[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-aws-labs-documentdb-well-architected-review":3,"mdc-fi1jy9-key":40,"related-org-aws-labs-documentdb-well-architected-review":3502,"related-repo-aws-labs-documentdb-well-architected-review":3680},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":29,"repoUrl":30,"updatedAt":31,"license":32,"forks":33,"topics":34,"repo":35,"sourceUrl":38,"mdContent":39},"documentdb-well-architected-review","perform Well-Architected reviews for Amazon DocumentDB","Performs a comprehensive Well-Architected review of Amazon DocumentDB clusters. Evaluates cluster configuration against 6 pillars (Reliability, Security, Operational Excellence, Performance Efficiency, Cost Optimization, Sustainability), generates findings with remediation guidance, and produces a universal JSON export + interactive HTML dashboard.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"aws-labs","AWS Labs","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Faws-labs.png","awslabs",[13,17,20,23,26],{"name":14,"slug":15,"type":16},"Security","security","tag",{"name":18,"slug":19,"type":16},"Performance","performance",{"name":21,"slug":22,"type":16},"Architecture","architecture",{"name":24,"slug":25,"type":16},"Database","database",{"name":27,"slug":28,"type":16},"AWS","aws",157,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Famazon-documentdb-tools","2026-08-07T05:03:40.486376",null,86,[],{"repoUrl":30,"stars":29,"forks":33,"topics":36,"description":37},[],"Tools for use with the Amazon DocumentDB managed document database service.","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Famazon-documentdb-tools\u002Ftree\u002FHEAD\u002Fperformance\u002Fprism\u002Fdocumentdb-well-architected-review","---\nname: documentdb-well-architected-review\ndisplay_name: DocumentDB Well-Architected Review\ndescription: \"Performs a comprehensive Well-Architected review of Amazon DocumentDB clusters. Evaluates cluster configuration against 6 pillars (Reliability, Security, Operational Excellence, Performance Efficiency, Cost Optimization, Sustainability), generates findings with remediation guidance, and produces a universal JSON export + interactive HTML dashboard.\"\nicon: \"πŸ—οΈ\"\ntrigger: I need to do a DocumentDB well-architected review\ninputs:\n - name: cluster_identifier\n description: \"The DocumentDB cluster identifier to review\"\n type: string\n required: true\n - name: aws_region\n description: \"AWS region where the cluster resides\"\n type: string\n required: true\n default: us-east-1\ndepends-on:\n - aws-api\ntools:\n - call_aws\n - run_python\n - file_write\n---\n\n## 1. Overview\n\nPerforms a comprehensive Well-Architected review of an Amazon DocumentDB cluster across 6 AWS Well-Architected pillars: Reliability, Security, Operational Excellence, Performance Efficiency, Cost Optimization, and Sustainability. Evaluates **38 automated checks** against live cluster configuration, CloudWatch metrics, and security posture. Produces a health score, prioritized findings with remediation CLI commands, Bedrock-generated pillar recommendations, a Universal WAR Export JSON file, an interactive HTML dashboard, and (inside Prism) a PDF export.\n\nThis is a **sub-skill** of the Prism platform (see the top-level `prism-dash`\nskill). It owns cluster assessment and pillar scoring. Conversational advice and\nper-query tuning are owned by the `documentdb-advisor` skill β€” defer chat\u002Fadvisory\nquestions there.