[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F67XWDA0SV92AFFVMNSYVH":3,"skill-same:01M6F67XWDA0SV92AFFVMNSYVH":74},{"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":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":57,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6F67XWDA0SV92AFFVMNSYVH","databricks-prod-checklist",null,"Execute Databricks production deployment checklist and rollback procedures.\nUse when deploying Databricks jobs to production, preparing for launch,\nor implementing go-live procedures.\nTrigger with phrases like \"databricks production\", \"deploy databricks\",\n\"databricks go-live\", \"databricks launch checklist\".","Dicklesworthstone\u002Fpi_agent_rust","Dicklesworthstone","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F35050222?v=4","01M6CG26AR942CSVMN1M5E3DSE","Jeff Emanuel","01M6CG26EPP71PSHW1VDS71121",1992,"MIT","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fsaas-packs\u002Fdatabricks-pack\u002Fskills\u002Fdatabricks-prod-checklist\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"databricks","databricks prod checklist","checklist","use","when","for","implementing","with","trigger","io","live","launch checklist","deployment checklist","go","execute","read","grep","rollback","Bash","Production","deployment","deploy","launch","2026-11-10 10:12:30",false,"---\nname: databricks-prod-checklist\ndescription: |\n  Execute Databricks production deployment checklist and rollback procedures.\n  Use when deploying Databricks jobs to production, preparing for launch,\n  or implementing go-live procedures.\n  Trigger with phrases like \"databricks production\", \"deploy databricks\",\n  \"databricks go-live\", \"databricks launch checklist\".\nallowed-tools: Read, Bash(databricks:*), Grep\nversion: 1.0.0\nlicense: MIT\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\n---\n\n# Databricks Production Checklist\n\n## Overview\nComplete checklist for deploying Databricks jobs and pipelines to production.\n\n## Prerequisites\n- Staging environment tested and verified\n- Production workspace access\n- Unity Catalog configured\n- Monitoring and alerting ready\n\n## Instructions\n\n### Step 1: Pre-Deployment Configuration\n\n#### Security\n- [ ] Service principal configured for automation\n- [ ] Secrets in Databricks Secret Scopes (not env vars)\n- [ ] Token expiration set (max 90 days)\n- [ ] Unity Catalog permissions configured\n- [ ] Cluster policies enforced\n- [ ] IP access lists configured\n\n#### Infrastructure\n- [ ] Production cluster pool configured\n- [ ] Instance types validated for workload\n- [ ] Autoscaling configured appropriately\n- [ ] Spot instance ratio set (cost vs reliability)\n\n### Step 2: Code Quality Verification\n\n#### Testing\n- [ ] Unit tests passing\n- [ ] Integration tests on staging data\n- [ ] Data quality tests defined\n- [ ] Performance benchmarks met\n\n```bash\n# Run tests via Asset Bundles\ndatabricks bundle validate -t prod\ndatabricks bundle run -t staging test-job\n\n# Verify test results\ndatabricks runs get --run-id $RUN_ID | jq '.state.result_state'\n```\n\n#### Code Review\n- [ ] No hardcoded credentials\n- [ ] Error handling covers all failure modes\n- [ ] Logging is production-appropriate\n- [ ] Delta Lake best practices followed\n- [ ] No `collect()` on large datasets\n\n### Step 3: Job Configuration\n\n```yaml\n# resources\u002Fprod_job.yml\nresources:\n  jobs:\n    etl_pipeline:\n      name: \"prod-etl-pipeline\"\n      tags:\n        environment: production\n        team: data-engineering\n        cost_center: analytics\n\n      schedule:\n        quartz_cron_expression: \"0 0 6 * * ?