[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AEKE1EJDSW9K8EW0MCNJ2C":3,"skill-same:01M6AEKE1EJDSW9K8EW0MCNJ2C":108},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":51,"ownerHtmlUrl":52,"ownerBlogUrl":53,"ownerDescription":54,"updatedAt":55,"install":56,"resources":69},"01M6AEKE1EJDSW9K8EW0MCNJ2C","google-cloud-networking-observability",null,"Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs, NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33602,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fgoogle-cloud-networking-observability\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"google cloud","cloud","observability","networking","google cloud networking observability","google","diagnostics","issues","apache","metrics","logs","analyzing","flow","firewall","nat","vpc","threat","throughput","path","when","2026-11-10 10:12:30",false,"---\nname: google-cloud-networking-observability\ndescription: Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs, NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics.\nmetadata:\n  source: google\u002Fskills (Apache 2.0)\n---\n\n# Google Cloud Networking Observability Expert\n\n## Core Directive: Results First\n\n1.  **Identify the Primary Source**: Quickly determine if the user needs\n    firewall logs, threat logs, Cloud NAT, VPC Flow logs, or metrics.\n2.  **Execute & Present**: Perform the minimum required query to get a direct\n    answer.\n3.  **Definitive Termination**: Once you identify the requested data, regardless\n    of the value (including 0, null, or \"No traffic\"), present the finding and\n    call the finish tool in the same turn. Do NOT attempt to find \"active\" or\n    \"busier\" resources to provide a \"better\" answer unless specifically\n    instructed to troubleshoot a resource that is expected to be busy.\n\n## Log & Telemetry Overview\n\n-   **Threat Logs**: Specialized logs from Cloud Firewall Plus and Cloud IDS\n    that identify malicious traffic patterns (for example, SQL injection or\n    malware) using deep packet inspection.\n-   **VPC Flow Logs**: Capture sample IP traffic to and from network interfaces.\n    Use for traffic analysis, volume trends, and top talkers.\n-   **Firewall Logs**: Record connection attempts matched by firewall rules. Use\n    to identify \"DENY\" events or verify \"ALLOW\" rules.\n-   **Cloud NAT Logs**: Audit NAT translations. Use to audit traffic going\n    through NAT gateways or troubleshoot port exhaustion.\n-   **Networking Metrics**: Aggregated time-series data for throughput, RTT\n    (latency), and packet loss. Use for historical trends and performance\n    monitoring.\n-   **Connectivity Tests**: Static analysis tool for path diagnostics. Use to\n    identify firewall or routing misconfigurations between endpoints.\n\n## Procedures\n\n### 0. Log Source Preference\n\n-   **ALWAYS** check for BigQuery linked datasets (for example,\n    `big_query_linked_dataset`, `_AllLogs`) before using Cloud Logging for\n    high-volume analysis or aggregations. This is the preferred method for\n    finding trends or top-blocking rules.\n-   **Metadata Awareness (BigQuery)**: Subnetworks may be configured with\n    `EXCLUDE_ALL_METADATA`, causing VM names to be NULL in VPC Flow Logs. If a\n    query by VM name returns nothing, retry using the internal IP address\n    (`jsonPayload.connection.src_ip`).\n\n### 1. Tool Selection & Discovery\n\n-   **MCP Servers First**: Use\n    [Cloud Monitoring MCP](references\u002Fmcp-usage.md#cloud-monitoring-mcp),\n    [BigQuery MCP](references\u002Fmcp-usage.md#bigquery-mcp), or\n    [Cloud Logging MCP](references\u002Fmcp-usage.md#cloud-logging-mcp).\n-   **Resource Discovery**: If a user-specified resource (for example, NAT\n    gateway, VPN tunnel) is not found in metrics\u002Flogs:\n    1.  Use `run_shell_command` with `gcloud` to list resources in the project.\n    2.  Search [Cloud Logging MCP](references\u002Fmcp-usage.md#cloud-logging-mcp)\n        for the resource name to find correct labels.\n-   **CLI Fallback**: Use `gcloud` or `bq` only if MCP servers are unavailable.\n    DO NOT use gcloud monitoring; it is restricted. Immediately use the curl\n    templates in [metrics-analysis.md](references\u002Fmetrics-analysis.md).