\n\n### Two execution modes\n\n1. **Prism Dash plugin execution** β€” the live app runs the checks via the\n `wa_checks\u002F` plugin registry (Section 4a) and renders results in the UI, with\n Bedrock recommendations and PDF export. This is the primary mode.\n2. **Standalone manual execution** β€” an agent with AWS CLI access can perform the\n review directly using the 13-step workflow in Section 3 and the check catalog in\n Section 4. Both modes share the **same check IDs** so findings are traceable.\n\n## 2. DocumentDB Engine Context\n\nThe reviewing agent MUST know these facts:\n\n- DocumentDB is NOT MongoDB β€” API-compatible but purpose-built storage engine with B-tree indexes\n- Only 2\u002F3 of instance RAM is available for buffer cache (1\u002F3 reserved by DocumentDB)\n- Connection limits by instance type: db.t3\u002Ft4g.medium=1000, db.r5\u002Fr6g.large=3400, db.r5\u002Fr6g.xlarge=7000, db.r5\u002Fr6g.2xlarge=14200, db.r5\u002Fr6g.4xlarge=28400, db.r5\u002Fr6g.8xlarge+=60000\n- Compression: LZ4 available all versions; ZSTD available 8.0+ only; NOT enabled by default on 5.0 (must set default_compression parameter)\n- IAM authentication: available on 5.0+ instance-based clusters only; primary user always uses password auth\n- Query Planner: v1 (3.6\u002F4.0), v2 (5.0 β€” up to 10x improvement), v3 (8.0)\n- retryWrites must be false in connection strings\n- I\u002FO-Optimized storage: no per-operation I\u002FO charges; beneficial when I\u002FO cost exceeds ~25% of total cluster bill\n- Graviton instances (r6g, r7g, r8g, t4g, r6gd): better price-performance; r8g is latest generation (Graviton4, engine 5.0\u002F8.0 only)\n- 6-way replication across 3 AZs regardless of instance count\n- Automatic failover requires minimum 2 instances; typical RTO ~30 seconds with multi-AZ\n- Global clusters: cross-region RPO less than 1 second, RTO ~1 minute\n- Backup: continuous to S3, point-in-time recovery to any second within retention window\n- Index best practices: \u003C=5 indexes per collection, compound indexes \u003C=3 fields, follow ESR (Equality-Sort-Range) rule\n- Long-running queries (>30 min) block MVCC garbage collection causing cascading bloat and CPU pressure\n- MVCC total capacity: 1.4 billion IDs\n\n## 3. Workflow\n\n### Step 1: Discover cluster configuration\n- call_aws: `aws docdb describe-db-clusters --db-cluster-identifier {cluster_identifier} --region {aws_region}`\n- Extract: EngineVersion, BackupRetentionPeriod, DeletionProtection, StorageEncrypted, StorageType, DBClusterParameterGroup, DBSubnetGroup, VpcSecurityGroups, EnabledCloudwatchLogsExports, DBClusterMembers, DBClusterArn, Endpoint\n\n### Step 2: Fetch instance details\n- call_aws: `aws docdb describe-db-instances --filters Name=db-cluster-id,Values={cluster_identifier} --region {aws_region}`\n- Extract: DBInstanceIdentifier, DBInstanceClass, AvailabilityZone, IsClusterWriter (from DBClusterMembers)\n\n### Step 3: Fetch parameter group settings\n- call_aws: `aws docdb describe-db-cluster-parameters --db-cluster-parameter-group-name {parameter_group_name} --region {aws_region}`\n- Extract: tls (enabled\u002Fdisabled), tls_version\n\n### Step 4: Fetch security groups\n- For each VpcSecurityGroupId from Step 1:\n- call_aws: `aws ec2 describe-security-groups --group-ids {sg_id} --region {aws_region}`\n- Check: IpPermissions for 0.0.0.0\u002F0 or ::\u002F0 CidrIp rules\n\n### Step 5: Fetch CloudWatch metrics (7-day window, 1-hour period)\n- For each instance from Step 2, fetch these metrics with Dimensions=[{Name=DBInstanceIdentifier, Value={instance_id}}]:\n - CPUUtilization (Statistics: Average)\n - FreeableMemory (Statistics: Minimum)\n - BufferCacheHitRatio (Statistics: Average)\n - DatabaseConnections (Statistics: Maximum)\n - SwapUsage (Statistics: Maximum)\n - DiskQueueDepth (Statistics: Average)\n - IndexBufferCacheHitRatio (Statistics: Average)\n - DatabaseCursorsTimedOut (Statistics: Sum, Period: 86400)\n - AvailableMVCCIds (Statistics: Minimum) β€” writer instance only\n - ReadIOPS (Statistics: Average) β€” reader instances only\n- For cluster-level: DBClusterReplicaLagMaximum with Dimensions=[{Name=DBClusterIdentifier, Value={cluster_identifier}}]\n\n### Step 6: Fetch events (last 13 days)\n- call_aws: `aws docdb describe-events --source-identifier {cluster_identifier} --source-type db-cluster --start-time {13_days_ago} --region {aws_region}`\n- Filter for: \"failover\" in Message or EventCategories\n\n### Step 7: Check Secrets Manager\n- call_aws: `aws secretsmanager list-secrets --region {aws_region}`\n- Search: cluster_identifier or cluster endpoint in secret Name or Description\n\n### Step 8: Check CloudWatch alarms\n- call_aws: `aws cloudwatch describe-alarms --alarm-name-prefix {cluster_identifier} --region {aws_region}`\n- Count: MetricAlarms\n\n### Step 9: Evaluate all checks against collected data\n- Apply each check from the Check Catalog (Section 4) against the data collected in Steps 1-8\n- For per-instance checks: iterate over each instance, identify writer vs reader role\n\n### Step 10: Score results\n- health_score = pass_count \u002F (pass_count + warn_count + fail_count) * 100\n- Exclude \"info\" status from scoring\n- Color: >=80% green, 60-79% warning, \u003C60% red\n\n### Step 11: Generate Universal WAR Export JSON\n- Use the schema defined in Section 6\n- Write to file: `war_export_{cluster_identifier}_{timestamp}.json`\n\n### Step 12: Generate interactive HTML dashboard\n- Self-contained HTML with embedded CSS\n- 6-pillar grid layout with pillar colors\n- Health score header bar\n- Per-pillar panels grouped by status (fail first, then warn, then pass)\n- Write to file: `war_dashboard_{cluster_identifier}_{timestamp}.html`\n\n### Step 13: Present findings summary\n- Report health score\n- List all CRITICAL and HIGH severity findings\n- Provide top 3 remediation actions\n\n## 4. Check Catalog\n\n**38 checks across 6 pillars.** Of these, **37 are registered in the `wa_checks\u002F`\nplugin registry** via the `@register_check` decorator; **PERF1b** is contributed by\nthe Prism database-level path (`tabs\u002Fwell_architected._run_db_checks`) and merged\ninto the same result set. All check IDs are **stable** β€” use exactly what the code\ndefines (e.g. `REL1a` not `REL-1`, `PERF1b` not `PERF-1b`).\n\nThe complete catalog is broken down by pillar in the `pillars\u002F` directory. Each file\ncontains the full check table with IDs, thresholds, API calls, and remediation commands.