScript and are not obfuscated.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The skill makes no assumptions about user project structure, operating independently.",{"category":95,"check":96,"severity":24,"summary":97},"Trust","Issues Attention","With 0 open issues and 44 closed in the last 90 days, the maintainer engagement is excellent.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","The `version` field in SKILL.md frontmatter is set to '1.0.0', indicating clear versioning.",{"category":103,"check":104,"severity":24,"summary":105},"Execution","Validation","The tool inputs are structured JSON, implying validation through schema adherence by the MCP server or API.",{"category":65,"check":107,"severity":24,"summary":108},"Unguarded Destructive Operations","The skill performs optimization calculations and does not involve destructive operations.",{"category":110,"check":111,"severity":24,"summary":112},"Code Execution","Error Handling","The tools are designed to return structured results, implying robust error handling for infeasible problems or other issues.",{"category":110,"check":114,"severity":115,"summary":116},"Logging","not_applicable","The skill is primarily an API/MCP server providing structured output, not a destructive or complex process requiring local audit logging.",{"category":118,"check":119,"severity":24,"summary":120},"Compliance","GDPR","The skill operates on abstract mathematical problems and does not process personal data.",{"category":118,"check":122,"severity":24,"summary":123},"Target market","The skill's optimization capabilities are universally applicable, and no regional restrictions were detected.",{"category":91,"check":125,"severity":24,"summary":126},"Runtime stability","The skill operates as an API or MCP server and relies on standard web technologies and Node.js, ensuring broad compatibility.",{"category":44,"check":128,"severity":24,"summary":129},"README","The README is comprehensive, well-structured, and clearly explains the skill's purpose and capabilities.",{"category":33,"check":131,"severity":24,"summary":132},"Tool surface size","The MCP tool catalog lists 17 tools, which is within the acceptable range and well-documented.",{"category":40,"check":134,"severity":24,"summary":135},"Overlapping near-synonym tools","The listed tools have distinct names and functionalities, avoiding redundancy.",{"category":44,"check":137,"severity":24,"summary":138},"Phantom features","All advertised features, including specific tools and API endpoints, are implemented and documented.",{"category":140,"check":141,"severity":24,"summary":142},"Install","Installation instruction","Clear installation instructions are provided for MCP server, REST API, and npm SDK, with copy-paste examples.",{"category":144,"check":145,"severity":24,"summary":146},"Errors","Actionable error messages","The API and tool documentation implies structured responses, which would include actionable error messages for infeasible problems or other issues.",{"category":103,"check":148,"severity":24,"summary":149},"Pinned dependencies","The project indicates npm usage and has a lockfile, suggesting pinned dependencies.",{"category":33,"check":151,"severity":115,"summary":152},"Dry-run preview","The skill performs deterministic calculations and does not involve state-changing operations or outbound data transmission that would require a dry-run feature.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The tools are stateless calculations, making them inherently idempotent. API documentation implies timely responses.",{"category":118,"check":158,"severity":24,"summary":159},"Telemetry opt-in","No telemetry is mentioned, and the skill's design suggests it does not collect user data.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The `SKILL.md` precisely defines the purpose of scheduling and optimization, including artifacts and user intents like planning and allocating.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter in `SKILL.md` is concise, clearly stating the core capability and providing trigger phrases.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The `SKILL.md` is concise, detailing the core functionality and using examples effectively.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","The `SKILL.md` is concise, with detailed examples and tool descriptions, aligning with progressive disclosure principles.",{"category":170,"check":174,"severity":115,"summary":175},"Forked exploration","This skill performs direct calculations and does not involve deep exploration or code review that would necessitate `context: fork`.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The `SKILL.md` and README provide clear, ready-to-use JSON examples for both scheduling and custom constraint optimization.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The `SKILL.md` mentions infeasible problems and how to handle them by relaxing constraints, covering a key edge case.",{"category":110,"check":183,"severity":115,"summary":184},"Tool Fallback","The skill does not rely on external MCP servers or tools that would require a fallback mechanism.