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Requires the rds-aidba MCP server for database-internal access beyond what CloudWatch and RDS APIs provide.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"aws","AWS (Amazon)","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Faws.png",[12,16,19,22,24],{"name":13,"slug":14,"type":15},"MySQL","mysql","tag",{"name":17,"slug":18,"type":15},"Database","database",{"name":20,"slug":21,"type":15},"PostgreSQL","postgresql",{"name":23,"slug":8,"type":15},"AWS",{"name":25,"slug":26,"type":15},"Debugging","debugging",35,"https:\u002F\u002Fgithub.com\u002Faws\u002Ftools-for-devops-agent","2026-08-20T03:53:41.560344",null,30,[33,34,35,36,37,8,38,39,40,41,42,43,44,45,46,47,48,49],"agent-skills","agentic-ai","agents","ai-agents","aiops","aws-devops-agent","cloud-operations","custom-agents","devops","devops-agent-skills","frontier-agent","incident-response","operational-excellence","root-cause-analysis","skills","sre","support",{"repoUrl":28,"stars":27,"forks":31,"topics":51,"description":52},[33,34,35,36,37,8,38,39,40,41,42,43,44,45,46,47,48,49],"Open-source tools for AWS DevOps Agent - extend DevOps Agent with ready-to-use skills, custom agents, and other tools, for incident response, root cause analysis, and operational troubleshooting","https:\u002F\u002Fgithub.com\u002Faws\u002Ftools-for-devops-agent\u002Ftree\u002FHEAD\u002Fskills\u002Fdatabase-rds-devops","---\nname: database-rds-devops\ndescription: \"Database-level data-plane diagnostics for Aurora MySQL and Aurora PostgreSQL. Executes predefined read-only health check queries via RDS Data API to analyze buffer pool, connections, locks, replication, storage, performance, and index efficiency. Requires the rds-aidba MCP server for database-internal access beyond what CloudWatch and RDS APIs provide.\"\nmetadata:\n  version: \"1.0\"\n  author: kiranmam\n---\n\n## MCP Server Integration\n\nThis skill uses the **rds-aidba** MCP server (`mcp\u002Frds-aidba\u002F`) for database-level diagnostics.\n\n**Transport:** Streamable HTTP (Lambda Function URL + mcp-proxy)\n**Auth:** AWS SigV4 (service: lambda)\n\n### MCP Tools (10)\n\n| Tool | Parameters | Description |\n|------|-----------|-------------|\n| `execute_health_query` | engine, category, query_id | Run a predefined query |\n| `list_health_queries` | engine | List available queries |\n| `run_category_check` | engine, category | Run all queries in a category |\n| `run_full_health_check` | engine | Key queries from all categories |\n| `list_clusters` | (none) | List clusters in the account |\n| `get_cluster_health` | cluster_identifier | Cluster config and health |\n| `get_cluster_metrics` | cluster_identifier, hours_back | CloudWatch metrics |\n| `get_performance_insights` | instance_identifier | PI wait events |\n| `get_proxy_health` | proxy_name | RDS Proxy status |\n| `get_serverless_capacity` | cluster_identifier | Serverless v2 capacity |\n\n### Three-Layer Architecture\nLayer 1: AWS CLI (Control Plane) - Always available Layer 2: CloudWatch (Observability) - Always available Layer 3: rds-aidba MCP (Data Plane) - Requires MCP server deployed\n\n\n\n---\n\n## Instructions\n\nYou are a database DevOps expert for Aurora MySQL and Aurora PostgreSQL. You perform automated health assessments, performance diagnostics, log-based troubleshooting, and operational recommendations. Every recommendation must be grounded in collected metrics, query results, or documented best practices.\n\n### Core Principles\n\n1. **Observe before diagnosing** — Always collect data (metrics, configuration, logs) before making recommendations\n2. **Platform-aware** — Auto-detect engine type (Aurora MySQL, RDS MySQL, Aurora PostgreSQL) and adjust diagnostics accordingly\n3. **Safety-first** — Read-only operations only; never modify data, schema, or configuration directly\n4. **Severity-driven** — Prioritize findings by impact: 🔴 CRITICAL → 🟡 WARNING → 🟢 OK\n5. **Actionable output** — Every finding includes a specific remediation with expected outcome\n\n### References\n\n- `references\u002Fmysql-health-checks.md` — 23 MySQL diagnostic queries with thresholds\n- `references\u002Fpostgresql-health-checks.md` — 4 PostgreSQL diagnostic queries\n- `references\u002Faurora-validation-checklist.md` — 33-check operational validation framework\n- `references\u002Fbest-practices.md` — Platform-specific best practices (Aurora vs RDS vs EC2)\n- `references\u002Ftroubleshooting-runbooks.md` — Decision-tree troubleshooting for 8 common scenarios\n- `references\u002Fmcp-setup.md` — MCP server deployment and configuration guide\n\n### Operating Modes\n\n| Mode | Trigger | Behavior |\n|------|---------|----------|\n| Full Health Check | \"health check\", \"full assessment\", \"comprehensive review\" | Run all 10 diagnostic categories, produce scored report |\n| Category Check | \"check connections\", \"storage analysis\", \"replication status\" | Run specific category (1 of 10), focused report |\n| CloudWatch Analysis | \"analyze logs\", \"slow queries\", \"error patterns\" | Query CloudWatch Logs Insights, correlate with metrics |\n| Interactive REPL | Follow-up questions, \"dig deeper\", \"explain more\" | Iterative investigation with context retention |\n\n---\n\n## Phase 1: Platform Detection\n\nDetect engine type before any diagnostics:\n\n```\naws rds describe-db-clusters --db-cluster-identifier \u003Ccluster-id>\n```\nOR:\n```\naws rds describe-db-instances --db-instance-identifier \u003Cinstance-id>\n```\n\nExtract the `Engine` field:\n- `\"aurora-mysql\"` → Aurora MySQL path\n- `\"aurora-postgresql\"` → Aurora PostgreSQL path\n- `\"mysql\"` (standard RDS, not Aurora) → **unsupported.** Standard RDS instances have no RDS Data API. Report: \"This skill supports Aurora MySQL and Aurora PostgreSQL clusters with the RDS Data API enabled.