\n\n- **[pillars\u002Freliability.md](pillars\u002Freliability.md)** β€” 9 checks: REL1a, REL1b, REL1c, REL5a, REL5b, REL5c, REL7, REL8, REL9\n- **[pillars\u002Fsecurity.md](pillars\u002Fsecurity.md)** β€” 7 checks: SEC1a, SEC1b, SEC2, SEC3, SEC5, SEC6, SEC8\n- **[pillars\u002Foperational-excellence.md](pillars\u002Foperational-excellence.md)** β€” 6 checks: OPS2, OPS5a, OPS5b, OPS5c, OPS7, OPS8\n- **[pillars\u002Fperformance-efficiency.md](pillars\u002Fperformance-efficiency.md)** β€” 9 checks: PERF5, PERF6, PERF8, PERF9, PERF11, PERF12, PERF13, PERF15, PERF1b\n- **[pillars\u002Fcost-optimization.md](pillars\u002Fcost-optimization.md)** β€” 5 checks: COST1, COST3, COST6, COST7, COST9\n- **[pillars\u002Fsustainability.md](pillars\u002Fsustainability.md)** β€” 2 checks: SUST1, SUST2\n\nRead each pillar file for the detailed evaluation logic.\n\n> Note: the Prism UI may surface *additional* exploratory checks beyond this\n> canonical catalog (e.g. `PERF1`, `PERF1c`, `PERF10`, `PERF16`, `COST4`, plus\n> `tabs\u002Fwa_v2\u002F` checks) from the legacy\u002Fnext-gen paths. Those are app extras; the\n> **38-check catalog above is the source of truth** for exports and scoring.\n\n### 4a. The `wa_checks\u002F` Plugin Registry (`wa_checks\u002Fregistry.py`)\n\nIn Prism, checks self-register with a decorator and are executed by `run_checks()`.\nThis is the authoritative implementation of the catalog.\n\n**Registration** β€” `@register_check(check_id, pillar, label, source=..., ...)`:\n\n```python\n@register_check(\"REL1a\", \"Reliability\", \"Backup retention period\",\n source=\"infrastructure\", priority=10)\ndef check_backup_retention(ctx: CheckContext) -> list[dict]:\n retention = ctx.cluster.get(\"BackupRetentionPeriod\", 1)\n return [{\"pillar\": \"Reliability\", \"id\": \"REL1a\",\n \"label\": f\"Backup retention period ({retention} days)\",\n \"status\": \"pass\" if retention >= 7 else \"warn\" if retention >= 3 else \"fail\",\n \"detail\": \"\"}]\n```\n\n**`CheckDefinition`** fields: `check_id`, `pillar`, `label`, `description`, `source`\n(`\"infrastructure\" | \"database\" | \"cloudwatch\"`), `func`, `per_instance`,\n`writer_only`, `reader_only`, `requires_analysis`, `priority`.\n\n**`CheckContext`** fields passed to every check: `cluster_id`, `region`, `cluster`\n(describe_db_clusters dict), `instances` (describe_db_instances list), `analysis_data`,\n`conn_str`, shared boto3 clients `docdb_client` \u002F `cw_client` \u002F `ec2_client`, and the\nper-instance fields `current_instance` and `is_writer`.\n\n**`run_checks(...)` execution model:**\n1. Determines writer instance IDs from `cluster[\"DBClusterMembers\"]` where\n `IsClusterWriter` is true.\n2. Runs all **non-per-instance** checks first, pillar by pillar (in fixed pillar order),\n sorted by `priority`. A check with `requires_analysis=True` and no `analysis_data`\n emits an `info` row (\"requires Analyze\") instead of running.\n3. Then iterates each instance, setting `ctx.current_instance` \u002F `ctx.is_writer`, and\n runs **per-instance** checks, honoring `writer_only` (e.g. REL9 MVCC) and\n `reader_only` (e.g. COST9 idle readers).\n4. A check that raises is caught and recorded as a `warn` row rather than aborting the\n run.\n\nReturns a flat list of result dicts: `{\"pillar\", \"id\", \"label\", \"status\", \"detail\"}`\nwith `status ∈ {pass, warn, fail, info}`.\n\n`tabs\u002Fwell_architected._run_wa_checks` calls `run_checks()` (plugin path), then appends\nthe database-level checks from `_run_db_checks(analysis_data)` (which contributes\n`PERF1b` plus app extras), then triggers AI recommendations (Section 4b). If the plugin\nimport fails, it falls back to a legacy inline path producing the same IDs.