[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-zxkane-aws-agentic-ai-en":3,"guides-for-zxkane-aws-agentic-ai":461,"similar-k177xhqzyrhtcvj4jsz4pxevhd86n3m2-en":462},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":241,"isFallback":227,"parentExtension":246,"providers":301,"relations":305,"repo":306,"tags":458,"workflow":459},1778699647844.018,"k177xhqzyrhtcvj4jsz4pxevhd86n3m2",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.",{"claudeCode":12},"zxkane/aws-skills","aws-agentic-ai","https://github.com/zxkane/aws-skills",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":225,"workflow":239},1778699758544.5852,"kn73wwty8sfeeee74dnxxfknrs86n91m","en",{"checks":20,"evaluatedAt":191,"extensionSummary":192,"features":193,"nonGoals":199,"promptVersionExtension":203,"promptVersionScoring":204,"purpose":205,"rationale":206,"score":207,"summary":208,"tags":209,"targetMarket":217,"tier":218,"useCases":219},[21,26,29,32,36,39,43,47,50,53,57,61,65,69,72,75,78,81,84,87,90,94,98,102,106,109,112,115,119,122,125,128,131,134,137,141,145,149,152,156,159,162,165,168,172,175,178,181,184,188],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the extension addresses deploying and managing AWS Bedrock AI agents at scale, using specific keywords and listing core services.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill provides comprehensive coverage of AWS Bedrock AgentCore services, including integration workflows, credential management, and governance, going beyond basic API wrappers.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill covers the full lifecycle for deploying and managing AWS Bedrock AI agents, including service selection, deployment patterns, credential management, and discovery.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses specifically on AWS Bedrock AgentCore services and related AWS integrations, maintaining a coherent domain.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The description is detailed, accurate, and clearly reflects the capabilities outlined in the SKILL.md and provided examples.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The skill utilizes narrowly scoped tools, such as specific AWS CLI commands (e.g., `aws bedrock-agentcore-control create-*`) and MCP commands (e.g., `mcp__acdocs__search_agentcore_docs`).",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The SKILL.md and associated references provide detailed documentation for services, workflows, and advanced topics, including credential management patterns and protocol specifications.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","Tool names are descriptive and follow a consistent pattern, like `mcp__acdocs__search_agentcore_docs` or `aws bedrock-agentcore-control create-*`.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","Tools and commands appear to request only necessary parameters, and outputs are focused on the task completion, as inferred from the description and tool examples.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is licensed under the MIT License, which is a permissive open-source license.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The last commit was on April 22, 2026, which is within the last 3 months.",{"category":58,"check":62,"severity":63,"summary":64},"Dependency Management","not_applicable","No third-party dependencies are explicitly mentioned or detected in the provided files that would require special management beyond standard Python packaging.",{"category":66,"check":67,"severity":63,"summary":68},"Security","Secret Management","The extension's purpose and provided scripts do not indicate the use or handling of secrets directly within the agent's code or output.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","The skill focuses on orchestrating AWS services via CLI and MCP, and the documentation does not suggest it loads or executes untrusted third-party code or data directly.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","The provided code templates and skill descriptions do not indicate runtime fetching of external code or data beyond managed AWS services via CLI or MCP.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","The extension interacts with AWS services via CLI and MCP, which operate within their own secure AWS managed environments, not directly within the agent's sandbox.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","No detached processes, retry loops around denied calls, or other sandbox escape primitives are evident in the provided code snippets or descriptions.",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","The skill's purpose is to manage AWS services, and no imperative instructions to read and submit confidential data to a third party are present.",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","The bundled content and descriptions appear free of hidden steering tricks, Unicode tags, or other obfuscation methods.",{"category":66,"check":88,"severity":24,"summary":89},"Opaque code execution","The provided code templates (Python scripts, Dockerfile) are in plain, readable source code, with no evidence of obfuscation or `eval` usage.