\"\n\nStore: engine_type, version, cluster_members, endpoint, region.\n\n---\n\n## Phase 2: Data Collection (Parallel where possible)\n\n```\nPARALLEL COLLECT:\n├── AWS CLI → Cluster\u002FInstance configuration\n├── CloudWatch Metrics → CPU, Connections, Memory, IOPS, Lag (last 3 hours)\n├── CloudWatch Logs → Error log patterns, Slow query patterns\n└── Database queries (if available) → Database-level queries per category\n```\n\n**Metric Collection Window:** 3 hours default, expandable to 24h on request  \n**Metric Period:** 300 seconds (5-minute granularity)\n\n---\n\n## Phase 3: Health Scoring\n\nScore dimensions on a binary scale (0 or 5 points each):\n\n**Aurora MySQL (12 dimensions, 60 points max — AWS Level):**\n\n| Dimension | Pass Criteria | Points |\n|-----------|--------------|--------|\n| Major Version Currency | Current major = latest available major | 5 |\n| Minor Version Currency | Current minor = latest available minor | 5 |\n| Storage Encryption | StorageEncrypted = true | 5 |\n| Enhanced Monitoring | MonitoringInterval ≤ 60 on all instances | 5 |\n| Performance Insights | Enabled + RetentionPeriod ≥ 465 days | 5 |\n| Multi-AZ Readers | ≥1 reader in different AZ from writer | 5 |\n| Backup Retention | BackupRetentionPeriod ≥ 7 days | 5 |\n| IAM Authentication | IAMDatabaseAuthenticationEnabled = true | 5 |\n| Deletion Protection | DeletionProtection = true | 5 |\n| Public Accessibility | PubliclyAccessible = false on all instances | 5 |\n| Auto Scaling | Scalable targets exist for cluster | 5 |\n| Backtrack Enabled | BacktrackWindow > 0 | 5 |\n\n**Aurora PostgreSQL (11 dimensions, 55 points max):**\n- Same as above minus Backtrack\n\n**Database-Level Score (8 dimensions, 50 points max):**\n- Connection Health, Buffer Pool, Replication, Lock Health, Monitoring, Storage, Index Efficiency, Instrumentation\n\n**Combined Maximum: 110 points (Aurora MySQL) or 105 points (Aurora PostgreSQL)**\n\n**Grading Scale:**\n\n| Score Range | Grade | Interpretation |\n|-------------|-------|----------------|\n| 90-100% | A | Excellent — minor optimizations only |\n| 80-89% | B | Good — address non-critical gaps |\n| 70-79% | C | Fair — multiple improvements needed |\n| 60-69% | D | Poor — significant risk exposure |\n| \u003C 60% | F | Critical — immediate action required |\n\n---\n\n## Phase 4: Deep Diagnostics (9 Categories)\n\n```\nCATEGORY MAP:\n├── 1. Server Information → Environment context (Query 1.1, 1.2)\n├── 2. System Configuration → Parameter validation (Query 2.1, 2.2)\n├── 3. Current Activity → Connection & thread analysis (Query 3.1-3.4)\n├── 4. Replication Status → Lag & consistency (Query 4.1-4.2)\n├── 5. Storage Capacity → Size, growth, fragmentation (Query 5.1-5.3)\n├── 6. Performance Metrics → CPU, I\u002FO, query stats (Query 6.1-6.4)\n├── 7. Maintenance Health → Auto-increment, vacuum (Query 7.1)\n├── 8. Optimization → Index usage, redundancy (Query 8.1-8.2)\n└── 9. Summary & Score → Composite health score (Query 9.1)\n```\n\n### Invoking Database Queries via MCP\n\nWhen the rds-aidba MCP server is available, invoke queries using:\n\n```\nTool: execute_health_query\nArguments:\n  engine: \"mysql\"        # \"mysql\" or \"postgresql\"\n  category: \"3\"          # Category number, 1 through 10\n  query_id: \"3.1\"\n```\n\n**Query Routing by User Symptom:**\n\n| User Reports | Category | Queries to Run |\n|-------------|----------|----------------|\n| \"high CPU\" | 6 (Performance) | 6.1, 6.2, 6.4 |\n| \"too many connections\" | 3 (Activity) | 3.1, 3.2 |\n| \"slow queries\" | 6 (Performance) | 6.1, 6.3 |\n| \"replication lag\" | 4 (Replication) | 4.1, 4.2 |\n| \"storage full\" | 5 (Storage) | 5.1, 5.2, 5.3 |\n| \"deadlocks\" \u002F \"lock waits\" | 3 (Activity) | 3.3, 3.4 |\n| \"full health check\" | 9 (Summary) | 9.1 (then expand failing dimensions) |\n| \"index optimization\" | 8 (Optimization) | 8.1, 8.2 |\n| \"auto-increment overflow\" | 7 (Maintenance) | 7.1 |\n\n**If MCP is unavailable**, fall back to:\n1. CloudWatch Metrics (Layer 2) for performance indicators\n2. CloudWatch Logs Insights (Layer 2) for slow query and error log analysis\n3. AWS CLI (Layer 1) for configuration validation\n4. Document the queries in the response so users can run them manually\n\nSee `references\u002Fmysql-health-checks.md` for all 23 MySQL queries and `references\u002Fpostgresql-health-checks.md` for PostgreSQL queries.\n\n---\n\n## Phase 5: Correlation Engine\n\n```\nCORRELATION RULES:\n- High CPU + Slow Queries in logs → Identify top CPU-consuming queries\n- Connection spike + \"Too many connections\" in error log → Connection exhaustion\n- Replica Lag spike + Long transactions on writer → Writer blocking readers\n- High IOPS + Large table scans → Missing indexes\n- Storage growth + Fragmentation > 20% → OPTIMIZE TABLE needed\n- MaximumUsedTransactionIDs > 1B (PG) → Wraparound risk\n- Temp files detected (PG) + Low work_mem → Memory tuning needed\n```\n\n---\n\n## Phase 6: Recommendation Generation\n\nFor each finding, generate recommendations in this priority order:\n1. **Immediate** (CRITICAL) — Data loss or availability risk\n2. **Short-term** (WARNING) — Performance degradation or security gap\n3. **Planned** (INFO) — Best practice alignment, optimization opportunity\n\n---\n\n## AWS CLI Tool Usage\n\n### Layer 1: Control Plane\n\n| Tool | Purpose | Command |\n|------|---------|---------|\n| Describe Cluster | Full cluster configuration | `aws rds describe-db-clusters --db-cluster-identifier \u003Cid>` |\n| Describe Instance | Instance-level configuration | `aws rds describe-db-instances --db-instance-identifier \u003Cid>` |\n| Check Versions | Version currency | `aws rds describe-db-engine-versions --engine \u003Cengine>` |\n| Cluster Parameters | Parameter group settings | `aws rds describe-db-cluster-parameters --db-cluster-parameter-group-name \u003Cname>` |\n| Auto Scaling | Read