\n\n### 4b. Bedrock Recommendation Generation\n\nAfter checks run, `_generate_ai_recommendations(check_results, cluster_id, region,\nanalysis_data)` sends the failing\u002Fwarning checks to Bedrock using the system prompt in\n**`wa-advisor-prompt.md`** (loaded via `_load_wa_advisor_prompt()`). Models:\n`us.anthropic.claude-sonnet-4-20250514-v1:0` (primary) β†’\n`us.anthropic.claude-haiku-4-5-20251001-v1:0` (fallback). The prompt returns **JSON\nkeyed by pillar**, each entry `{check_id, action, why, impact, priority}` where\n`priority ∈ {Critical, High, Medium, Low}`. Only pillars with failing\u002Fwarning checks\nappear; the model must not invent checks not present in the input. Recommendations are\ngenerated asynchronously and stored in `_wa[\"ai_md\"]`.\n## 5. Reference Constants\n\n### CONN_LIMITS (instance type β†’ max connections)\n```\ndb.t3.medium: 1000 db.t4g.medium: 1000\ndb.r5.large: 3400 db.r6g.large: 3400 db.r6gd.large: 3400 db.r8g.large: 3400\ndb.r5.xlarge: 7000 db.r6g.xlarge: 7000 db.r6gd.xlarge: 7000 db.r8g.xlarge: 7000\ndb.r5.2xlarge: 14200 db.r6g.2xlarge: 14200 db.r6gd.2xlarge: 14200 db.r8g.2xlarge: 14200\ndb.r5.4xlarge: 28400 db.r6g.4xlarge: 28400 db.r6gd.4xlarge: 28400 db.r8g.4xlarge: 28400\ndb.r5.8xlarge: 60000 db.r6g.8xlarge: 60000 db.r6gd.8xlarge: 60000 db.r8g.8xlarge: 60000\ndb.r5.12xlarge: 60000 db.r6g.12xlarge: 60000 db.r6gd.12xlarge: 60000 db.r8g.12xlarge: 60000\ndb.r5.16xlarge: 60000 db.r6g.16xlarge: 60000 db.r6gd.16xlarge: 60000 db.r8g.16xlarge: 60000\ndb.r5.24xlarge: 60000\n```\n\n### INSTANCE_RAM_GIB (instance type β†’ RAM in GiB)\n```\ndb.t3.medium: 4 db.t4g.medium: 4\ndb.r5.large: 16 db.r6g.large: 16 db.r6gd.large: 16 db.r8g.large: 16\ndb.r5.xlarge: 32 db.r6g.xlarge: 32 db.r6gd.xlarge: 32 db.r8g.xlarge: 32\ndb.r5.2xlarge: 64 db.r6g.2xlarge: 64 db.r6gd.2xlarge: 64 db.r8g.2xlarge: 64\ndb.r5.4xlarge: 128 db.r6g.4xlarge: 128 db.r6gd.4xlarge: 128 db.r8g.4xlarge: 128\ndb.r5.8xlarge: 256 db.r6g.8xlarge: 256 db.r6gd.8xlarge: 256 db.r8g.8xlarge: 256\ndb.r5.12xlarge: 384 db.r6g.12xlarge: 384 db.r6gd.12xlarge: 384 db.r8g.12xlarge: 384\ndb.r5.16xlarge: 512 db.r6g.16xlarge: 512 db.r6gd.16xlarge: 512 db.r8g.16xlarge: 512\ndb.r5.24xlarge: 768\n```\n\n### GRAVITON_FAMILIES\n```\nr6g, r7g, r8g, t4g, r6gd\n```\n\n### Other Constants\n- MVCC total capacity: 1,400,000,000 IDs\n- Engine version classification: deprecated (3.x, 4.x), current (5.0), latest (8.0)\n- CloudWatch lookback: 7 days for all metrics, 13 days for failover events\n- CloudWatch period: 3600 seconds (1 hour) for all metrics except DatabaseCursorsTimedOut (86400 = daily)\n\n## 6. Universal WAR Export Schema\n\n```json\n{\n \"schema_version\": \"1.0\",\n \"metadata\": {\n \"service\": \"documentdb\",\n \"cluster_id\": \"\",\n \"account_id\": \"\",\n \"region\": \"\",\n \"engine_version\": \"\",\n \"timestamp\": \"\",\n \"skill_name\": \"documentdb-well-architected-review\",\n \"checks_evaluated\": 0,\n \"instances_reviewed\": 0\n },\n \"scoring\": {\n \"overall_health_score\": 0,\n \"by_status\": {\"pass\": 0, \"warn\": 0, \"fail\": 0, \"info\": 0},\n \"by_pillar\": {}\n },\n \"findings\": [\n {\n \"finding_id\": \"\u003Cunique UUID>\",\n \"check_id\": \"REL1a\",\n \"pillar\": \"reliability\",\n \"check_name\": \"Backup retention period\",\n \"status\": \"pass|warn|fail|info\",\n \"severity\": \"CRITICAL|HIGH|MEDIUM|LOW\",\n \"detail\": \"Human-readable finding with actual values\",\n \"instance_id\": null,\n \"remediation_cli\": \"aws docdb modify-db-cluster ...