\n\n### 2. Schema Verification & Error Recovery\n\nIf a BigQuery query fails with an 'Unrecognized name' error or schema mismatch:\n1. **Validate Schema**: Run `bq show --schema --format=json\n{project_id}:{dataset_id}.{table_id}` to verify field names and casing (for\nexample, `jsonPayload` versus `json_payload`). 2. **Dry Run**: Before executing\na corrected query, use `bq query --use_legacy_sql=false --dry_run\n\"{query_text}\"` to verify field references without incurring cost or execution\ntime. 3. **Retry**: Apply identified fixes to the original query and execute.\n\n### 3. Analysis Guides (Read Only When Needed)\n\nFor detailed SQL patterns, field definitions, and advanced troubleshooting, read\nthe corresponding reference file:\n\n-   **Threat Log Analysis**:\n    [references\u002Fthreat-analysis.md](references\u002Fthreat-analysis.md)\n-   **VPC Flow Analysis**:\n    [references\u002Fvpc-flow-analysis.md](references\u002Fvpc-flow-analysis.md)\n-   **Cloud NAT Analysis**:\n    [references\u002Fcloud-nat-analysis.md](references\u002Fcloud-nat-analysis.md)\n-   **Firewall Rule Analysis**:\n    [references\u002Ffirewall-analysis.md](references\u002Ffirewall-analysis.md)\n-   **Networking Metrics**:\n    [references\u002Fmetrics-analysis.md](references\u002Fmetrics-analysis.md)\n-   **Connectivity Test Analysis**:\n    [references\u002Fconnectivity-tests.md](references\u002Fconnectivity-tests.md)\n\n## Boundaries (CRITICAL)\n\n-   **ALWAYS** present the direct answer as soon as it is identified.\n-   **NEVER** run more than 2 exploratory queries before showing results.\n-   **NEVER** perform secondary verification (for example, don't check VPC flows\n    after finding a firewall block) without explicit user permission.\n-   **ALWAYS** print the generated SQL for review before execution.\n-   **ALWAYS** include a link to the Flow Analyzer in the\n    [Google Cloud Console](https:\u002F\u002Fconsole.cloud.google.com\u002Fnet-intelligence\u002Fflow-analyzer).\n-   **NEVER** query a second data source (such as, BigQuery logs) if the primary\n    source (for example, Cloud Monitoring metrics) has already provided a\n    conclusive answer. **DO NOT** compare metrics and logs to \"verify\" accuracy\n    unless the user specifically asks why they differ.\n-   **NO DISCREPANCY LOOPS**: If Tool A provides a result (such as, 80,000\n    counts) and Tool B provides a different result (for example, 1,000 counts),\n    **DO NOT** initiate a deep dive to explain the difference. Present the\n    result from the primary tool and STOP.\n-   **ALWAYS** perform time-range calculations (such as, \"12 hours ago\") during\n    the first turn to save steps.\n-   **Conclusive Acceptance of Inactivity**: Treat a result of \"0\", \"0 traffic\",\n    \"No data found\", or \"No records found\" as a conclusive finding for the\n    requested timeframe and resource. You MUST report this as the definitive\n    state and terminate immediately.\n-   **Standardized Discovery Path**: For all \"Top-N\" or volume-based discovery\n    tasks (for example, \"highest traffic,\" \"most hits,\" \"top talkers\"), you MUST\n    use BigQuery aggregation on _AllLogs datasets. Manual aggregation of\n    individual time-series points using the Monitoring API is forbidden due to\n    step inefficiency.\n-   **Ban on Auxiliary Scripting**: Execute all data retrieval and parsing logic\n    as direct tool calls (bq, curl, gcloud). Do NOT write or execute local shell\n    scripts (.sh) or python files, as these introduce avoidable environment and\n    permission errors that lead to investigation timeouts.\n-   **Discovery Efficiency**: For volume analysis (for example, \"how many\n    connections\" or \"top IPs by bytes\"), BigQuery aggregation on VPC Flow logs\n    (_AllLogs) is the **Primary Source of Truth**. If BigQuery data is\n    available, it is conclusive. Do NOT query Monitoring API to \"double check\"\n    BigQuery counts.\n","# Google Cloud Networking Observability Expert\n\n## Core Directive: Results First\n\n1.  **Identify the Primary Source**: Quickly determine if the user needs\n    firewall logs, threat logs, Cloud NAT, VPC Flow logs, or metrics.\n2.  **Execute & Present**: Perform the minimum required query to get a direct\n    answer.\n3.  **Definitive Termination**: Once you identify the requested data, regardless\n    of the value (including 0, null, or \"No traffic\"), present the finding and\n    call the finish tool in the same turn. Do NOT attempt to find \"active\" or\n    \"busier\" resources to provide a \"better\" answer unless specifically\n    instructed to troubleshoot a resource that is expected to be busy.\n\n## Log & Telemetry Overview\n\n-   **Threat Logs**: Specialized logs from Cloud Firewall Plus and Cloud IDS\n    that identify malicious traffic patterns (for example, SQL injection or\n    malware) using deep packet inspection.\n-   **VPC Flow Logs**: Capture sample IP traffic to and from network interfaces.\n    Use for traffic analysis, volume trends, and top talkers.\n-   **Firewall Logs**: Record connection attempts matched by firewall rules. Use\n    to identify \"DENY\" events or verify \"ALLOW\" rules.\n-   **Cloud NAT Logs**: Audit NAT translations. Use to audit traffic going\n    through NAT gateways or troubleshoot port exhaustion.\n-   **Networking Metrics**: Aggregated time-series data for throughput, RTT\n    (latency), and packet loss. Use for historical trends and performance\n    monitoring.\n-   **Connectivity Tests**: Static analysis tool for path diagnostics. Use to\n    identify firewall or routing misconfigurations between endpoints.\n\n## Procedures\n\n### 0. Log Source Preference\n\n-   **ALWAYS** check for BigQuery linked datasets (for example,\n    `big_query_linked_dataset`, `_AllLogs`) before using Cloud Logging for\n    high-volume analysis or aggregations. This is the preferred method for\n    finding trends or top-blocking rules.\n-   **Metadata Awareness (BigQuery)**: Subnetworks may be configured with\n    `EXCLUDE_ALL_METADATA`, causing VM names to be NULL in VPC Flow Logs. If a\n    query by VM name returns nothing, retry using the internal IP address\n    (`jsonPayload.connection.src_ip`).\n\n### 1. Tool Selection & Discovery\n\n-   **MCP Servers First**: Use\n    [Cloud Monitoring MCP](references\u002Fmcp-usage.md#cloud-monitoring-mcp),\n    [BigQuery MCP](references\u002Fmcp-usage.md#bigquery-mcp), or\n    [Cloud Logging MCP](references\u002Fmcp-usage.md#cloud-logging-mcp).\n-   **Resource Discovery**: If a user-specified resource (for example, NAT\n    gateway, VPN tunnel) is not found in metrics\u002Flogs:\n    1.  Use `run_shell_command` with `gcloud` to list resources in the project.\n    2.  Search [Cloud Logging MCP](references\u002Fmcp-usage.md#cloud-logging-mcp)\n        for the resource name to find correct labels.\n-   **CLI Fallback**: Use `gcloud` or `bq` only if MCP servers are unavailable.\n    DO NOT use gcloud monitoring; it is restricted. Immediately use the curl\n    templates in [metrics-analysis.md](references\u002Fmetrics-analysis.md).\n\n### 2. Schema Verification & Error Recovery\n\nIf a BigQuery query fails with an 'Unrecognized name' error or schema mismatch:\n1. **Validate Schema**: Run `bq show --schema --format=json\n{project_id}:{dataset_id}.{table_id}` to verify field names and casing (for\nexample, `jsonPayload` versus `json_payload`). 2. **Dry Run**: Before executing\na corrected query, use `bq query --use_legacy_sql=false --dry_run\n\"{query_text}\"` to verify field references without incurring cost or execution\ntime. 3. **Retry**: Apply identified fixes to the original query and execute.\n\n### 3. Analysis Guides (Read Only When Needed)\n\nFor detailed SQL patterns, field definitions, and advanced troubleshooting, read\nthe corresponding reference file:\n\n-   **Threat Log Analysis**:\n    [references\u002Fthreat-analysis.md](references\u002Fthreat-analysis.md)\n-   **VPC Flow Analysis**:\n    [references\u002Fvpc-flow-analysis.md](references\u002Fvpc-flow-analysis.md)\n-   **Cloud NAT Analysis**:\n    [references\u002Fcloud-nat-analysis.md](references\u002Fcloud-nat-analysis.md)\n-   **Firewall Rule Analysis**:\n    [references\u002Ffirewall-analysis.md](references\u002Ffirewall-analysis.md)\n-   **Networking Metrics**:\n    [references\u002Fmetrics-analysis.md](references\u002Fmetrics-analysis.md)\n-   **Connectivity Test Analysis**:\n    [references\u002Fconnectivity-tests.md](references\u002Fconnectivity-tests.md)\n\n## Boundaries (CRITICAL)\n\n-   **ALWAYS** present the direct answer as soon as it is identified.