\"\n        timezone_id: \"America\u002FNew_York\"\n\n      email_notifications:\n        on_failure:\n          - \"oncall@company.com\"\n        on_success:\n          - \"data-team@company.com\"\n\n      webhook_notifications:\n        on_failure:\n          - id: \"slack-webhook-id\"\n\n      max_concurrent_runs: 1\n      timeout_seconds: 14400  # 4 hours\n\n      tasks:\n        - task_key: bronze_ingest\n          job_cluster_key: etl_cluster\n          notebook_task:\n            notebook_path: \u002FRepos\u002Fprod\u002Fpipelines\u002Fbronze\n          timeout_seconds: 3600\n\n        - task_key: silver_transform\n          depends_on:\n            - task_key: bronze_ingest\n          job_cluster_key: etl_cluster\n          notebook_task:\n            notebook_path: \u002FRepos\u002Fprod\u002Fpipelines\u002Fsilver\n\n      job_clusters:\n        - job_cluster_key: etl_cluster\n          new_cluster:\n            spark_version: \"14.3.x-scala2.12\"\n            node_type_id: \"Standard_DS3_v2\"\n            num_workers: 4\n            autoscale:\n              min_workers: 2\n              max_workers: 8\n            spark_conf:\n              spark.sql.shuffle.partitions: \"200\"\n              spark.databricks.delta.optimizeWrite.enabled: \"true\"\n            instance_pool_id: \"prod-pool-id\"\n```\n\n### Step 4: Deployment Commands\n\n```bash\n# Pre-flight checks\necho \"=== Pre-flight Checks ===\"\ndatabricks workspace list \u002FRepos\u002Fprod\u002F  # Verify repo exists\ndatabricks clusters list | grep prod    # Verify pools\u002Fclusters\ndatabricks secrets list-scopes          # Verify secrets\n\n# Deploy with Asset Bundles\necho \"=== Deploying ===\"\ndatabricks bundle deploy -t prod\n\n# Verify deployment\ndatabricks bundle summary -t prod\ndatabricks jobs list | grep prod-etl\n\n# Manual trigger to verify\necho \"=== Verification Run ===\"\nRUN_ID=$(databricks jobs run-now --job-id $JOB_ID | jq -r '.run_id')\necho \"Run ID: $RUN_ID\"\n\n# Monitor run\ndatabricks runs get --run-id $RUN_ID --wait\n```\n\n### Step 5: Monitoring Setup\n\n```python\n# monitoring\u002Fhealth_check.py\nfrom databricks.sdk import WorkspaceClient\nfrom datetime import datetime, timedelta\n\ndef check_job_health(w: WorkspaceClient, job_id: int) -> dict:\n    \"\"\"Check job health metrics.\"\"\"\n    # Get recent runs\n    runs = list(w.jobs.list_runs(\n        job_id=job_id,\n        completed_only=True,\n        limit=10,\n    ))\n\n    if not runs:\n        return {\"status\": \"NO_RUNS\", \"healthy\": False}\n\n    # Calculate success rate\n    successful = sum(1 for r in runs if r.state.result_state == \"SUCCESS\")\n    success_rate = successful \u002F len(runs)\n\n    # Calculate average duration\n    durations = [\n        (r.end_time - r.start_time) \u002F 1000 \u002F 60  # minutes\n        for r in runs if r.end_time\n    ]\n    avg_duration = sum(durations) \u002F len(durations) if durations else 0\n\n    # Check last run\n    last_run = runs[0]\n    last_state = last_run.state.result_state\n\n    return {\n        \"status\": \"HEALTHY\" if success_rate > 0.9 else \"DEGRADED\",\n        \"healthy\": success_rate > 0.9 and last_state == \"SUCCESS\",\n        \"success_rate\": success_rate,\n        \"avg_duration_minutes\": avg_duration,\n        \"last_run_state\": last_state,\n        \"last_run_time\": datetime.fromtimestamp(last_run.start_time \u002F 1000),\n    }\n```\n\n### Step 6: Rollback Procedure\n\n```bash\n#!\u002Fbin\u002Fbash\n# rollback.sh - Emergency rollback procedure\n\nJOB_ID=$1\nPREVIOUS_VERSION=$2\n\necho \"=== ROLLBACK INITIATED ===\"\necho \"Job: $JOB_ID\"\necho \"Target Version: $PREVIOUS_VERSION\"\n\n# 1. Pause the job\necho \"Pausing job...