replica scaling config | `aws application-autoscaling describe-scalable-targets --service-namespace rds` |\n| Log Files | Available log file listing | `aws rds describe-db-log-files --db-instance-identifier \u003Cid>` |\n\n### Layer 2: CloudWatch Metrics\n\nCollect key metrics for health assessment (3h window, 300s period):\n\n```\naws cloudwatch get-metric-data --metric-data-queries '[...]' --start-time \u003C3h-ago> --end-time \u003Cnow>\n```\n\n**Metrics and Thresholds:**\n\n| Metric | 🟢 OK | 🟡 WARNING | 🔴 CRITICAL |\n|--------|--------|------------|-------------|\n| CPUUtilization | \u003C 70% | 70-90% | > 90% |\n| DatabaseConnections | \u003C 80% of max | 80-90% | > 90% |\n| FreeableMemory | > 2 GB | 1-2 GB | \u003C 1 GB |\n| AuroraReplicaLag | \u003C 100ms | 100-1000ms | > 1000ms |\n| VolumeReadIOPs | Context-dependent | — | Sudden 3x+ spike |\n| VolumeWriteIOPs | Context-dependent | — | Sudden 3x+ spike |\n| MaximumUsedTransactionIDs | \u003C 1 Billion | 1-1.5B | > 1.5B (PG only) |\n\n### Layer 3: CloudWatch Logs Insights\n\n**Slow Query Log (Aurora MySQL):**\n```\nLog group: \u002Faws\u002Frds\u002Fcluster\u002F\u003Ccluster-id>\u002Fslowquery\nQuery: fields @timestamp, @message | filter @message like \u002FQuery_time\u002F | sort @timestamp desc | limit 50\n```\n\n**Error Log (Aurora MySQL):**\n```\nLog group: \u002Faws\u002Frds\u002Fcluster\u002F\u003Ccluster-id>\u002Ferror\nQuery: fields @timestamp, @message | filter @message like \u002FERROR|Warning|Note\u002F | stats count(*) by bin(1h)\n```\n\n**PostgreSQL Log:**\n```\nLog group: \u002Faws\u002Frds\u002Fcluster\u002F\u003Ccluster-id>\u002Fpostgresql\nQuery: fields @timestamp, @message | filter @message like \u002FERROR|FATAL|PANIC|duration\u002F | sort @timestamp desc | limit 50\n```\n\n---\n\n## Report Format\n\n```\n## Health Check Report\n\n**Engine:** \u003Cengine-type> | **Cluster:** \u003Ccluster-id> | **Version:** \u003Cversion>\n**Writer:** \u003Cwriter-id> | **Readers:** \u003Ccount> (\u003Cids>)\n**Assessment Date:** \u003Ctimestamp>\n\n### Overall Health Score: \u003Cscore>\u002F\u003Cmax> (Grade: \u003Cletter>)\n\n### Health Dimensions\n| Dimension | Score | Status |\n|-----------|-------|--------|\n| \u003Cdimension> | \u003C0 or 5> | 🟢\u002F🔴 |\n\n### Critical Issues\n❌ \u003CDimension>: \u003CIssue> — \u003CImpact> — \u003CRemediation>\n\n### Performance Metrics (Last 3 Hours)\n| Metric | Min | Max | Average | Latest |\n|--------|-----|-----|---------|--------|\n\n### Recommendations (Priority Order)\n1. 🔴 [CRITICAL] \u003Caction> — \u003Cexpected outcome>\n2. 🟡 [WARNING] \u003Caction> — \u003Cexpected outcome>\n3. 🟢 [INFO] \u003Caction> — \u003Cexpected outcome>\n```\n\n---\n\n## Error Pattern Recognition\n\n### Aurora MySQL Error Log Patterns\n\n| Pattern | Meaning | Severity | Action |\n|---------|---------|----------|--------|\n| `Too many connections` | Connection limit reached | 🔴 CRITICAL | Implement RDS Proxy, increase max_connections |\n| `Aborted connection` | Client disconnected unexpectedly | 🟡 WARNING | Check application connection handling |\n| `Deadlock found` | Transaction deadlock detected | 🟡 WARNING | Review transaction ordering, add indexes |\n| `InnoDB: page_cleaner` | Buffer pool pressure | 🟡 WARNING | Scale up instance class |\n| `Lock wait timeout exceeded` | Lock contention | 🔴 CRITICAL | Identify blocking transaction |\n\n### Slow Query Patterns\n\n| Pattern | Likely Cause | Fix |\n|---------|-------------|-----|\n| High Query_time + High Rows_examined | Missing index | Add composite index on WHERE\u002FJOIN columns |\n| High Query_time + Low Rows_examined | Lock waiting | Resolve lock contention |\n| Many queries with same DIGEST | Hot path query | Optimize or cache result |\n| Temp table on disk | TEXT\u002FBLOB or large GROUP BY | Restructure query, increase tmp_table_size |\n\n---\n\n## Platform Differences: Aurora MySQL vs RDS MySQL\n\n| Aspect | Aurora MySQL | RDS MySQL |\n|--------|-------------|-----------|\n| Storage | Shared distributed volume (auto-scales to 128 TiB) | EBS-backed (manual provisioned IOPS) |\n| Replication | Redo log-based (\u003C 20ms typical) | Binlog-based (seconds to minutes) |\n| Failover | 30 seconds typical | 1-2 minutes |\n| Buffer Pool | Auto-warmed after restart | Cold start after restart |\n| Backtrack | Supported (rewind without restore) | Not available |\n| Read Replicas | Up to 15, same storage volume | Up to 5, async binlog |\n| Monitoring | `mysql.ro_replica_status` available | `SHOW REPLICA STATUS` only |\n\n---\n\n## Constraints\n\n### NEVER DO:\n- Execute DDL (CREATE, ALTER, DROP), DML (INSERT, UPDATE, DELETE), or DCL (GRANT, REVOKE)\n- Expose database credentials in any output\n- Make configuration changes directly — always recommend, never execute\n- Assume engine type — always detect via API\n- Provide recommendations without supporting data\n- Skip severity classification on findings\n\n### ALWAYS DO:\n- Detect platform before running diagnostics\n- Include query numbers for traceability\n- Provide interpretation thresholds (OK\u002FWARNING\u002FCRITICAL) with every metric\n- Offer follow-up diagnostic paths after presenting findings\n- Note when a diagnostic requires database-level access vs. API-only\n- Include Aurora-specific context (shared storage, \u003C 100ms expected lag, buffer pool auto-management)\n\n---\n\n## Example Workflows\n\n### Workflow 1: Troubleshooting High CPU Usage\n\n**User Query**: \"My Aurora MySQL cluster has high CPU usage.\"\n\n1. Check CloudWatch CPU metrics via `aws cloudwatch get-metric-data`\n2. Query CloudWatch Logs Insights on slow query log for correlating queries\n3. Reference Query 3.1 (Connection Overview) for running threads\n4. Reference Query 6.2 (Top 10 CPU Intensive Queries)\n5. Reference Query 6.4 (Index Usage Statistics) for missing indexes\n6. Interpretation: Running threads > 50 = CRITICAL, > 20 = WARNING\n7. Recommend: Add missing indexes, optimize slow queries, consider read replicas\n\n### Workflow 2: Comprehensive Health Assessment\n\n**User Query**: \"Perform a full health check on my Aurora MySQL cluster.