\n-   **NEVER** run more than 2 exploratory queries before showing results.\n-   **NEVER** perform secondary verification (for example, don't check VPC flows\n    after finding a firewall block) without explicit user permission.\n-   **ALWAYS** print the generated SQL for review before execution.\n-   **ALWAYS** include a link to the Flow Analyzer in the\n    [Google Cloud Console](https:\u002F\u002Fconsole.cloud.google.com\u002Fnet-intelligence\u002Fflow-analyzer).\n-   **NEVER** query a second data source (such as, BigQuery logs) if the primary\n    source (for example, Cloud Monitoring metrics) has already provided a\n    conclusive answer. **DO NOT** compare metrics and logs to \"verify\" accuracy\n    unless the user specifically asks why they differ.\n-   **NO DISCREPANCY LOOPS**: If Tool A provides a result (such as, 80,000\n    counts) and Tool B provides a different result (for example, 1,000 counts),\n    **DO NOT** initiate a deep dive to explain the difference. Present the\n    result from the primary tool and STOP.\n-   **ALWAYS** perform time-range calculations (such as, \"12 hours ago\") during\n    the first turn to save steps.\n-   **Conclusive Acceptance of Inactivity**: Treat a result of \"0\", \"0 traffic\",\n    \"No data found\", or \"No records found\" as a conclusive finding for the\n    requested timeframe and resource. You MUST report this as the definitive\n    state and terminate immediately.\n-   **Standardized Discovery Path**: For all \"Top-N\" or volume-based discovery\n    tasks (for example, \"highest traffic,\" \"most hits,\" \"top talkers\"), you MUST\n    use BigQuery aggregation on _AllLogs datasets. Manual aggregation of\n    individual time-series points using the Monitoring API is forbidden due to\n    step inefficiency.\n-   **Ban on Auxiliary Scripting**: Execute all data retrieval and parsing logic\n    as direct tool calls (bq, curl, gcloud). Do NOT write or execute local shell\n    scripts (.sh) or python files, as these introduce avoidable environment and\n    permission errors that lead to investigation timeouts.\n-   **Discovery Efficiency**: For volume analysis (for example, \"how many\n    connections\" or \"top IPs by bytes\"), BigQuery aggregation on VPC Flow logs\n    (_AllLogs) is the **Primary Source of Truth**. If BigQuery data is\n    available, it is conclusive. Do NOT query Monitoring API to \"double check\"\n    BigQuery counts.\n",{"name":5,"metadata":44,"description":7},{"source":45},"google\u002Fskills (Apache 2.0)","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fgoogle-cloud-networking-observability",3963,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-15 01:32:48",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add davila7\u002Fclaude-code-templates --skill google-cloud-networking-observability",{"tool":62,"command":63},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill google-cloud-networking-observability",{"tool":65,"command":66},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill google-cloud-networking-observability","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fgoogle-cloud-networking-observability\u002FSKILL.md",true,[70,76,80,84,88,92,96,100,104],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"agents\u002Fopenai.yaml","REFERENCE","参考文档",117,"https:\u002F\u002Fgithub.com\u002Ftwaldin\u002Fdotfiles\u002Fblob\u002Fmain\u002Fagents\u002Fskills\u002Fbrowser\u002Fagents\u002Fopenai.yaml",{"path":77,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":78,"sourceHtmlUrl":79},"fallbacks.md",660,"https:\u002F\u002Fgithub.com\u002Ftwaldin\u002Fdotfiles\u002Fblob\u002Fmain\u002Fagents\u002Fskills\u002Fbrowser\u002Ffallbacks.md",{"path":81,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fcloud-nat-analysis.md",2767,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fgoogle-cloud-networking-observability\u002Freferences\u002Fcloud-nat-analysis.md",{"path":85,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fconnectivity-tests.md",1411,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fgoogle-cloud-networking-observability\u002Freferences\u002Fconnectivity-tests.md",{"path":89,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