\"\ndatabricks jobs update --job-id $JOB_ID --json '{\"settings\": {\"schedule\": null}}'\n\n# 2. Cancel active runs\necho \"Cancelling active runs...\"\ndatabricks runs list --job-id $JOB_ID --active-only | \\\n  jq -r '.runs[].run_id' | \\\n  xargs -I {} databricks runs cancel --run-id {}\n\n# 3. Reset to previous version\necho \"Rolling back to version $PREVIOUS_VERSION...\"\ndatabricks bundle deploy -t prod --force\n\n# 4. Re-enable schedule\necho \"Re-enabling schedule...\"\n# (restore from backup config)\n\n# 5. Trigger verification run\necho \"Triggering verification run...\"\ndatabricks jobs run-now --job-id $JOB_ID\n\necho \"=== ROLLBACK COMPLETE ===\"\n```\n\n## Output\n- Deployed production job\n- Health checks passing\n- Monitoring active\n- Rollback procedure documented\n\n## Error Handling\n| Alert | Condition | Severity |\n|-------|-----------|----------|\n| Job Failed | `result_state = FAILED` | P1 |\n| Long Running | Duration > 2x average | P2 |\n| Consecutive Failures | 3+ failures in a row | P1 |\n| Data Quality | Expectations failed | P2 |\n\n## Examples\n\n### Production Health Dashboard Query\n```sql\n-- Job health metrics (Unity Catalog system tables)\nSELECT\n  job_id,\n  job_name,\n  COUNT(*) as total_runs,\n  SUM(CASE WHEN result_state = 'SUCCESS' THEN 1 ELSE 0 END) as successes,\n  AVG(execution_duration) \u002F 60000 as avg_minutes,\n  MAX(start_time) as last_run\nFROM system.lakeflow.job_run_timeline\nWHERE start_time > current_timestamp() - INTERVAL 7 DAYS\nGROUP BY job_id, job_name\nORDER BY total_runs DESC\n```\n\n### Pre-Production Verification\n```bash\n# Comprehensive pre-prod check\ndatabricks bundle validate -t prod && \\\ndatabricks bundle deploy -t prod --dry-run && \\\necho \"Validation passed, ready to deploy\"\n```\n\n## Resources\n- [Databricks Production Best Practices](https:\u002F\u002Fdocs.databricks.com\u002Fdev-tools\u002Fbundles\u002Fbest-practices.html)\n- [Job Configuration](https:\u002F\u002Fdocs.databricks.com\u002Fworkflows\u002Fjobs\u002Fjobs.html)\n- [Monitoring Guide](https:\u002F\u002Fdocs.databricks.com\u002Fadministration-guide\u002Fworkspace\u002Fmonitoring.html)\n\n## Next Steps\nFor version upgrades, see `databricks-upgrade-migration`.\n","# Databricks Production Checklist\n\n## Overview\nComplete checklist for deploying Databricks jobs and pipelines to production.\n\n## Prerequisites\n- Staging environment tested and verified\n- Production workspace access\n- Unity Catalog configured\n- Monitoring and alerting ready\n\n## Instructions\n\n### Step 1: Pre-Deployment Configuration\n\n#### Security\n- [ ] Service principal configured for automation\n- [ ] Secrets in Databricks Secret Scopes (not env vars)\n- [ ] Token expiration set (max 90 days)\n- [ ] Unity Catalog permissions configured\n- [ ] Cluster policies enforced\n- [ ] IP access lists configured\n\n#### Infrastructure\n- [ ] Production cluster pool configured\n- [ ] Instance types validated for workload\n- [ ] Autoscaling configured appropriately\n- [ ] Spot instance ratio set (cost vs reliability)\n\n### Step 2: Code Quality Verification\n\n#### Testing\n- [ ] Unit tests passing\n- [ ] Integration tests on staging data\n- [ ] Data quality tests defined\n- [ ] Performance benchmarks met\n\n```bash\n# Run tests via Asset Bundles\ndatabricks bundle validate -t prod\ndatabricks bundle run -t staging test-job\n\n# Verify test results\ndatabricks runs get --run-id $RUN_ID | jq '.state.result_state'\n```\n\n#### Code Review\n- [ ] No hardcoded credentials\n- [ ] Error handling covers all failure modes\n- [ ] Logging is production-appropriate\n- [ ] Delta Lake best practices followed\n- [ ] No `collect()` on large datasets\n\n### Step 3: Job Configuration\n\n```yaml\n# resources\u002Fprod_job.yml\nresources:\n  jobs:\n    etl_pipeline:\n      name: \"prod-etl-pipeline\"\n      tags:\n        environment: production\n        team: data-engineering\n        cost_center: analytics\n\n      schedule:\n        quartz_cron_expression: \"0 0 6 * * ?