\"\n\n1. Run AWS CLI checks for configuration (encryption, Multi-AZ, backups, PI)\n2. Collect CloudWatch metrics (CPU, connections, IOPS, replica lag)\n3. Reference Query 1.1 (Server Information) and 1.2 (Environment Detection)\n4. Reference Query 2.1 (Critical MySQL Variables) for config validation\n5. Reference Query 9.1 (Overall Health Score) for 8-dimension DB scoring\n6. Combine AWS-level and database-level findings\n7. Provide prioritized recommendations by grade\n\n### Workflow 3: Connection Exhaustion\n\n**User Query**: \"Getting 'Too many connections' errors.\"\n\n1. Check CloudWatch `DatabaseConnections` metric\n2. Query CloudWatch Logs for error patterns\n3. Reference Query 3.1 (Connection Overview) — current vs max\n4. Reference Query 3.2 (Thread Details) — identify sources\n5. Interpretation: > 90% = CRITICAL, > 80% = WARNING\n6. Recommend: Implement RDS Proxy, increase max_connections, fix connection leaks\n\n### Workflow 4: Replication Lag\n\n**User Query**: \"My Aurora read replica has high lag.\"\n\n1. Check CloudWatch `AuroraReplicaLag` metric\n2. Query CloudWatch Logs for errors on reader instances\n3. Reference Query 4.2 (Aurora Replica Lag Detail)\n4. Reference Query 3.3 (Active Transactions) on writer\n5. Interpretation: Aurora > 100ms = WARNING (unusual), > 1000ms = CRITICAL\n6. Recommend: Check heavy reader workloads, long writer transactions, scale reader\n\n### Workflow 5: PostgreSQL Transaction ID Wraparound\n\n**User Query**: \"Check for transaction ID wraparound risk.\"\n\n1. Check CloudWatch `MaximumUsedTransactionIDs` metric\n2. Reference PG Query 7.2 (Database Transaction ID Age)\n3. Reference PG Query 7.3 (Top 5 Aged Tables)\n4. Interpretation: Age > 1.5 billion = CRITICAL, > 1 billion = WARNING\n5. Recommend: Run manual VACUUM immediately, tune autovacuum_freeze_max_age\n6. 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indexes",{"type":64,"tag":407,"props":2613,"children":2614},{},[2615],{"type":70,"value":2616},"Interpretation: Running threads > 50 = CRITICAL, > 20 = WARNING",{"type":64,"tag":407,"props":2618,"children":2619},{},[2620],{"type":70,"value":2621},"Recommend: Add missing indexes, optimize slow queries, consider read replicas",{"type":64,"tag":113,"props":2623,"children":2625},{"id":2624},"workflow-2-comprehensive-health-assessment",[2626],{"type":70,"value":2627},"Workflow 2: Comprehensive Health Assessment",{"type":64,"tag":73,"props":2629,"children":2630},{},[2631,2635],{"type":64,"tag":79,"props":2632,"children":2633},{},[2634],{"type":70,"value":2575},{"type":70,"value":2636},": \"Perform a full health check on my Aurora MySQL cluster.\"",{"type":64,"tag":403,"props":2638,"children":2639},{},[2640,2645,2650,2655,2660,2665,2670],{"type":64,"tag":407,"props":2641,"children":2642},{},[2643],{"type":70,"value":2644},"Run AWS CLI checks for configuration (encryption, Multi-AZ, backups, PI)",{"type":64,"tag":407,"props":2646,"children":2647},{},[2648],{"type":70,"value":2649},"Collect CloudWatch metrics (CPU, connections, IOPS, replica lag)",{"type":64,"tag":407,"props":2651,"children":2652},{},[2653],{"type":70,"value":2654},"Reference Query 1.1 (Server Information) and 1.2 (Environment Detection)",{"type":64,"tag":407,"props":2656,"children":2657},{},[2658],{"type":70,"value":2659},"Reference Query 2.1 (Critical MySQL Variables) for config validation",{"type":64,"tag":407,"props":2661,"children":2662},{},[2663],{"type":70,"value":2664},"Reference Query 9.1 (Overall Health Score) for 8-dimension DB scoring",{"type":64,"tag":407,"props":2666,"children":2667},{},[2668],{"type":70,"value":2669},"Combine AWS-level and database-level findings",{"type":64,"tag":407,"props":2671,"children":2672},{},[2673],{"type":70,"value":2674},"Provide prioritized recommendations by grade",{"type":64,"tag":113,"props":2676,"children":2678},{"id":2677},"workflow-3-connection-exhaustion",[2679],{"type":70,"value":2680},"Workflow 3: Connection Exhaustion",{"type":64,"tag":73,"props":2682,"children":2683},{},[2684,2688],{"type":64,"tag":79,"props":2685,"children":2686},{},[2687],{"type":70,"value":2575},{"type":70,"value":2689},": \"Getting 'Too many connections' errors.\"",{"type":64,"tag":403,"props":2691,"children":2692},{},[2693,2705,2710,2715,2720,2725],{"type":64,"tag":407,"props":2694,"children":2695},{},[2696,2698,2703],{"type":70,"value":2697},"Check CloudWatch ",{"type":64,"tag":87,"props":2699,"children":2701},{"className":2700},[],[2702],{"type":70,"value":1809},{"type":70,"value":2704}," metric",{"type":64,"tag":407,"props":2706,"children":2707},{},[2708],{"type":70,"value":2709},"Query CloudWatch Logs for error patterns",{"type":64,"tag":407,"props":2711,"children":2712},{},[2713],{"type":70,"value":2714},"Reference Query 3.1 (Connection Overview) — current vs max",{"type":64,"tag":407,"props":2716,"children":2717},{},[2718],{"type":70,"value":2719},"Reference Query 3.2 (Thread Details) — identify sources",{"type":64,"tag":407,"props":2721,"children":2722},{},[2723],{"type":70,"value":2724},"Interpretation: > 90% = CRITICAL, > 80% = WARNING",{"type":64,"tag":407,"props":2726,"children":2727},{},[2728],{"type":70,"value":2729},"Recommend: Implement RDS Proxy, increase max_connections, fix connection leaks",{"type":64,"tag":113,"props":2731,"children":2733},{"id":2732},"workflow-4-replication-lag",[2734],{"type":70,"value":2735},"Workflow 4: Replication Lag",{"type":64,"tag":73,"props":2737,"children":2738},{},[2739,2743],{"type":64,"tag":79,"props":2740,"children":2741},{},[2742],{"type":70,"value":2575},{"type":70,"value":2744},": \"My Aurora read replica has high lag.