Dsql
AWS LabsOfficialPublished by AWS Labs
- Skill
- Tool integration
- Data and AI
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The dsql skill provides a structured framework for interacting with Amazon Aurora DSQL. It encompasses schema management, migration strategies from MySQL and PostgreSQL, performance diagnostics, and bulk data loading. The skill integrates with MCP tools for direct database operations and provides extensive documentation on IAM authentication, multi-tenant patterns, and transaction management to ensure safe and efficient database operations.
What it does
- Executes SQL queries and DDL operations via MCP tools.
- Provides migration paths and type mapping for MySQL and PostgreSQL to DSQL.
- Offers diagnostic workflows for cluster performance and query plan explainability.
- Facilitates bulk data loading using specialized loaders.
- Enforces best practices for IAM authentication and scoped database roles.
- Validates SQL compatibility and provides linting for DSQL-specific constraints.
When to use it
- When building or migrating applications to Aurora DSQL.
- When performing schema changes or complex DDL migrations.
- When troubleshooting performance issues or query plan inefficiencies.
- When setting up multi-tenant database architectures.
- When bulk loading data into a DSQL cluster.
Advantages
- Ensures adherence to DSQL-specific transaction and concurrency limits.
- Promotes secure access patterns through IAM and scoped roles.
- Provides clear, phased workflows for complex tasks like performance tuning.
- Reduces migration friction with automated linting and type mapping guidance.
- Standardizes diagnostic reporting for query plan analysis.
Usage examples
- “How do I migrate my existing PostgreSQL schema to Aurora DSQL?”
- The agent will guide you through pg-migrations/type-mapping.md and schema-objects.md, providing steps to convert indexes, enums, and data types while ensuring compatibility.
- “My query is running slowly on DSQL, can you help?”
- The agent will initiate Workflow 9 (Query Plan Explainability) to analyze the query plan, check for full scans, and provide a structured diagnostic report.
- “How do I set up a multi-tenant table in DSQL?”
- The agent will provide instructions on using tenant_id in your schema design, enforcing it in WHERE clauses, and using separate transact calls for DDL.
- “What are the transaction limits for DSQL?”
- The agent will consult the limits-and-error-codes.md or awsknowledge tool to provide the exact limits for row modifications, data size, and duration.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/awslabs/dsql/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip dsql.zip -d .agents/skills
Documentation by AWS Labs
Amazon Aurora DSQL Skill
Aurora DSQL is a serverless, PostgreSQL-compatible distributed SQL database. This skill covers direct query execution via MCP tools, schema management, migrations, multi-tenant isolation, IAM auth, and bulk data loading via aurora-dsql-loader.
Reference Files
Load these files as needed for detailed guidance:
Core:
| Reference | When to Load | Contains |
|---|---|---|
| development-guide.md | ALWAYS before schema changes or DB operations | Best practices, DDL rules, transaction limits, foreign key constraints |
| foreign-keys.md | MUST load for foreign key operations or migrations | FK syntax, actions, validation, tenant keys |
| language.md | MUST load for language-specific choices | Driver selection, DSQL Connectors, connection code |
| access-control.md | MUST load for roles, grants, or sensitive data | Scoped role setup, IAM-to-database role mapping |
| troubleshooting.md | SHOULD load for errors or unexpected behavior | OCC and 23503, FK, connections, cluster state |
| limits-and-error-codes.md | SHOULD load before sizing batches, keys or indexes, or on 54000/0A000/54011 | Every limit, rejection and quota with SQLSTATE and exact message |
| dsql-examples.md | Load for implementation examples | Multi-tenant access, batch operations, identity and sequences, connection pooling |
| onboarding.md | User requests "Get started with DSQL" | Interactive step-by-step guide |
| occ-retry-patterns.md | MUST load for OCC retry code or conflict mitigation | Connectors, 40001 retry, non-retryable 23503, FK read conflicts, idempotent design |
MCP:
| Reference | When to Load | Contains |
|---|---|---|
| mcp-setup.md | Always for MCP server guidance | Setup instructions, 2 configuration options |
| mcp-tools.md | For MCP tool syntax and examples | Tool parameters, input validation |
| dsql-lint.md | MUST load before running dsql_lint or processing external SQL | Tool reference, fix statuses, unfixable error resolution |
DDL Migrations:
| Reference | When to Load | Contains |
|---|---|---|
| ddl-migrations/overview.md | MUST load for ALTER TYPE, SET NOT NULL, MODIFY PRIMARY KEY | Direct ALTER coverage and last-resort table recreation |
| ddl-migrations/column-operations.md | DROP COLUMN, ALTER TYPE, SET/DROP NOT NULL/DEFAULT | Column-level migration patterns |
| ddl-migrations/constraint-operations.md | ADD/DROP CONSTRAINT, VALIDATE CONSTRAINT, MODIFY PRIMARY KEY | Constraint and structural changes |
| ddl-migrations/batched-migration.md | Tables exceeding 3,000 rows | Batching patterns, progress tracking |
MySQL Migrations:
| Reference | When to Load | Contains |
|---|---|---|
| mysql-migrations/type-mapping.md | MUST load for MySQL → DSQL migration | Data type mappings, feature alternatives |
| mysql-migrations/ddl-operations.md | Translating MySQL DDL to DSQL | AUTO_INCREMENT, ENUM, SET, FK patterns |
| mysql-migrations/full-example.md | Complete MySQL table migration | End-to-end example with decision summary |
PostgreSQL Migrations:
| Reference | When to Load | Contains |
|---|---|---|
| pg-migrations/type-mapping.md | MUST load for DSQL NUMERIC or PG type questions | C collation rules, NUMERIC(p,s), JSON/JSONB |
| pg-migrations/index-conversion.md | MUST load for any index conversion or index support question | GIN/GiST/BRIN → btree, operator class removal, partial + expression indexes, sort order |
| pg-migrations/schema-objects.md | MUST load for ENUM, materialized views, extensions, multi-schema, CREATE FUNCTION | ENUM → CHECK, views, role/IAM mapping, LANGUAGE sql functions |
| pg-migrations/multi-region.md | Multi-region, active-active, or HA questions | Architecture, geographic partitioning |
ORM Guides:
| Reference | When to Load | Contains |
|---|---|---|
| orm-guides/overview.md | Migrating an ORM or using ORM locking with DSQL | Adapter names, locking, and key gotchas for Django/EF Core/Hibernate/Rails/SQLAlchemy |
Data Loading:
| Reference | When to Load | Contains |
|---|---|---|
| data-loading.md | Planning or running bulk loads with aurora-dsql-loader | Fresh-vs-warm partitions, resume/retry, --on-conflict semantics, throughput diagnostics |
System Diagnostics:
| Reference | When to Load | Contains |
|---|---|---|
| system-diagnostics/workflow.md | MUST load at Workflow 12 entry — cluster performance diagnostics | Prerequisites, 5 diagnostic phases, temporal comparison, handoff |
| system-diagnostics/wait-events.md | ALWAYS load when interpreting AAS results | Canonical DSQL wait event descriptions and investigation guidance |
| system-diagnostics/promql-patterns.md | Load when constructing PromQL queries | Reusable query templates for AAS breakdown, top-SQL, temporal compare |
Query Plan Explainability:
| Reference | When to Load | Contains |
|---|---|---|
| query-plan/workflow.md | MUST load at Workflow 9 entry — gates all other files | Trigger criteria, context disambiguation, routing, phased workflow |
| query-plan/plan-interpretation.md | MUST load at Workflow 9 Phase 0 | DSQL node types, Node Duration math, estimation-error bands |
| query-plan/catalog-queries.md | MUST load at Workflow 9 Phase 0 | pg_class/pg_stats/pg_indexes SQL, correlated-predicate verification |
| query-plan/guc-experiments.md | MUST load at Workflow 9 Phase 0 | GUC experiment procedures, 30-second skip protocol |
| query-plan/report-format.md | MUST load at Workflow 9 Phase 0 | Required report structure, element checklist, support request template |
| query-plan/query-rewrites-generic.md | SHOULD load at Phase 0; sub-files on-demand | Index of 10 generic rewrite patterns |
| query-plan/query-rewrites-dsql-specific.md | SHOULD load at Phase 0; sub-files on-demand | Index of DSQL-specific rewrite patterns |
Choosing How to Connect: MCP vs CLI/psql
The aurora-dsql MCP server binds a single cluster at startup (--cluster_endpoint), so
using it for another cluster means editing .mcp.json and restarting the session.