\"\n        timezone_id: \"America\u002FNew_York\"\n\n      email_notifications:\n        on_failure:\n          - \"oncall@company.com\"\n        on_success:\n          - \"data-team@company.com\"\n\n      webhook_notifications:\n        on_failure:\n          - id: \"slack-webhook-id\"\n\n      max_concurrent_runs: 1\n      timeout_seconds: 14400  # 4 hours\n\n      tasks:\n        - task_key: bronze_ingest\n          job_cluster_key: etl_cluster\n          notebook_task:\n            notebook_path: \u002FRepos\u002Fprod\u002Fpipelines\u002Fbronze\n          timeout_seconds: 3600\n\n        - task_key: silver_transform\n          depends_on:\n            - task_key: bronze_ingest\n          job_cluster_key: etl_cluster\n          notebook_task:\n            notebook_path: \u002FRepos\u002Fprod\u002Fpipelines\u002Fsilver\n\n      job_clusters:\n        - job_cluster_key: etl_cluster\n          new_cluster:\n            spark_version: \"14.3.x-scala2.12\"\n            node_type_id: \"Standard_DS3_v2\"\n            num_workers: 4\n            autoscale:\n              min_workers: 2\n              max_workers: 8\n            spark_conf:\n              spark.sql.shuffle.partitions: \"200\"\n              spark.databricks.delta.optimizeWrite.enabled: \"true\"\n            instance_pool_id: \"prod-pool-id\"\n```\n\n### Step 4: Deployment Commands\n\n```bash\n# Pre-flight checks\necho \"=== Pre-flight Checks ===\"\ndatabricks workspace list \u002FRepos\u002Fprod\u002F  # Verify repo exists\ndatabricks clusters list | grep prod    # Verify pools\u002Fclusters\ndatabricks secrets list-scopes          # Verify secrets\n\n# Deploy with Asset Bundles\necho \"=== Deploying ===\"\ndatabricks bundle deploy -t prod\n\n# Verify deployment\ndatabricks bundle summary -t prod\ndatabricks jobs list | grep prod-etl\n\n# Manual trigger to verify\necho \"=== Verification Run ===\"\nRUN_ID=$(databricks jobs run-now --job-id $JOB_ID | jq -r '.run_id')\necho \"Run ID: $RUN_ID\"\n\n# Monitor run\ndatabricks runs get --run-id $RUN_ID --wait\n```\n\n### Step 5: Monitoring Setup\n\n```python\n# monitoring\u002Fhealth_check.py\nfrom databricks.sdk import WorkspaceClient\nfrom datetime import datetime, timedelta\n\ndef check_job_health(w: WorkspaceClient, job_id: int) -> dict:\n    \"\"\"Check job health metrics.\"\"\"\n    # Get recent runs\n    runs = list(w.jobs.list_runs(\n        job_id=job_id,\n        completed_only=True,\n        limit=10,\n    ))\n\n    if not runs:\n        return {\"status\": \"NO_RUNS\", \"healthy\": False}\n\n    # Calculate success rate\n    successful = sum(1 for r in runs if r.state.result_state == \"SUCCESS\")\n    success_rate = successful \u002F len(runs)\n\n    # Calculate average duration\n    durations = [\n        (r.end_time - r.start_time) \u002F 1000 \u002F 60  # minutes\n        for r in runs if r.end_time\n    ]\n    avg_duration = sum(durations) \u002F len(durations) if durations else 0\n\n    # Check last run\n    last_run = runs[0]\n    last_state = last_run.state.result_state\n\n    return {\n        \"status\": \"HEALTHY\" if success_rate > 0.9 else \"DEGRADED\",\n        \"healthy\": success_rate > 0.9 and last_state == \"SUCCESS\",\n        \"success_rate\": success_rate,\n        \"avg_duration_minutes\": avg_duration,\n        \"last_run_state\": last_state,\n        \"last_run_time\": datetime.fromtimestamp(last_run.start_time \u002F 1000),\n    }\n```\n\n### Step 6: Rollback Procedure\n\n```bash\n#!