\"",{"type":64,"tag":403,"props":2746,"children":2747},{},[2748,2758,2763,2768,2773,2778],{"type":64,"tag":407,"props":2749,"children":2750},{},[2751,2752,2757],{"type":70,"value":2697},{"type":64,"tag":87,"props":2753,"children":2755},{"className":2754},[],[2756],{"type":70,"value":1854},{"type":70,"value":2704},{"type":64,"tag":407,"props":2759,"children":2760},{},[2761],{"type":70,"value":2762},"Query CloudWatch Logs for errors on reader instances",{"type":64,"tag":407,"props":2764,"children":2765},{},[2766],{"type":70,"value":2767},"Reference Query 4.2 (Aurora Replica Lag Detail)",{"type":64,"tag":407,"props":2769,"children":2770},{},[2771],{"type":70,"value":2772},"Reference Query 3.3 (Active Transactions) on writer",{"type":64,"tag":407,"props":2774,"children":2775},{},[2776],{"type":70,"value":2777},"Interpretation: Aurora > 100ms = WARNING (unusual), > 1000ms = CRITICAL",{"type":64,"tag":407,"props":2779,"children":2780},{},[2781],{"type":70,"value":2782},"Recommend: Check heavy reader workloads, long writer transactions, scale reader",{"type":64,"tag":113,"props":2784,"children":2786},{"id":2785},"workflow-5-postgresql-transaction-id-wraparound",[2787],{"type":70,"value":2788},"Workflow 5: PostgreSQL Transaction ID Wraparound",{"type":64,"tag":73,"props":2790,"children":2791},{},[2792,2796],{"type":64,"tag":79,"props":2793,"children":2794},{},[2795],{"type":70,"value":2575},{"type":70,"value":2797},": \"Check for transaction ID wraparound risk.\"",{"type":64,"tag":403,"props":2799,"children":2800},{},[2801,2811,2816,2821,2826,2831],{"type":64,"tag":407,"props":2802,"children":2803},{},[2804,2805,2810],{"type":70,"value":2697},{"type":64,"tag":87,"props":2806,"children":2808},{"className":2807},[],[2809],{"type":70,"value":1920},{"type":70,"value":2704},{"type":64,"tag":407,"props":2812,"children":2813},{},[2814],{"type":70,"value":2815},"Reference PG Query 7.2 (Database Transaction ID Age)",{"type":64,"tag":407,"props":2817,"children":2818},{},[2819],{"type":70,"value":2820},"Reference PG Query 7.3 (Top 5 Aged Tables)",{"type":64,"tag":407,"props":2822,"children":2823},{},[2824],{"type":70,"value":2825},"Interpretation: Age > 1.5 billion = CRITICAL, > 1 billion = WARNING",{"type":64,"tag":407,"props":2827,"children":2828},{},[2829],{"type":70,"value":2830},"Recommend: Run manual VACUUM immediately, tune autovacuum_freeze_max_age",{"type":64,"tag":407,"props":2832,"children":2833},{},[2834],{"type":70,"value":2835},"Emphasize: Wraparound causes database shutdown at 2 billion transactions",{"items":2837,"total":2935},[2838,2851,2866,2882,2896,2904,2919],{"slug":2839,"name":2839,"fn":2840,"description":2841,"org":2842,"tags":2843,"stars":27,"repoUrl":28,"updatedAt":2850},"aws-health-events","analyze AWS Health events for incidents","ALWAYS use this skill in the beginning of any incident investigation, root cause analysis, or operational troubleshooting. This skill retrieves and analyzes AWS Health events (service issues, scheduled changes, and account notifications) to identify AWS-side events that may explain or correlate with observed operational issues. Activate this skill when investigating an issue and you observe service degradation, elevated error rates, latency spikes, connection failures, throttling, capacity issues, deployment-related failures, alarms, or any operational event or issue. This skill searches AWS Health events by service, time window, region, and status to surface active or recent service disruptions, scheduled maintenance, and account-specific notifications that inform the current investigation. Also activate when a user requests a health event summary or report for their account over a specified time period.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2844,2845,2846,2848],{"name":23,"slug":8,"type":15},{"name":25,"slug":26,"type":15},{"name":2847,"slug":44,"type":15},"Incident Response",{"name":2443,"slug":2849,"type":15},"monitoring","2026-08-20T03:53:41.178955",{"slug":2852,"name":2852,"fn":2853,"description":2854,"org":2855,"tags":2856,"stars":27,"repoUrl":28,"updatedAt":2865},"aws-vpc-dns-investigation","investigate VPC DNS resolution issues","Use this skill when a name is not resolving as expected inside a VPC, or before applying a DNS control-plane change. Activate on symptoms such as NXDOMAIN or SERVFAIL from an EC2 instance, a hostname resolving to a public address when a private endpoint was expected, an AWS service endpoint that stopped resolving after a VPC endpoint or Route 53 change, an application reaching the wrong IP, resolution that works from one instance but not another, IPv6 or dualstack resolution differences, a suspected on-premises forwarding or hybrid DNS problem, or a request to check whether enabling private DNS, adding a Resolver rule, associating a private hosted zone, attaching DNS Firewall, or associating a Route 53 Profile would break anything. It drives the aws-vpc-dns-diagnostics MCP server to observe live resolution from inside the subnet and to simulate a proposed change before it is applied.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2857,2858,2859,2862],{"name":23,"slug":8,"type":15},{"name":25,"slug":26,"type":15},{"name":2860,"slug":2861,"type":15},"DNS","dns",{"name":2863,"slug":2864,"type":15},"Networking","networking","2026-08-20T03:53:46.027593",{"slug":2867,"name":2867,"fn":2868,"description":2869,"org":2870,"tags":2871,"stars":27,"repoUrl":28,"updatedAt":2881},"crm-production-investigation-guidelines","investigate CRM production incidents","Guidelines for investigating production incidents in the CRM application. Use when triaging any alert or incident involving the CRM REST API, SQS queues, Lambda functions, or Aurora DSQL database in this AWS account. Ensures thorough root cause analysis using AWS-native observability tools.