Use the
aurora-dsqlMCP tools (readonly_query,transact,get_schema) ONLY when the server already targets the cluster you need.Otherwise — unconfigured, disabled, or bound to a different cluster — do NOT reconfigure it. Use the CLI +
psqlpath instead:scripts/psql-connect.sh<cluster-id> --region <region> --command "SELECT ..."(mints an IAM token and runs viapsql).If you cannot confirm which cluster the MCP targets, confirm first or use the CLI/psql path — running against the wrong cluster is worse than the check.
The doc-only MCP tools (dsql_lint, dsql_*_documentation, dsql_recommend) need no cluster.
The CloudWatch MCP (Workflow 12) takes region/cluster_id per call, so one running server can
query clusters in any PromQL-enabled region (pass each cluster's region on the call). Details:
connectivity-tools.md.
MCP Tools Available
The aurora-dsql MCP server provides these tools:
Database Operations:
readonly_query - Execute SELECT queries (returns list of dicts)
transact - Execute DDL/DML statements in transaction (takes list of SQL statements)
get_schema - Get table structure for a specific table
SQL Validation:
dsql_lint - Validate SQL for DSQL compatibility and optionally auto-fix issues. Use before executing externally-sourced SQL.
Documentation & Knowledge:
dsql_search_documentation - Search Aurora DSQL documentation
dsql_read_documentation - Read specific documentation pages
dsql_recommend - Get DSQL best practice recommendations
Note: There is no list_tables tool. Use readonly_query with information_schema.
See mcp-setup.md for detailed setup instructions. See mcp-tools.md for detailed usage and examples.
AWS Knowledge MCP (awsknowledge)
Consult for verifying DSQL service limits before advising users. The numeric limits below are defaults that may change — when a user's decision depends on an exact limit, verify it first:
| Limit | Default | Verify query |
|---|---|---|
| Max row modifications per transaction | 3,000 | aurora dsql transaction limits |
| Max data size per transaction | 10 MiB | aurora dsql transaction limits |
| Max transaction duration | 5 minutes | aurora dsql transaction limits |
| Max connections per cluster | 10,000 | aurora dsql connection limits |
| Auth token expiry | 15 minutes | aurora dsql authentication token |
| Max connection duration | 60 minutes | aurora dsql connection limits |
| Max indexes per table | 24 incl. PK | aurora dsql index limits |
| Max columns per index | 8 | aurora dsql index limits |
| IDENTITY/SEQUENCE CACHE values | 1 or >= 65536 | aurora dsql sequence cache |
| Supported column data types | See docs | aurora dsql supported data types |
When to verify: Before recommending batch sizes, connection pool settings, or schema designs where hitting a limit would cause failures; any time the exact number can affect user decision. For the full limit set with each SQLSTATE and its exact error message, see limits-and-error-codes.md; where it marks a value observed and the docs differ, use the observed value.
Fallback: If awsknowledge is unavailable, use the defaults above and flag that limits should be verified against DSQL documentation.