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The deterministic nature of the solver and the explicit handling of infeasible problems suggest that unexpected states would halt the workflow appropriately.",{"category":91,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill is self-contained, focusing on optimization tasks and does not appear to implicitly rely on other skills.",1778699052626,"This skill provides deterministic optimization solutions for AI agents, capable of handling task scheduling, budget allocation, and general LP/MIP constraint problems through specific tools like `solve_schedule` and `solve_constraints`. It includes a REST API and SDKs.",[195,196,197,198,199],"Industrial-grade scheduling with energy matching","Budget allocation with custom constraints","LP/MIP solver for optimization problems","Deterministic and millisecond-fast results","REST API and SDKs for broad integration",[201,202,203],"Performing heuristic-based or approximate solutions","Replacing LLM-based reasoning for non-mathematical tasks","Handling tasks that are not mathematically optimizable","3.0.0","4.4.0","To provide AI agents with mathematically precise and deterministic optimization capabilities, enabling them to solve complex scheduling and resource allocation problems efficiently and accurately.","The extension is highly polished, with excellent documentation, clear purpose, and robust implementation covering complex optimization tasks. All checks passed with a high degree of quality.",100,"A robust and well-documented skill for industrial-grade AI agent scheduling and resource optimization.",[211,212,213,214,215,216],"optimization","scheduling","linear-programming","resource-allocation","operations-research","planning","global","verified",[220,221,222,223],"Planning daily/weekly schedules matching tasks to energy levels","Allocating budget across competing priorities with hard constraints","Solving resource allocation problems with limited capacity","Optimizing staffing, routing, or capacity planning",{"codeQuality":225,"collectedAt":227,"documentation":228,"maintenance":231,"security":237,"testCoverage":240},{"hasLockfile":226},true,1778699040327,{"descriptionLength":229,"readmeSize":230},182,9472,{"closedIssues90d":232,"forks":233,"hasChangelog":226,"manifestVersion":234,"openIssues90d":8,"pushedAt":235,"stars":236},44,2,"1.0.0",1777714123000,8,{"hasNpmPackage":238,"license":239,"smitheryVerified":238},false,"MIT",{"hasCi":226,"hasTests":226},{"updatedAt":242},1778699052902,{"basePath":244,"githubOwner":245,"githubRepo":246,"locale":18,"slug":13,"type":247},"mission-control/packages/clawhub-skills/oraclaw-solver","Whatsonyourmind","oraclaw","skill",null,{"evaluate":250,"extract":252},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":251,"targetMarket":217,"tier":218},[211,212,213,214,215,216],{"commitSha":253},"HEAD",{"repoId":255},"kd76fmxm1ng903s4fmj0p7hxxs86ndkg",{"_creationTime":257,"_id":255,"identity":258,"providers":259,"workflow":415},1778698831609.0093,{"githubOwner":245,"githubRepo":246,"sourceUrl":14},{"classify":260,"discover":391,"github":394},{"commitSha":253,"extensions":261},[262,273,281,289,297,305,313,321,329,337,345,353,361,369,374],{"basePath":263,"description":264,"displayName":265,"installMethods":266,"rationale":267,"selectedPaths":268,"source":272,"sourceLanguage":18,"type":247},"mission-control/packages/clawhub-skills/oraclaw-anomaly","Anomaly detection for AI agents. Z-score, IQR, and streaming detection. Find outliers in data instantly. Sub-millisecond response. Works on single values or full datasets.","oraclaw-anomaly",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-anomaly/SKILL.md",[269],{"path":270,"priority":271},"SKILL.md","mandatory","rule",{"basePath":274,"description":275,"displayName":276,"installMethods":277,"rationale":278,"selectedPaths":279,"source":272,"sourceLanguage":18,"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",[280],{"path":270,"priority":271},{"basePath":282,"description":283,"displayName":284,"installMethods":285,"rationale":286,"selectedPaths":287,"source":272,"sourceLanguage":18,"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",[288],{"path":270,"priority":271},{"basePath":290,"description":291,"displayName":292,"installMethods":293,"rationale":294,"selectedPaths":295,"source":272,"sourceLanguage":18,"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",[296],{"path":270,"priority":271},{"basePath":298,"description":299,"displayName":300,"installMethods":301,"rationale":302,"selectedPaths":303,"source":272,"sourceLanguage":18,"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",[304],{"path":270,"priority":271},{"basePath":306,"description":307,"displayName":308,"installMethods":309,"rationale":310,"selectedPaths":311,"source":272,"sourceLanguage":18,"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",[312],{"path":270,"priority":271},{"basePath":314,"description":315,"displayName":316,"installMethods":317,"rationale":318,"selectedPaths":319,"source":272,"sourceLanguage":18,"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",[320],{"path":270,"priority":271},{"basePath":322,"description":323,"displayName":324,"installMethods":325,"rationale":326,"selectedPaths":327,"source":272,"sourceLanguage":18,"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",[328],{"path":270,"priority":271},{"basePath":330,"description":331,"displayName":332,"installMethods":333,"rationale":334,"selectedPaths":335,"source":272,"sourceLanguage":18,"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",[336],{"path":270,"priority":271},{"basePath":338,"description":339,"displayName":340,"installMethods":341,"rationale":342,"selectedPaths":343,"source":272,"sourceLanguage":18,"type":247},"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",[344],{"path":270,"priority":271},