\u002Fbin\u002Fbash\n# rollback.sh - Emergency rollback procedure\n\nJOB_ID=$1\nPREVIOUS_VERSION=$2\n\necho \"=== ROLLBACK INITIATED ===\"\necho \"Job: $JOB_ID\"\necho \"Target Version: $PREVIOUS_VERSION\"\n\n# 1. Pause the job\necho \"Pausing job...\"\ndatabricks jobs update --job-id $JOB_ID --json '{\"settings\": {\"schedule\": null}}'\n\n# 2. Cancel active runs\necho \"Cancelling active runs...\"\ndatabricks runs list --job-id $JOB_ID --active-only | \\\n  jq -r '.runs[].run_id' | \\\n  xargs -I {} databricks runs cancel --run-id {}\n\n# 3. Reset to previous version\necho \"Rolling back to version $PREVIOUS_VERSION...\"\ndatabricks bundle deploy -t prod --force\n\n# 4. Re-enable schedule\necho \"Re-enabling schedule...\"\n# (restore from backup config)\n\n# 5. Trigger verification run\necho \"Triggering verification run...\"\ndatabricks jobs run-now --job-id $JOB_ID\n\necho \"=== ROLLBACK COMPLETE ===\"\n```\n\n## Output\n- Deployed production job\n- Health checks passing\n- Monitoring active\n- Rollback procedure documented\n\n## Error Handling\n| Alert | Condition | Severity |\n|-------|-----------|----------|\n| Job Failed | `result_state = FAILED` | P1 |\n| Long Running | Duration > 2x average | P2 |\n| Consecutive Failures | 3+ failures in a row | P1 |\n| Data Quality | Expectations failed | P2 |\n\n## Examples\n\n### Production Health Dashboard Query\n```sql\n-- Job health metrics (Unity Catalog system tables)\nSELECT\n  job_id,\n  job_name,\n  COUNT(*) as total_runs,\n  SUM(CASE WHEN result_state = 'SUCCESS' THEN 1 ELSE 0 END) as successes,\n  AVG(execution_duration) \u002F 60000 as avg_minutes,\n  MAX(start_time) as last_run\nFROM system.lakeflow.job_run_timeline\nWHERE start_time > current_timestamp() - INTERVAL 7 DAYS\nGROUP BY job_id, job_name\nORDER BY total_runs DESC\n```\n\n### Pre-Production Verification\n```bash\n# Comprehensive pre-prod check\ndatabricks bundle validate -t prod && \\\ndatabricks bundle deploy -t prod --dry-run && \\\necho \"Validation passed, ready to deploy\"\n```\n\n## Resources\n- [Databricks Production Best Practices](https:\u002F\u002Fdocs.databricks.com\u002Fdev-tools\u002Fbundles\u002Fbest-practices.html)\n- [Job Configuration](https:\u002F\u002Fdocs.databricks.com\u002Fworkflows\u002Fjobs\u002Fjobs.html)\n- [Monitoring Guide](https:\u002F\u002Fdocs.databricks.com\u002Fadministration-guide\u002Fworkspace\u002Fmonitoring.html)\n\n## Next Steps\nFor version upgrades, see `databricks-upgrade-migration`.\n",{"name":5,"author":47,"license":15,"version":48,"description":49,"allowed-tools":50},"Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Execute Databricks production deployment checklist and rollback procedures.\nUse when deploying Databricks jobs to production, preparing for launch,\nor implementing go-live procedures.\nTrigger with phrases like \"databricks production\", \"deploy databricks\",\n\"databricks go-live\", \"databricks launch checklist\".