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The skill assumes standard AWS CLI and AgentCore Runtime environments, and the deployment scripts are designed for broad compatibility.",{"category":95,"check":96,"severity":24,"summary":97},"Trust","Issues Attention","There are 0 open issues and 0 closed issues in the last 90 days, indicating either a new project or no recent activity, which does not raise concerns for this metric.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","The `pushedAt` timestamp indicates recent activity, and the installation instructions reference specific versions or the marketplace, ensuring discoverability.",{"category":103,"check":104,"severity":24,"summary":105},"Code Execution","Validation","The provided Python script templates and descriptions imply standard validation practices for AWS CLI commands and MCP interactions.",{"category":66,"check":107,"severity":24,"summary":108},"Unguarded Destructive Operations","The skill focuses on managing AWS services, and any destructive operations would be guarded by AWS IAM policies and service-specific confirmations, not directly by the skill's code.",{"category":103,"check":110,"severity":24,"summary":111},"Error Handling","The provided code templates and referenced libraries (Strands, FastAPI, boto3) follow standard Python error handling practices, including try-except blocks.",{"category":103,"check":113,"severity":24,"summary":114},"Logging","The provided Python templates include logging configurations, and the observability section of the documentation highlights integration with CloudWatch Logs.",{"category":116,"check":117,"severity":63,"summary":118},"Compliance","GDPR","The skill manages AWS services and does not appear to operate directly on personal data in a way that would trigger GDPR concerns beyond standard AWS service compliance.",{"category":116,"check":120,"severity":24,"summary":121},"Target market","The extension is designed for AWS services, which are globally available, and no regional restrictions are mentioned, so the target market is 'global'.",{"category":91,"check":123,"severity":24,"summary":124},"Runtime stability","The extension relies on standard AWS CLI, Python, Docker, and AgentCore Runtime, which are designed for cross-platform compatibility.",{"category":44,"check":126,"severity":24,"summary":127},"README","A comprehensive README file exists and clearly outlines the extension's purpose, features, installation, and structure.",{"category":33,"check":129,"severity":24,"summary":130},"Tool surface size","The skill exposes a focused set of tools related to AWS Bedrock AgentCore, fitting within the ideal range for manageability.",{"category":40,"check":132,"severity":24,"summary":133},"Overlapping near-synonym tools","The tools utilized (AWS CLI commands and MCP commands) appear to be distinct and cover specific functionalities without significant overlap.",{"category":44,"check":135,"severity":24,"summary":136},"Phantom features","All advertised features, including service coverage and workflow examples, are supported by the SKILL.md and code templates.",{"category":138,"check":139,"severity":24,"summary":140},"Install","Installation instruction","The README provides clear installation instructions for both the marketplace and individual npx methods, along with copy-paste examples.",{"category":142,"check":143,"severity":24,"summary":144},"Errors","Actionable error messages","The provided code templates and documentation suggest that errors would be handled with descriptive messages and potentially recovery steps through AWS or standard Python error practices.",{"category":146,"check":147,"severity":24,"summary":148},"Execution","Pinned dependencies","The provided Python scripts and Dockerfile indicate dependency management via `requirements.txt` or `pyproject.toml` with lock files, suggesting pinned dependencies.",{"category":33,"check":150,"severity":63,"summary":151},"Dry-run preview","The skill's primary function is managing AWS services via CLI and MCP; dry-run capabilities would be inherent to the respective AWS CLI commands or MCP tools if supported, not a direct feature of the skill wrapper itself.",{"category":153,"check":154,"severity":24,"summary":155},"Protocol","Idempotent retry & timeouts","Interactions with AWS services via CLI and MCP typically involve built-in retry mechanisms and timeouts, and the Strands framework handles process management.",{"category":116,"check":157,"severity":24,"summary":158},"Telemetry opt-in","The documentation highlights AgentCore's observability features, which are enabled via environment variables, implying an opt-in mechanism for telemetry collection.",{"category":40,"check":160,"severity":24,"summary":161},"Precise Purpose","The description clearly defines the extension's purpose and target audience, specifying its use with AWS Bedrock AgentCore services and listing relevant keywords.",{"category":40,"check":163,"severity":24,"summary":164},"Concise Frontmatter","The frontmatter in SKILL.md is concise and effectively summarizes the core capabilities and trigger phrases for the AWS Agentic AI skill.",{"category":44,"check":166,"severity":24,"summary":167},"Concise Body","The SKILL.md is well-structured with clear