CLI Scripts Available
Bash scripts in scripts/ for cluster management (create, delete, list, cluster info), psql connection, and bulk data loading from local/s3 csv/tsv/parquet files. See scripts/README.md for usage and hook configuration.
Quick Start
Pick a connection path: confirm the
aurora-dsqlMCP targets your cluster; if not, use the CLI/psqlpath instead — see Choosing How to Connect. The steps below name MCP tools; the equivalent SQL runs the same way throughpsql-connect.sh --command "...".Explore: Use
readonly_querywithinformation_schemato list tables. Useget_schemafor table structure.Query: Use
readonly_queryfor SELECT queries. MUST includetenant_idin WHERE for multi-tenant apps. MUST build SQL withsafe_query.build().Schema changes: Use
transactwith one DDL per transaction. MUST batch DML under 3,000 rows. MUST useCREATE INDEX ASYNCin a separate call. Usedsql_lintto validate first.Bulk load data: Use
aurora-dsql-loaderfor CSV/TSV/Parquet. Load data-loading.md for details. Use--dry-runfirst.
Performance Routing
When the user reports a performance problem, use this table to select the correct workflow:
| User signal | Route to |
|---|---|
| General performance complaint, "cluster is slow", "something changed", latency regression, no specific query identified | Workflow 12 (System Diagnostics) — observe via CloudWatch first |
| Specific query or query_id to investigate, "explain this plan", "why is this query slow" | Workflow 9 (Query Plan Explainability) — direct EXPLAIN analysis |
| OCC conflicts, commit errors, retry storms | Workflow 12 (System Diagnostics) — confirm via CW metrics before investigating |
| Cost optimization, "where is compute time spent" | Workflow 12 (System Diagnostics) — identify top contributors first |
Rule: When in doubt, start with Workflow 12. It identifies specific queries to investigate and routes to Workflow 9 with context.
Common Workflows
Workflow 1: Create Multi-Tenant Schema
Create tenant-owned tables with
tenant_idusing separatetransactcallsUse composite tenant FKs for tenant-owned parents and ordinary FKs for shared parents
Create tenant and query-pattern indexes with separate
CREATE INDEX ASYNCcallsVerify with
get_schema
MUST issue each DDL in its own transact call:
transact(["CREATE TABLE ..."])MUST serialize arrays into a single-column representation — DSQL has no array column type; PREFER
JSONB(operators work directly); MAY useTEXTwhen the column is opaque to the database; ASK the user. ForJSONBarrays, expand at query time withjsonb_array_elements_text(data)
Workflow 2: Safe Data Migration
MUST validate every DDL with dsql_lint(fix=true) before executing. DML does not require linting.
Validate DDL with
dsql_lint(sql=..., fix=true)— handle diagnostics per dsql-lint.mdExecute the reviewed
fixed_sqlwhen present, otherwise the reviewed source statementAdd column in its own
transactcallPopulate existing rows with UPDATE (batched under 3,000 rows)
Verify with readonly_query COUNT
Create an index if needed: validate then execute the reviewed DDL in its own
transactcall
MUST issue each
ALTER TABLEin its owntransactcall — DSQL rejects multi-DDL transactions withmultiple ddl statements not supported in a transactionMUST add column with only name and type; apply DEFAULT via separate UPDATE
MUST batch updates under 3,000 rows in separate transact calls
Recovery: Resume failed batches by filtering WHERE new_column IS NULL.
Workflow 3: Bulk Data Loading
Use aurora-dsql-loader for CSV, TSV, or Parquet loads. MUST load data-loading.md before advising on throughput or diagnosing slow loads.
Validate with
--dry-runfirstRun with
--manifest-diron persistent storage (not/tmp— tmpfs on AL2023, lost on crash) and--headerif file has a header rowOn failure: resume with
--resume-job-id; for duplicates use--on-conflict do-nothingFor large tables: create secondary indexes after load using
CREATE INDEX ASYNC
Workflow 4: Foreign Key Constraints
MUST load and follow foreign-keys.md before creating, altering, dropping, or migrating foreign keys.