\n","Read, Bash(databricks:*), Grep","main","90b50ed0778745a80fd5bfb13b1843d45b9e82f7","tests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fsaas-packs\u002Fdatabricks-pack\u002Fskills\u002Fdatabricks-prod-checklist",233,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust","https:\u002F\u002Fgithub.com\u002FDicklesworthstone","https:\u002F\u002Fwww.jeffreyemanuel.com\u002F","Building in NY","2026-11-15 01:54:15",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add Dicklesworthstone\u002Fpi_agent_rust --skill databricks-prod-checklist",{"tool":66,"command":67},"bunx","bunx skills add Dicklesworthstone\u002Fpi_agent_rust --skill databricks-prod-checklist",{"tool":69,"command":70},"pnpm","pnpm dlx skills add Dicklesworthstone\u002Fpi_agent_rust --skill databricks-prod-checklist","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fsaas-packs\u002Fdatabricks-pack\u002Fskills\u002Fdatabricks-prod-checklist\u002FSKILL.md",true,[],[75,114,138,164,191,213],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":80,"sourceHtmlUrl":81,"categories":82,"keywords":88,"publishedAt":42},"01M6FEGPPK0ZTKCCM0R3E3KS33","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). 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Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[121,124],{"categoryNo":122,"categoryName":123,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":84,"categoryName":85,"taxonomyType":86,"taxonomyTypeDesc":87},[126,127,128,94,129,130,90,131,132,133,104,134,135,136,137,26,24],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","logs","applications","behavior","local",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":142,"sourceHtmlUrl":143,"categories":144,"keywords":145,"publishedAt":42},"01M6FE20F7ZTQBFWKF4MPJ2SBX","neuropixels-analysis","Neuropixels neural recording analysis. Load SpikeGLX\u002FOpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen\u002FIBL curation, AI-assisted visual analysis, for Neuropixels 1.0\u002F2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FSKILL.md",[],[146,147,148,149,150,151,152,153,22,154,155,156,157,158,159,160,26,161,162,163,24,23],"analysis","neuropixels analysis","spike","metrics","data","open","quality","user","recordings","Neural","motion","visual","quality metrics","unit","sorting","curation","recording","load",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":168,"sourceHtmlUrl":169,"categories":170,"keywords":174,"publishedAt":42},"01M6FDP0PDK6F1GGX395C7625Z","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[171],{"categoryNo":172,"categoryName":173,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000023","Skill与智能体开发",[175,176,177,178,179,180,181,182,183,184,185,186,187,188,26,24,189,23,190],"skill creator","creator","workflows","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":195,"sourceHtmlUrl":196,"categories":197,"keywords":199,"publishedAt":42},"01M6FDM8Y7VK7NYG9SM7QPA3PW","notebooklm","Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FSKILL.md",[198],{"categoryNo":172,"categoryName":173,"taxonomyType":86,"taxonomyTypeDesc":87},[193,200,201,202,203,204,205,133,206,207,22,208,209,210,211,24,212],"google","browser automation","gemini","automation","auth","code","library","management","only","document","query","Citation","source",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":217,"sourceHtmlUrl":218,"categories":219,"keywords":226,"publishedAt":42},"01M6FDHPXE10J3XGTCH5HBHS1M","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like \"make me a GIF of X doing Y for Slack.\"","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[220,223],{"categoryNo":221,"categoryName":222,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":224,"categoryName":225,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000016","图像与多媒体",[227,228,229,176,230,105,231,232,181,22,233,188,234,235,236,24,237,23],"slack gif creator","slack","gif","validation","constraints","Animation","me","make","Utilities","concepts","request"]