\",\n \"recommendations\": []\n }\n ]\n}\n```\n\nNote: check_id uses the SAME IDs as the Dash app (REL1a, SEC2, PERF5, etc.). The pillar field uses lowercase with underscores (reliability, security, operational_excellence, performance_efficiency, cost_optimization, sustainability).\n\n## 7. Dashboard Specification\n\n- Self-contained HTML file with embedded CSS (no external dependencies except CDN-loaded Plotly)\n- Dark theme with AWS-inspired color palette\n- Header: cluster_id, region, engine version, timestamp, health score (large, colored)\n- 6-pillar grid (2 columns x 3 rows)\n- Pillar colors: Reliability=#d45b07, Security=#dd344c, Operational Excellence=#5e6b7a, Performance Efficiency=#8c4fff, Cost Optimization=#067f68, Sustainability=#0972d3\n- Each pillar panel: header with icon + name + status counts, body grouped by status (fail first with red left border, warn with orange, pass with green)\n- Each finding row: status symbol + check_id (monospace) + label + detail (muted)\n- Responsive layout (collapses to single column on mobile)\n\n## 8. Priority Criteria (shared Prism severity schema)\n\nThese four levels are used consistently across all Prism skills. The Bedrock\nrecommendation output (Section 4b) uses these exact `priority` values.\n\n- **Critical**: Data loss risk, security breach risk, cluster unavailability (e.g. deletion protection off, encryption disabled, single instance with no failover, MVCC IDs \u003C25%)\n- **High**: Significant performance degradation or cost waste actively occurring (e.g. buffer cache \u003C95%, no CloudWatch alarms, swap usage, TLS disabled)\n- **Medium**: Best practice gap with moderate risk (e.g. missing Secrets Manager, non-Graviton instances, no profiler logging, oversized instances)\n- **Low**: Optimization opportunity with low urgency (e.g. compression not enabled, cost allocation tags missing, engine upgrade available, storage type informational)\n\nSeparately, each check **row** carries a `status` of `pass | warn | fail | info`. The\nhealth score (Section 3, Step 10) excludes `info` rows from the denominator.\n\n## 9. Lessons Learned \u002F Anti-Patterns\n\n- Always check engine version before recommending ZSTD compression (8.0+ only; use LZ4 for 5.0)\n- Connection limit checks must match the EXACT instance type (db.r6g.large β‰  db.r6g.xlarge)\n- BufferCacheHitRatio below 99% does NOT automatically mean the instance is undersized β€” check if the working set genuinely exceeds available cache\n- NEVER recommend dropping the _id index\n- Per-instance checks MUST identify writer vs reader roles. In the registry this is the `writer_only` \u002F `reader_only` flags on `@register_check`, resolved from `DBClusterMembers.IsClusterWriter` (REL9\u002FMVCC is `writer_only`, COST9\u002Fidle is `reader_only`)\n- Use EXACT check IDs from the code β€” never invent new numbering (REL1a not REL-1, PERF1b not PERF-1b)\n- Silent checks (REL7, REL8) should only produce findings when problems are detected β€” do not emit a \"pass\" row\n- FreeableMemory is compared against INSTANCE_RAM_GIB lookup β€” not a fixed threshold\n- The health score formula excludes \"info\" status findings from the denominator\n- `requires_analysis=True` checks (COST3, PERF8, PERF9, PERF15, SUST2) emit an `info` \"requires Analyze\" row when no `analysis_data` is present β€” they do not fail\n- PERF1b is not a registry check β€” it comes from the database-level path; keep it in the catalog but don't expect a `@register_check(\"PERF1b\", ...)