Workflow 5: Query with Tenant Isolation
MUST authorize the caller against the tenant — format validation does not establish authorization
MUST build SQL with
safe_query.build()— useallow()/regex()for values (emits'v'),ident()for table/column names (emits"v"). See input-validation.mdMUST include
tenant_idin the WHERE clause; reject cross-tenant access at the application layer
Workflow 6: Set Up Scoped Database Roles
MUST load access-control.md for role setup, IAM mapping, and schema permissions.
Workflow 7: Table Recreation DDL Migration
For ALTER COLUMN TYPE, SET NOT NULL, or MODIFY PRIMARY KEY, MUST
load ddl-migrations/overview.md. Use a direct ALTER form
when supported; otherwise, present a user-approved table-recreation plan.
Workflow 8: Validate and Migrate to DSQL
MUST load dsql-lint.md before running dsql_lint. Run dsql_lint(sql=source_sql, fix=true) to validate and auto-convert. For MySQL-origin SQL, MUST cross-check against mysql-migrations/type-mapping.md even when lint returns clean. On parse_error, fall back to manual conversion then re-lint.
Workflow 9: Query Plan Explainability
Explains why the DSQL optimizer chose a particular plan. Triggered by slow queries, high DPU, unexpected Full Scans, or plans the user doesn't understand. REQUIRES a structured Markdown diagnostic report as the deliverable.
MUST load query-plan/workflow.md at entry — it defines trigger criteria, context disambiguation, routing, and the full phased workflow (Phase 0–4). Workflow.md specifies which reference files to load at each phase.
Safety. Plan capture uses readonly_query exclusively. Rewrite DML to SELECT for plan capture. MUST NOT use transact --allow-writes for plan capture.
Workflow 10: Full PostgreSQL → DSQL Schema Migration
MUST load pg-migrations/type-mapping.md, pg-migrations/index-conversion.md, pg-migrations/schema-objects.md, and foreign-keys.md. Run dsql_lint(fix=true) first for mechanical fixes, preserve foreign-key relationships, translate unsupported source syntax or options, then apply semantic conversions from the pg-migrations references for unfixable diagnostics and patterns the linter cannot handle. Re-lint the final output before deploying.
Workflow 11: ORM Migration and Locking (Django/EF Core/Hibernate/Rails/SQLAlchemy)
Load orm-guides/overview.md for adapter names, locking guidance, and framework-specific gotchas.
Workflow 12: System Diagnostics (CloudWatch AAS)
Diagnose cluster performance by querying db.active_sessions.avg via PromQL. Detects temporal anomalies in wait event distribution, identifies regressed queries, and routes to Workflow 9 for per-query investigation.
Requires: CloudWatch MCP server (awslabs.cloudwatch-mcp-server) enabled and configured with PromQL access in the same region as the cluster — see mcp/mcp-setup.md for enabling it, region requirements, and the session restart needed for its tools to register.
MUST load system-diagnostics/workflow.md at entry — it defines prerequisites, 5 diagnostic phases, temporal baselines, and the routing to Workflow 9 for identified queries.
Error Scenarios
awsknowledgereturns no results: Use the default limits in the table above and note that limits should be verified against DSQL documentation.dsql_lintunavailable or timing out: See the Error Handling section of dsql-lint.md. Do not silently skip validation — inform the user and require explicit confirmation before proceeding with manual rules from development-guide.md.OCC serialization error (
40001): Retry with backoff, and keep(OC001)in the loop too — see occ-retry-patterns.md.Foreign key violation (
23503): Correct the relationship or referential action; MUST NOT send it through the40001retry loop — see troubleshooting.md.Transaction exceeds limits: Split batches under 3,000 rows and 10 MiB; size is checked at
COMMIT— see limits-and-error-codes.md to size them and batched-migration.md for the pattern.Token expiration mid-operation: Generate a fresh IAM token — see authentication-guide.md. See troubleshooting.md for other issues.