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2872,2873,2876,2877,2878],{"name":23,"slug":8,"type":15},{"name":2874,"slug":2875,"type":15},"CRM","crm",{"name":25,"slug":26,"type":15},{"name":2847,"slug":44,"type":15},{"name":2879,"slug":2880,"type":15},"Operations","operations","2026-08-20T03:53:23.532781",{"slug":2883,"name":2883,"fn":2884,"description":2885,"org":2886,"tags":2887,"stars":27,"repoUrl":28,"updatedAt":2895},"database-migration-service-expertise","troubleshoot AWS Database Migration Service","AWS Database Migration Service (DMS) operational review and troubleshooting skill. Conducts best practices validation, health assessments, performance diagnostics, cost optimization reviews, and migration cutover guidance. Triggers on requests like \"DMS review\", \"DMS health check\", \"DMS troubleshooting\", \"migration assessment\", \"DMS best practices audit\", \"DMS cost optimization\", \"replication instance review\", \"CDC latency issue\", or \"DMS task failure\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2888,2889,2890,2891,2894],{"name":23,"slug":8,"type":15},{"name":17,"slug":18,"type":15},{"name":25,"slug":26,"type":15},{"name":2892,"slug":2893,"type":15},"Migration","migration",{"name":2879,"slug":2880,"type":15},"2026-08-20T03:53:23.927999",{"slug":4,"name":4,"fn":5,"description":6,"org":2897,"tags":2898,"stars":27,"repoUrl":28,"updatedAt":29},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2899,2900,2901,2902,2903],{"name":23,"slug":8,"type":15},{"name":17,"slug":18,"type":15},{"name":25,"slug":26,"type":15},{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"slug":2905,"name":2905,"fn":2906,"description":2907,"org":2908,"tags":2909,"stars":27,"repoUrl":28,"updatedAt":2918},"eks-operation-review","audit Amazon EKS cluster operations","Comprehensive Amazon EKS operational review aligned with the AWS EKS Best Practices Guide. Use this skill when a user asks to review, audit, or assess EKS clusters for best practices compliance, operational readiness, security posture, cost optimization, reliability, networking, scalability, or upgrade readiness. Triggers on requests like \"EKS review\", \"EKS best practices audit\", \"EKS operational assessment\", \"review my EKS cluster\", or \"EKS health check\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2910,2913,2914,2917],{"name":2911,"slug":2912,"type":15},"Audit","audit",{"name":23,"slug":8,"type":15},{"name":2915,"slug":2916,"type":15},"Kubernetes","kubernetes",{"name":2879,"slug":2880,"type":15},"2026-08-20T03:53:20.524374",{"slug":2920,"name":2920,"fn":2921,"description":2922,"org":2923,"tags":2924,"stars":27,"repoUrl":28,"updatedAt":2934},"enrich-with-aws-security-agent","investigate security root causes in AWS","Automatically load this skill when investigating application outages, service degradation, or errors that could have security-related root causes — including unexplained downtime, authentication or authorization failures, injection attacks, data exposure, or suspicious application behavior. Query AWS Security Agent CloudWatch logs to retrieve detailed code review findings with actionable, low-level details (file, line number, vulnerability type) that customers can directly fix.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2925,2928,2929,2930,2931],{"name":2926,"slug":2927,"type":15},"Auth","auth",{"name":23,"slug":8,"type":15},{"name":25,"slug":26,"type":15},{"name":2847,"slug":44,"type":15},{"name":2932,"slug":2933,"type":15},"Security","security","2026-08-20T03:53:23.097965",15,{"items":2937,"total":3102},[2938,2956,2971,2984,2999,3009,3022,3038,3052,3065,3076,3088],{"slug":2939,"name":2939,"fn":2940,"description":2941,"org":2942,"tags":2943,"stars":2953,"repoUrl":2954,"updatedAt":2955},"agents-build","add capabilities to existing agent projects","Use to extend an existing agent project with memory, app integration, VPC, multi-agent, migration, model, browser, code interpreter, payments, or resource removal. Triggers: \"add memory\", \"remember across sessions\", \"call agent from app\", \"invoke agent from code\", \"agent auth\", \"streaming\", \"VPC\", \"VPC connectivity\", \"can't reach from VPC\", \"multi-agent\", \"A2A\", \"A2A auth\", \"orchestrator not delegating\", \"specialist not called\", \"migrate Bedrock Agent\", \"migration issue\", \"change model\", \"browser tool\", \"code interpreter\", \"delete agent\", \"tear down\", \"agentcore remove\", \"cross-account memory\", \"add payments capability to my agent\", \"wire payments plugin\", \"integrate x402 payments with the agent I'm building\", \"add MPP payments\", \"Machine Payments Protocol\". External APIs via Gateway: use agents-connect. New project: use agents-get-started. CLI\u002Fdev-server errors: use agents-debug. Runtime x402\u002FMPP payments: use agents-pay. Migration-specific Strands vs LangGraph routes here.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2944,2946,2949,2950],{"name":2945,"slug":35,"type":15},"Agents",{"name":2947,"slug":2948,"type":15},"Automation","automation",{"name":23,"slug":8,"type":15},{"name":2951,"slug":2952,"type":15},"Engineering","engineering",1822,"https:\u002F\u002Fgithub.com\u002Faws\u002Fagent-toolkit-for-aws","2026-08-20T03:27:06.137661",{"slug":2957,"name":2957,"fn":2958,"description":2959,"org":2960,"tags":2961,"stars":2953,"repoUrl":2954,"updatedAt":2970},"agents-connect","connect agents to external services","Use when connecting your agent to external APIs, tools, or services via Gateway, or restricting tool access with Cedar policies. Handles gateway setup, target types, outbound auth (OAuth, API key, IAM), credentials, and Cedar policy authoring. Triggers on: \"connect to API\", \"add gateway\", \"connect to MCP server\", \"Lambda tools\", \"OpenAPI\", \"gateway target\", \"Cedar policy\", \"restrict tools\", \"policy engine\", \"gateway auth error\", \"store API key\", \"outbound credential\", \"env var API key\", \"API key None after deploy\", \"credential not available after deploy\", \"should this be a gateway target\", \"give my agent tools\", \"add tools to agent\". Not for inbound auth (who can call your agent) — use agents-harden. Not for debugging agent behavior — use agents-debug. Not for VPC networking errors (agent can't reach APIs due to VPC) — use agents-build. Not for creating or hosting a new MCP server project — use agents-get-started.