sections and links to detailed references, keeping the main content 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modes.",{"category":103,"check":182,"severity":24,"summary":183},"Tool Fallback","The MCP protocol descriptions indicate standard connection mechanisms, and the existence of multiple protocols (HTTP, MCP, A2A, AG-UI) suggests flexibility and potential fallbacks if one protocol or server is unavailable.",{"category":185,"check":186,"severity":24,"summary":187},"Safety","Halt on unexpected state","Interactions with AWS services via CLI and MCP are generally robust and handle state errors gracefully. The provided templates adhere to standard Python error handling.",{"category":91,"check":189,"severity":24,"summary":190},"Cross-skill coupling","The AWS Agentic AI skill is self-contained, focusing on AgentCore services, and does not appear to implicitly rely on other skills within the repository.",1778699758401,"This skill provides expert guidance and tools for deploying, managing, and integrating AWS Bedrock AgentCore services, including Gateway, Runtime, Memory, and Identity. 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Use when debugging AgentCore agent sessions, tracing tool calls, or analyzing latency.",{"claudeCode":524},"awslabs/mcp","agentcore-investigation","https://github.com/awslabs/mcp",{"basePath":528,"githubOwner":529,"githubRepo":215,"locale":18,"slug":525,"type":245},"src/cloudwatch-mcp-server/skills/agentcore-investigation","awslabs",{"evaluate":531,"extract":538},{"promptVersionExtension":203,"promptVersionScoring":204,"score":532,"tags":533,"targetMarket":217,"tier":274},85,[210,534,211,212,535,536,537],"cloudwatch","logs","debugging","otel",{"commitSha":276},{"repoId":540},"kd71cq56hfddetnwspw92kb09x86mbzy",[212,210,211,534,536,535,537],{"evaluatedAt":543,"extractAt":544,"updatedAt":543},1778683005075,1778682862752,{"_creationTime":546,"_id":547,"community":548,"display":549,"identity":555,"providers":560,"relations":566,"tags":569,"workflow":570},1778696691708.3274,"k170az7r02e9e2v47mpy80kx6n86nff3",{"reviewCount":8},{"description":550,"installMethods":551,"name":553,"sourceUrl":554},"Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy",{"claudeCode":552},"ruvnet/ruflo","Trader Regime","https://github.com/ruvnet/ruflo",{"basePath":556,"githubOwner":557,"githubRepo":558,"locale":18,"slug":559,"type":245},"plugins/ruflo-neural-trader/skills/trader-regime","ruvnet","ruflo","trader-regime",{"evaluate":561,"extract":565},{"promptVersionExtension":203,"promptVersionScoring":204,"score":480,"tags":562,"targetMarket":217,"tier":218},[563,482,564,213,505,292],"finance","market-analysis",{"commitSha":276,"license":237},{"parentExtensionId":567,"repoId":568},"k17drge8h1fgzchr0p4jaeg33n86mwmy","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[213,292,563,564,482,505],{"evaluatedAt":571,"extractAt":572,"updatedAt":571},1778701108877,1778696691708,{"_creationTime":574,"_id":575,"community":576,"display":577,"identity":581,"providers":583,"relations":587,"tags":588,"workflow":589},1778693539593.1863,"k173a67a16bpq0e29wjd85v71986nx03",{"reviewCount":8},{"description":578,"installMethods":579,"name":580,"sourceUrl":472},"Domain knowledge for AI trading memory — Outcome-Weighted Memory (OWM) architecture, 5 memory types, recall scoring, and behavioral analysis. Use when recording trades, recalling similar contexts, analyzing performance, or checking behavioral drift. Triggers on \"record trade\", \"remember trade\", \"recall\", \"similar trades\", \"performance\", \"behavioral\", \"disposition\", \"affective state\", \"confidence\".",{"claudeCode":470},"trading-memory",{"basePath":582,"githubOwner":475,"githubRepo":476,"locale":18,"slug":580,"type":245},"tradememory-plugin/skills/trading-memory",{"evaluate":584,"extract":586},{"promptVersionExtension":203,"promptVersionScoring":204,"score":480,"tags":585,"targetMarket":217,"tier":218},[482,213,483,563,506],{"commitSha":276},{"parentExtensionId":488,"repoId":489},[213,563,483,506,482],{"evaluatedAt":590,"extractAt":493,"updatedAt":591},1778693719816,1778693833320,{"_creationTime":593,"_id":594,"community":595,"display":596,"identity":602,"providers":607,"relations":615,"tags":618,"workflow":619},1778691104676.0042,"k17c25w174y6873nhdh566etts86mv7m",{"reviewCount":8},{"description":597,"installMethods":598,"name":600,"sourceUrl":601},"Transform images with resize, crop, smart crop, upscale, remove background, and 20+ operations.",{"claudeCode":599},"iterationlayer/skills","Image Transformation API","https://github.com/iterationlayer/skills",{"basePath":603,"githubOwner":604,"githubRepo":605,"locale":18,"slug":606,"type":245},"skills/image-transformation-api","iterationlayer","skills","image-transformation-api",{"evaluate":608,"extract":614},{"promptVersionExtension":203,"promptVersionScoring":204,"score":480,"tags":609,"targetMarket":217,"tier":218},[610,611,612,613,213],"image","transformation","editing","api",{"commitSha":276,"license":237},{"parentExtensionId":616,"repoId":617},"k1721s0xmp59902ybtpakrrffn86n10s","kd76p4g2qmtrkgx99cnab3683d86n4g8",[213,613,612,610,611],{"evaluatedAt":620,"extractAt":621,"updatedAt":620},1778693613399,1778691104676]