Additional Resources
Files
- ATTRIBUTION.mdAdded by AutoGineer868 B
- LICENSELicence file added by AutoGineer9.9 KiB
- NOTICELicence file added by AutoGineer89 B
- SKILL.md27.9 KiB
- mcp/mcp-setup.md6.7 KiB
- mcp/mcp-tools.md2.0 KiB
- mcp/platforms/claude-code.md3.6 KiB
- mcp/platforms/codex.md1.6 KiB
- mcp/platforms/gemini.md2.1 KiB
- mcp/platforms/kiro.md2.8 KiB
- mcp/tools/database-tools.md4.6 KiB
- mcp/tools/documentation-tools.md1.3 KiB
- mcp/tools/input-validation.md3.3 KiB
- mcp/tools/safe_query.pyScript10.4 KiB
- mcp/tools/workflow-patterns.md3.1 KiB
- references/access-control.md5.0 KiB
- references/auth/authentication-guide.md4.6 KiB
- references/auth/connectivity-tools.md8.7 KiB
- references/auth/scaling-guide.md3.9 KiB
- references/data-loading.md6.0 KiB
- references/ddl-migrations/batched-migration.md2.5 KiB
- references/ddl-migrations/column-operations.md5.2 KiB
- references/ddl-migrations/constraint-operations.md6.7 KiB
- references/ddl-migrations/overview.md4.8 KiB
- references/development-guide.md9.6 KiB
- references/dsql-examples.md1.5 KiB
- references/dsql-lint.md10.8 KiB
- references/examples/connection.md2.5 KiB
- references/examples/data-operations.md2.9 KiB
- references/examples/migrations.md1.6 KiB
- references/examples/patterns.md5.5 KiB
- references/examples/schema.md1.6 KiB
- references/foreign-keys.md6.0 KiB
- references/language.md8.6 KiB
- references/limits-and-error-codes.md19.2 KiB
- references/mysql-migrations/ddl-auto-increment.md4.3 KiB
- references/mysql-migrations/ddl-batching.md1.0 KiB
- references/mysql-migrations/ddl-column-changes.md1.3 KiB
- references/mysql-migrations/ddl-constraints.md782 B
- references/mysql-migrations/ddl-operations.md1.5 KiB
- references/mysql-migrations/ddl-structural.md3.7 KiB
- references/mysql-migrations/ddl-type-alternatives.md4.5 KiB
- references/mysql-migrations/full-example.md7.3 KiB
- references/mysql-migrations/type-mapping.md14.3 KiB
- references/occ-retry-patterns.md9.6 KiB
- references/onboarding.md13.8 KiB
- references/orm-guides/overview.md10.9 KiB
- references/pg-migrations/index-conversion.md15.7 KiB
- references/pg-migrations/multi-region.md2.9 KiB
- references/pg-migrations/schema-objects.md15.0 KiB
- references/pg-migrations/type-mapping.md5.3 KiB
- references/query-plan/catalog-queries.md8.0 KiB
- references/query-plan/guc-experiments.md7.5 KiB
- references/query-plan/plan-interpretation.md15.0 KiB
- references/query-plan/query-rewrites-dsql-specific.md900 B
- references/query-plan/query-rewrites-generic.md1.9 KiB
- references/query-plan/query-rewrites/cte-late-materialization.md1.4 KiB
- references/query-plan/query-rewrites/flatten-union-all.md946 B
- references/query-plan/query-rewrites/left-join-to-inner.md794 B
- references/query-plan/query-rewrites/not-in-to-not-exists.md1.4 KiB
- references/query-plan/query-rewrites/or-to-in.md889 B
- references/query-plan/query-rewrites/propagate-filter.md1.0 KiB
- references/query-plan/query-rewrites/push-computation-to-constant.md1.0 KiB
- references/query-plan/query-rewrites/push-group-by-into-subquery.md1.6 KiB
- references/query-plan/query-rewrites/reltuples-estimate.md1.2 KiB
- references/query-plan/query-rewrites/split-large-joins.md2.2 KiB
- references/query-plan/query-rewrites/subquery-unnesting-correlated.md1.6 KiB