` decorator\n\n## 10. Relation to Prism Dash App\n\nThis skill is executed by the Prism Dash app through the `wa_checks\u002F` plugin\nregistry (Section 4a), which is the authoritative implementation of the check catalog.\nThe app adds capabilities on top of the raw checks:\n\n- **Live database-level analysis** (index health, compression, bloat) feeds\n `analysis_data`, enabling `requires_analysis` checks (COST3, PERF8, PERF9, PERF15,\n SUST2) and the `PERF1b` redundant-index check.\n- **AI recommendations** via Amazon Bedrock and `wa-advisor-prompt.md` (Section 4b).\n- **Historical trend tracking** via `agent_memory` (`wa_results.json` + `.v1`\u002F`.v2`\n versions, compared by `load_wa_results_previous`).\n- **PDF export** via `wa_pdf.generate_wa_pdf(results, ai_data, cluster_id, region)` β€”\n a cover page with health score, per-pillar sections grouped by status, and an AI\n recommendations section.\n- Optional **Universal WAR Export** (Section 6) for cross-tool interoperability.\n\nThe autonomous agent (top-level `prism-dash` skill) runs this review as its\n`well_architected` module. Both the standalone skill and the Dash app share the same\ncheck IDs (REL1a, SEC1b, PERF5, …) so findings are traceable across execution modes.\n\n\u003C!-- Check catalog must stay in sync with: wa_checks\u002F*.py implementations -->\n\u003C!-- IDs are stable β€” use exactly what @register_check defines; PERF1b comes from tabs\u002Fwell_architected._run_db_checks -->\n\nConsult the `references\u002F` directory for detailed DocumentDB-specific knowledge when generating recommendations. (These are the same reference docs used by the `documentdb-advisor` skill; do not duplicate their content here.)\n\n## 11. Required IAM Permissions\n\n```json\n{\n \"Effect\": \"Allow\",\n \"Action\": [\n \"rds:DescribeDBClusters\",\n \"rds:DescribeDBInstances\",\n \"rds:DescribeDBClusterParameters\",\n \"rds:DescribeDBSubnetGroups\",\n \"rds:DescribeEvents\",\n \"rds:ListTagsForResource\",\n \"ec2:DescribeSecurityGroups\",\n \"cloudwatch:GetMetricStatistics\",\n \"cloudwatch:DescribeAlarms\",\n \"secretsmanager:ListSecrets\",\n \"backup:ListBackupPlans\",\n \"backup:ListBackupSelections\",\n \"backup:GetBackupSelection\",\n \"backup:GetBackupPlan\"\n ],\n \"Resource\": \"*\"\n}\n```\n\nNote: DocumentDB uses the `rds` namespace for API calls (not a separate `docdb` namespace in IAM).\n",{"data":41,"body":61},{"name":4,"display_name":42,"description":6,"icon":43,"trigger":44,"inputs":45,"depends-on":55,"tools":57},"DocumentDB Well-Architected Review","πŸ—οΈ","I need to do a DocumentDB well-architected review",[46,51],{"name":47,"description":48,"type":49,"required":50},"cluster_identifier","The DocumentDB cluster identifier to review","string",true,{"name":52,"description":53,"type":49,"required":50,"default":54},"aws_region","AWS region where the cluster resides","us-east-1",[56],"aws-api",[58,59,60],"call_aws","run_python","file_write",{"type":62,"children":63},"root",[64,73,87,116,123,163,169,174,258,264,270,289,295,313,319,337,343,366,372,451,457,475,481,499,505,523,529,542,548,566,572,591,597,630,636,654,660,744,757,839,844,912,933,946,964,1048,1150,1247,1261,1381,1401,1434,1440,1517,1523,1529,1539,1545,1554,1560,1569,1575,1598,1604,2601,2606,2612,2655,2661,2673,2716,2750,2756,2882,2888,2900,3029,3049,3069,3075,3475,3496],{"type":65,"tag":66,"props":67,"children":69},"element","h2",{"id":68},"_1-overview",[70],{"type":71,"value":72},"text","1. 