{"basePath":346,"description":347,"displayName":348,"installMethods":349,"rationale":350,"selectedPaths":351,"source":272,"sourceLanguage":18,"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",[352],{"path":270,"priority":271},{"basePath":354,"description":355,"displayName":356,"installMethods":357,"rationale":358,"selectedPaths":359,"source":272,"sourceLanguage":18,"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",[360],{"path":270,"priority":271},{"basePath":362,"description":363,"displayName":364,"installMethods":365,"rationale":366,"selectedPaths":367,"source":272,"sourceLanguage":18,"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",[368],{"path":270,"priority":271},{"basePath":244,"description":10,"displayName":13,"installMethods":370,"rationale":371,"selectedPaths":372,"source":272,"sourceLanguage":18,"type":247},{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-solver/SKILL.md",[373],{"path":270,"priority":271},{"basePath":375,"description":376,"displayName":377,"installMethods":378,"license":239,"rationale":379,"selectedPaths":380,"source":272,"sourceLanguage":18,"type":390},"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":377},"server.json with namespace/server name at mission-control/packages/mcp-server/server.json",[381,383,385,387],{"path":382,"priority":271},"server.json",{"path":384,"priority":271},"package.json",{"path":386,"priority":271},"README.md",{"path":388,"priority":389},"src/index.ts","low","mcp",{"sources":392},[393],"manual",{"closedIssues90d":232,"description":395,"forks":233,"homepage":396,"license":239,"openIssues90d":8,"pushedAt":235,"readmeSize":230,"stars":236,"topics":397},"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",[398,399,400,401,402,403,404,390,211,405,406,407,408,409,410,213,411,412,413,414],"ai-agents","algorithms","api","bandits","decision-intelligence","fastify","machine-learning","typescript","agent-tools","anthropic","claude-mcp","contextual-bandit","deterministic-tools","llm-tools","model-context-protocol","monte-carlo","pagerank",{"classifiedAt":416,"discoverAt":417,"extractAt":418,"githubAt":418,"updatedAt":416},1778698837409,1778698831609,1778698835357,[213,215,211,216,214,212],{"evaluatedAt":242,"extractAt":421,"updatedAt":422},1778698837670,1778699188962,[],[425,455,474,505,533,561],{"_creationTime":426,"_id":427,"community":428,"display":429,"identity":435,"providers":440,"relations":449,"tags":451,"workflow":452},1778696691708.3035,"k17br1j5s86ae90zqeyd7zcg2586mkwr",{"reviewCount":8},{"description":430,"installMethods":431,"name":433,"sourceUrl":434},"Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms\n",{"claudeCode":432},"ruvnet/ruflo","Performance Analysis","https://github.com/ruvnet/ruflo",{"basePath":436,"githubOwner":437,"githubRepo":438,"locale":18,"slug":439,"type":247},".claude/skills/performance-analysis","ruvnet","ruflo","performance-analysis",{"evaluate":441,"extract":448},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":442,"targetMarket":217,"tier":218},[443,444,211,445,446,447],"performance","analysis","claude-flow","bottleneck-detection","reporting",{"commitSha":253,"license":239},{"repoId":450},"kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[444,446,445,211,443,447],{"evaluatedAt":453,"extractAt":454,"updatedAt":453},1778699217174,1778696691708,{"_creationTime":456,"_id":457,"community":458,"display":459,"identity":461,"providers":462,"relations":469,"tags":470,"workflow":471},1778698837670.7993,"k17fe7ybjme5s1n10mmg3emmns86nr26",{"reviewCount":8},{"description":307,"installMethods":460,"name":308,"sourceUrl":14},{"claudeCode":12},{"basePath":306,"githubOwner":245,"githubRepo":246,"locale":18,"slug":308,"type":247},{"evaluate":463,"extract":468},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":464,"targetMarket":217,"tier":218},[465,444,211,466,467],"decision-making","graph-theory","ai-agent-tools",{"commitSha":253},{"repoId":255},[467,444,465,466,211],{"evaluatedAt":472,"extractAt":421,"updatedAt":473},1778698934136,1778699187402,{"_creationTime":475,"_id":476,"community":477,"display":478,"identity":484,"providers":489,"relations":497,"tags":500,"workflow":501},1778694149049.3467,"k175wmq2n17n23xzphj2zzt3qs86n3xd",{"reviewCount":8},{"description":479,"installMethods":480,"name":482,"sourceUrl":483},"Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.",{"claudeCode":481},"mongodb/agent-skills","MongoDB Connection Optimizer","https://github.com/mongodb/agent-skills",{"basePath":485,"githubOwner":486,"githubRepo":487,"locale":18,"slug":488,"type":247},"skills/mongodb-connection","mongodb","agent-skills","mongodb-connection",{"evaluate":490,"extract":495},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":491,"targetMarket":217,"tier":218},[486,492,493,443,211,494],"database","connection","configuration",{"commitSha":253,"license":496},"Apache-2.0",{"parentExtensionId":498,"repoId":499},"k170hje3xzpy2mbkn00agzm38x86mkbz","kd74vahs1zbjqzqbert490xyrd86nfv5",[494,493,492,486,211,443],{"evaluatedAt":502,"extractAt":503,"updatedAt":504},1778694243014,1778694149049,1778694461446,{"_creationTime":506,"_id":507,"community":508,"display":509,"identity":515,"providers":520,"relations":526,"tags":529,"workflow":530},1778692488329.0164,"k17d7dgccv6vnj9tcw0cehwnph86nryb",{"reviewCount":8},{"description":510,"installMethods":511,"name":513,"sourceUrl":514},"Analyze which rules are actively used vs inert. Detect coverage gaps. 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