Ffirewall-analysis.md",3080,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fgoogle-cloud-networking-observability\u002Freferences\u002Ffirewall-analysis.md",{"path":93,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":94,"sourceHtmlUrl":95},"references\u002Fmcp-usage.md",2617,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fgoogle-cloud-networking-observability\u002Freferences\u002Fmcp-usage.md",{"path":97,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":98,"sourceHtmlUrl":99},"references\u002Fmetrics-analysis.md",3078,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fgoogle-cloud-networking-observability\u002Freferences\u002Fmetrics-analysis.md",{"path":101,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":102,"sourceHtmlUrl":103},"references\u002Fthreat-analysis.md",5787,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fgoogle-cloud-networking-observability\u002Freferences\u002Fthreat-analysis.md",{"path":105,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":106,"sourceHtmlUrl":107},"references\u002Fvpc-flow-analysis.md",4904,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fgoogle-cloud-networking-observability\u002Freferences\u002Fvpc-flow-analysis.md",[109,140,167,200,235,258],{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":120,"publishedAt":39},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[115],{"categoryNo":116,"categoryName":117,"taxonomyType":118,"taxonomyTypeDesc":119},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[121,122,123,124,125,126,127,128,129,38,130,131,132,133,134,135,136,137,138,139],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":147,"publishedAt":39},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[146],{"categoryNo":116,"categoryName":117,"taxonomyType":118,"taxonomyTypeDesc":119},[148,149,150,151,152,153,154,155,156,157,158,38,159,160,161,162,163,164,165,166],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":182,"publishedAt":39},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[173,176,177],{"categoryNo":174,"categoryName":175,"taxonomyType":118,"taxonomyTypeDesc":119},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":116,"categoryName":117,"taxonomyType":118,"taxonomyTypeDesc":119},{"categoryNo":178,"categoryName":179,"taxonomyType":180,"taxonomyTypeDesc":181},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[183,184,185,186,187,188,189,126,190,191,192,193,194,195,196,129,197,198,38,199],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":204,"licenseFileUrl":205,"sourceHtmlUrl":206,"categories":207,"keywords":211,"publishedAt":39},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[208],{"categoryNo":209,"categoryName":210,"taxonomyType":118,"taxonomyTypeDesc":119},"01JBSEEDCAT000000000000009","前端开发",[212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,126,234],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":236,"name":237,"nameZh":6,"description":238,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":204,"licenseFileUrl":239,"sourceHtmlUrl":240,"categories":241,"keywords":246,"publishedAt":39},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[242,245],{"categoryNo":243,"categoryName":244,"taxonomyType":118,"taxonomyTypeDesc":119},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":209,"categoryName":210,"taxonomyType":118,"taxonomyTypeDesc":119},[247,248,249,250,251,213,252,222,223,253,254,29,255,126,256,131,257],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","screenshots","local","behavior",{"skillNo":259,"name":260,"nameZh":6,"description":261,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":204,"licenseFileUrl":262,"sourceHtmlUrl":263,"categories":264,"keywords":272,"publishedAt":39},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[265,268,269],{"categoryNo":266,"categoryName":267,"taxonomyType":118,"taxonomyTypeDesc":119},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":116,"categoryName":117,"taxonomyType":118,"taxonomyTypeDesc":119},{"categoryNo":270,"categoryName":271,"taxonomyType":180,"taxonomyTypeDesc":181},"01JBSEEDCAT000000000000036","内容创作者",[273,274,275,276,277,278,279,280,215,281,225,159,282,283,284,285,286,195,287,38,288,289,290,291,292,137,293],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]