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Use when the user wants KPI calculations such as Gross Margin %, EBITDA Margin %, Operating Expense Ratio, or Revenue Growth % QoQ. Also use for quarterly performance review, P&L analysis, or interpreting financial ratios against benchmarks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3670,3671,3674,3675],{"name":3605,"slug":3606,"type":16},{"name":3672,"slug":3673,"type":16},"Data Analysis","data-analysis",{"name":3614,"slug":3615,"type":16},{"name":3676,"slug":3677,"type":16},"KPI","kpi","2026-07-12T08:39:59.54971",159,{"items":3681,"total":996},[3682,3694,3702],{"slug":113,"name":113,"fn":3683,"description":3684,"org":3685,"tags":3686,"stars":29,"repoUrl":30,"updatedAt":3693},"optimize Amazon DocumentDB performance and configuration","Amazon DocumentDB expert advisor for database optimization, query tuning, index management, instance sizing, cost analysis, anti-pattern detection, and migration guidance. Powers Prism's conversational advisor β€” agentic Bedrock tool-use, two-step classifyβ†’fetchβ†’answer chat, topic-aware reference injection, safety-gated query execution, and per-slow-query AI recommendations. Use when asking about DocumentDB best practices, performance issues, query plans, supported operators, serverless vs provisioned, storage options, backup\u002Frestore, compression, or differences from MongoDB.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3687,3688,3689,3692],{"name":3611,"slug":3612,"type":16},{"name":24,"slug":25,"type":16},{"name":3690,"slug":3691,"type":16},"Engineering","engineering",{"name":18,"slug":19,"type":16},"2026-08-07T05:03:40.122457",{"slug":4,"name":4,"fn":5,"description":6,"org":3695,"tags":3696,"stars":29,"repoUrl":30,"updatedAt":31},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3697,3698,3699,3700,3701],{"name":21,"slug":22,"type":16},{"name":27,"slug":28,"type":16},{"name":24,"slug":25,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"slug":105,"name":105,"fn":3703,"description":3704,"org":3705,"tags":3706,"stars":29,"repoUrl":30,"updatedAt":3712},"analyze Amazon DocumentDB with Prism Dash","Top-level guide to the Prism Dash platform β€” an autonomous Amazon DocumentDB analysis and advisory tool built on Python\u002FDash. Covers the Observeβ†’Reasonβ†’Decideβ†’Act agent loop, the AnalyzerPlugin system, the 8-module analysis pipeline, cross-module correlation, cross-run memory, the Bedrock AI advisory stack, the code review engine, and Markdown report generation. Use when you need to understand how Prism works end-to-end, where a capability lives, or how the sub-skills (documentdb-advisor, documentdb-well-architected-review) plug in.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3707,3708,3709,3710],{"name":24,"slug":25,"type":16},{"name":3690,"slug":3691,"type":16},{"name":3518,"slug":3519,"type":16},{"name":3711,"slug":969,"type":16},"Python","2026-08-07T05:03:39.767088"]