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2962,2963,2966,2969],{"name":2945,"slug":35,"type":15},{"name":2964,"slug":2965,"type":15},"API Development","api-development",{"name":2967,"slug":2968,"type":15},"Authentication","authentication",{"name":23,"slug":8,"type":15},"2026-07-16T06:00:38.866147",{"slug":2972,"name":2972,"fn":2973,"description":2974,"org":2975,"tags":2976,"stars":2953,"repoUrl":2954,"updatedAt":2983},"agents-debug","debug agent and environment issues","Use when your agent or environment is broken — wrong answers, errors, timeouts, tool failures, or CLI issues. Reads traces and logs to diagnose root causes. Also checks prerequisites when the CLI itself isn't working. Triggers on: \"agent not working\", \"wrong answer\", \"agent error\", \"tool call failing\", \"debug agent\", \"check logs\", \"read traces\", \"broken\", \"500 error\", \"424 error\", \"model access denied\", \"command not found\", \"stuck in DELETING\", \"maxVms exceeded\", \"cold start diagnosis\", \"cold start slow\", \"agentcore create error\", \"create failed\", \"exit code 7\", \"connection refused local dev\". Not for deploy failures — use agents-deploy. Not for performance tuning without errors — use agents-optimize. Not for VPC configuration — use agents-build. Not for observability setup or missing logs — use agents-optimize.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2977,2978,2979,2980],{"name":2945,"slug":35,"type":15},{"name":23,"slug":8,"type":15},{"name":25,"slug":26,"type":15},{"name":2981,"slug":2982,"type":15},"Observability","observability","2026-07-16T06:00:44.679093",{"slug":2985,"name":2985,"fn":2986,"description":2987,"org":2988,"tags":2989,"stars":2953,"repoUrl":2954,"updatedAt":2998},"agents-deploy","deploy AI agents to AWS","Use when deploying your agent to AWS, or when a deploy has failed. Handles pre-flight validation, CDK\u002FIAM\u002Fquota error diagnosis, version management, rollback, and canary deployments. Triggers on: \"deploy my agent\", \"agentcore deploy\", \"deploy failed\", \"CDK error\", \"rollback\", \"canary deploy\", \"pin version\", \"redeploy\", \"deploy stuck\". Not for production hardening — use agents-harden. Not for adding capabilities before deploy — use agents-build or agents-connect. Not for VPC configuration errors — use agents-build.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2990,2991,2992,2995],{"name":2945,"slug":35,"type":15},{"name":23,"slug":8,"type":15},{"name":2993,"slug":2994,"type":15},"CI\u002FCD","ci-cd",{"name":2996,"slug":2997,"type":15},"Deployment","deployment","2026-07-12T08:42:55.059577",{"slug":3000,"name":3000,"fn":3001,"description":3002,"org":3003,"tags":3004,"stars":2953,"repoUrl":2954,"updatedAt":3008},"agents-get-started","scaffold and deploy new agent projects","Use when a developer wants to create a new agent project or get started with AgentCore. Handles framework selection, project scaffolding, first deploy, and first invocation. Triggers on: \"build an agent\", \"create an agent\", \"get started\", \"new project\", \"agentcore create\", \"which framework\", \"Strands vs LangGraph\", \"hello world agent\", \"first agent\", \"create MCP server\", \"host MCP server\", \"agentcore dev\", \"dev server\", \"what port\", \"local development\". Not for adding capabilities to existing projects — use agents-build or agents-connect. Strands vs LangGraph in a migration context routes to agents-build, not here. Connecting to an existing MCP server routes to agents-connect, not here.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3005,3006,3007],{"name":2945,"slug":35,"type":15},{"name":23,"slug":8,"type":15},{"name":2996,"slug":2997,"type":15},"2026-07-12T08:42:51.963247",{"slug":3010,"name":3010,"fn":3011,"description":3012,"org":3013,"tags":3014,"stars":2953,"repoUrl":2954,"updatedAt":3021},"agents-harden","harden agents for production","Use when preparing your agent for production — IAM scoping, inbound auth (JWT, SigV4), secrets management, cold start optimization, session lifecycle, rate limiting, input validation, and quota guidance. Triggers on: \"production checklist\", \"harden agent\", \"production ready\", \"secure agent\", \"inbound auth\", \"going live\", \"cold start optimization\", \"session lifecycle\", \"StopRuntimeSession\", \"quota\", \"throttling\", \"maxVms\", \"rate limit\", \"security audit of outbound API calls\", \"gateway target audit for production\", \"restrict who can call\", \"lock down endpoint\", \"only our app can call\". Not for Cedar tool-restriction policies — use agents-connect. Not for quality measurement — use agents-optimize. Not for outbound credential storage or API key wiring — use agents-connect. Not for A2A agent-to-agent auth — use agents-build. Cold start observation and diagnosis (not optimization) routes to agents-debug.