- references/query-plan/query-rewrites/subquery-unnesting-scalar.md1.4 KiB
- references/query-plan/query-rewrites/subquery-unnesting-uncorrelated.md1.4 KiB
- references/query-plan/report-format.md10.9 KiB
- references/query-plan/workflow.md9.4 KiB
- references/system-diagnostics/promql-patterns.md8.6 KiB
- references/system-diagnostics/wait-events.md13.7 KiB
- references/system-diagnostics/workflow.md19.4 KiB
- references/troubleshooting.md11.7 KiB
references/query-plan/report-format.md
Diagnostic Report Format
The diagnostic report is produced as Markdown, rendered inline in the agent's response. Produce a full report for every explainability request, even ones that feel simple — the structure is the deliverable, not a formality.
Required Elements Checklist
Every report MUST contain all of these. Missing any one of them is a regression:
[ ]
# SQL Query Explainability — Diagnostic Reportas the H1[ ]
Preview Only - not for distributionon the line immediately below the H1[ ]
## Query Informationtable with Query Identifier, Planning Time, Execution Time, DPU Estimate[ ]
## SQL Statementsection with the SQL in a fenced block[ ]
## Plan Overviewsection with the plan tree in a fenced block[ ]
## Findingssection with numbered findings ordered by Node Duration (most expensive first)[ ] Each finding uses
#### What we observed,#### Why it happened,#### Recommendationas H4 subheadings, verbatim[ ] Final
## Summarytable with columns# | Finding | Severity | Recommendation | Expected Impact[ ] Closing
## Next Stepsblock inviting the user to say "reassess" (or equivalent) after applying any recommendation, so the skill can measure the actual impact against the predicted Expected Impact
Conditional requirements
Execution Time >30s: the report MUST include a section stating GUC experimentation was skipped due to the 30-second threshold, AND the verbatim manual GUC testing SQL (see the skipped-query block under GUC Comparison Table). Do not re-run the query for redundant predicate testing either.
Anomalous EXPLAIN values (e.g., trillion-row counts on small tables): the report MUST explicitly confirm to the user that query results are correct despite the anomalous EXPLAIN output, flag the anomaly as a potential DSQL reporting bug, and include a Support Request Template with Query ID, table statistics (reltuples, actual COUNT), and full plan output — no raw customer data values.
Table of Contents
Report Structure
Finding Format
Severity Levels
Summary Table
GUC Comparison Table
Support Request Template
Report Structure
Produce the report using this exact structure:
# SQL Query Explainability — Diagnostic Report
Preview Only - not for distribution
## Query Information
| Field | Value |
| ---------------- | ---------------------------------------------------------------- |
| Query Identifier | {query_id} |
| Planning Time | {planning_time} ms |
| Execution Time | {execution_time} ms |
| DPU Estimate | Compute: {compute}, Read: {read}, Write: {write}, Total: {total} |
## SQL Statement
\`\`\`sql
{sql_statement}
\`\`\`
## Plan Overview
\`\`\`
{formatted_plan_tree}
\`\`\`
## Findings
Each finding is presented with three H4 subsections, verbatim: "What we observed" → "Why it happened" → "Recommendation".
Findings are ordered by duration impact, starting from the most expensive.