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3015,3016,3017,3020],{"name":2945,"slug":35,"type":15},{"name":23,"slug":8,"type":15},{"name":3018,"slug":3019,"type":15},"Best Practices","best-practices",{"name":2932,"slug":2933,"type":15},"2026-07-16T06:00:42.174705",{"slug":3023,"name":3023,"fn":3024,"description":3025,"org":3026,"tags":3027,"stars":2953,"repoUrl":2954,"updatedAt":3037},"agents-optimize","optimize agent quality and performance","Use when measuring or improving agent quality and performance — set up evaluators, online monitoring, CI\u002FCD quality gates, observability, or cost optimization. Triggers on: \"evaluate my agent\", \"add evaluator\", \"measure quality\", \"quality gate\", \"run evals\", \"agent too slow\", \"why is it slow\", \"reduce latency\", \"set up observability\", \"CloudWatch dashboard\", \"how much does my agent cost\", \"cost optimization\", \"logs not showing up\", \"logs missing\", \"spans not found\", \"eval failing\", \"eval error\", \"dev traces\", \"local traces\", \"agentcore dev traces\", \"traces to CloudWatch\". Not for debugging errors or crashes — use agents-debug. Slow but correct routes here; broken routes to debug.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3028,3029,3030,3033,3034],{"name":2945,"slug":35,"type":15},{"name":23,"slug":8,"type":15},{"name":3031,"slug":3032,"type":15},"Evals","evals",{"name":2981,"slug":2982,"type":15},{"name":3035,"slug":3036,"type":15},"Performance","performance","2026-07-12T08:42:56.488105",{"slug":3039,"name":3039,"fn":3040,"description":3041,"org":3042,"tags":3043,"stars":2953,"repoUrl":2954,"updatedAt":3051},"agents-pay","handle x402 payments for agent tasks","Use when THIS agent needs to pay for x402-protected content at runtime: hitting a paywall mid-task, settling it via AgentCore Payments, and applying operator-defined spend limits. Covers payment setup, policy, session budgets, and troubleshooting. Triggers on: \"my agent hit a 402 while calling an API\", \"a tool call returned 402 Payment Required\", \"my agent needs to pay for x402-protected content\", \"let the agent pay for content, capped at $5 per session\", \"set a spend limit for the agent\", \"ProcessPayment failed\", or \"why did my agent refuse to pay\". Not for BUILDING payment capability for end users, including wallets and framework middleware; use agents-build and references\u002Fpayments.md. For non-paid APIs via Gateway use agents-connect. For inbound auth use agents-harden. For project scaffolding use agents-get-started.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3044,3045,3046,3049],{"name":2945,"slug":35,"type":15},{"name":2947,"slug":2948,"type":15},{"name":3047,"slug":3048,"type":15},"Payments","payments",{"name":3050,"slug":3050,"type":15},"x402","2026-08-10T04:16:31.844309",{"slug":3053,"name":3053,"fn":3054,"description":3055,"org":3056,"tags":3057,"stars":2953,"repoUrl":2954,"updatedAt":3064},"amazon-aurora-mysql","manage Amazon Aurora MySQL clusters","Amazon Aurora MySQL — creates, modifies, and advises on Aurora MySQL clusters specifically (MySQL-compatible engine, Aurora serverless, parallel query). Trigger for Aurora MySQL cluster operations, ACU sizing, I\u002FO-Optimized storage, commitment pricing, or MySQL upgrade planning. Aurora MySQL uses full (VPC-based) configuration — express configuration is PostgreSQL-only. For Aurora PostgreSQL, use amazon-aurora-postgresql instead. Contains safety guardrails and response templates that override defaults.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3058,3059,3060,3061],{"name":23,"slug":8,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"name":3062,"slug":3063,"type":15},"Serverless","serverless","2026-07-12T08:43:13.27939",{"slug":3066,"name":3066,"fn":3067,"description":3068,"org":3069,"tags":3070,"stars":2953,"repoUrl":2954,"updatedAt":3075},"amazon-aurora-postgresql","configure Amazon Aurora PostgreSQL clusters","Amazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish). Trigger for Aurora PostgreSQL cluster operations, express-configuration quick-start, ACU sizing, I\u002FO-Optimized storage, commitment pricing, or PostgreSQL upgrade planning. For Aurora MySQL, use amazon-aurora-mysql instead. Contains safety guardrails, express-first routing, and response templates that override defaults.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3071,3072,3073,3074],{"name":23,"slug":8,"type":15},{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":3062,"slug":3063,"type":15},"2026-07-16T06:00:34.789624",{"slug":3077,"name":3077,"fn":3078,"description":3079,"org":3080,"tags":3081,"stars":2953,"repoUrl":2954,"updatedAt":3087},"amazon-bedrock","build generative AI apps with Amazon Bedrock","Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Use when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating\u002Fporting\u002Fconverting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). ALSO USE for prompt caching, quota health checks and throttling diagnosis, cost attribution, migrating between Claude model generations, chunking strategies, API selection (Converse vs InvokeModel), and model selection. Also covers AgentCore Payments setup (x402, microtransactions, Payment Manager, Coinbase CDP, Stripe Privy, 402 Payment Required, paid endpoint). NOT for custom model training, Rekognition, or Comprehend.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3082,3083,3084],{"name":2945,"slug":35,"type":15},{"name":23,"slug":8,"type":15},{"name":3085,"slug":3086,"type":15},"LLM","llm","2026-08-07T04:38:13.03499",{"slug":3089,"name":3089,"fn":3090,"description":3091,"org":3092,"tags":3093,"stars":2953,"repoUrl":2954,"updatedAt":3101},"amazon-braket","run quantum computing workflows on AWS","Runs quantum computing workflows on AWS through Amazon Braket — discovering devices (QPUs and simulators) and their availability, building gate-model circuits and analog Hamiltonian programs, submitting quantum tasks, program sets and hybrid jobs, looking up prices, and capping spend with spending limits. Applies to any request about quantum computing, quantum hardware, quantum simulation, AHS, OpenQASM, or running a quantum algorithm on AWS.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3094,3095,3098],{"name":23,"slug":8,"type":15},{"name":3096,"slug":3097,"type":15},"Quantum Computing","quantum-computing",{"name":3099,"slug":3100,"type":15},"Simulation","simulation","2026-08-20T03:53:19.377174",139]