{findings}
## Summary
{summary_table}Finding Format
Each finding follows this structure:
### Finding N: {Title} ({Severity} — {duration_or_context})
**Applies to:** {query_variant_tag}
#### What we observed
{Specific problem identified. Include a metrics table when quantitative evidence is available:}
| Metric | Estimated | Actual | Error |
| -------- | --------- | ------ | -------- |
| {metric} | {est} | {act} | {ratio}x |
#### Why it happened
{Root cause analysis with evidence from the plan, optimizer statistics, and actual cardinalities.
Show the optimizer's calculation when relevant (selectivity math, independence assumption).}
#### Recommendation
{Specific, actionable recommendation.}
{When the recommendation involves SQL, include the exact statement:}
\`\`\`sql
{recommended_sql}
\`\`\`
**Expected impact:** {What improvement the customer should expect. Ground the prediction in the
evidence you gathered — actual-vs-estimated row counts, Node Duration math, filter selectivity,
DPU breakdown. When the evidence supports a concrete prediction, state it that way (e.g.,
"Storage Lookup drops from 50 rows per loop × 2000 loops to 1 per loop ≈ 50× less read DPU;
execution should go from ~4s to ~80ms"). When the evidence is insufficient for a numeric
prediction, **do not fabricate one** — name the missing evidence explicitly (e.g., "Cannot
predict magnitude without `most_common_freqs` on this column; expected qualitative direction
is a reduction in Node Duration"). Honesty about what you don't know is always preferable to
a plausible-sounding number with no data behind it.}Query Variant Tags
Tag each finding with which query variant it applies to:
| Tag | Meaning |
|---|---|
| Original Query | Finding from the original SQL execution |
| GUC Experiment | Finding from GUC-based plan experimentation |
| Redundant Predicate Experiment | Finding from redundant predicate testing |
Linking Cascading Findings
When one finding's root cause is another finding:
#### Recommendation
This finding is a consequence of Finding N — resolving that finding addresses this one.
No separate action needed.Severity Levels
| Severity | Criteria |
|---|---|
| CRITICAL | >50% of execution time; primary bottleneck |
| HIGH | Root cause of a CRITICAL finding or 20–50% of execution time |
| MODERATE | Measurable impact; worth fixing independently |
| LOW | Minor overhead; fix if convenient |
| BUG REPORT | Anomalous behavior indicating a potential DSQL bug |
Summary Table
Conclude the report with a summary table:
## Summary
| # | Finding | Severity | Recommendation | Expected Impact |
| - | ------- | ---------- | ------------------------- | ----------------- |
| 1 | {title} | {severity} | {one-line recommendation} | {one-line impact} |
| 2 | {title} | {severity} | {one-line recommendation} | {one-line impact} |GUC Comparison Table
When GUC experiments were performed, include a comparison:
## GUC Experiment Results
| Metric | Default | Merge Join Only |
| ----------------------------- | ---------- | --------------- |
| Plan structure | {describe} | {describe} |
| Execution time | {X}ms | {Y}ms |
| DPU (Total) | {N} | {M} |
| Key differences | {describe} | {describe} |
| Disabled strategy still used? | N/A | {Yes/No} |When GUC experiments were skipped (query >30s):
## GUC Experiment Results
GUC experimentation skipped — original query execution time ({X}s) exceeds 30-second threshold.
Recommend testing alternative strategies manually:
\`\`\`sql
SET enable_hashjoin = off;
SET enable_nestloop = off;
SET enable_mergejoin = on;
EXPLAIN ANALYZE VERBOSE {original_sql};
\`\`\`Support Request Template
Produce when a potential DSQL bug is identified:
## Support Request Template
**Subject:** {one-line description of the anomaly}
**Query Identifier:** {query_id}
**Description:**
{2-3 sentences explaining what was observed, why it is anomalous, and that the query
results are correct but diagnostic output appears affected.}
**Table Statistics:**
- {table}: reltuples={N}, relpages={M}, actual COUNT(*)={X}
- Index used: {index_name} ({index_columns})
- {additional context specific to the anomaly}
**DPU Estimate:** Compute={N}, Read={M}, Write={W}, Total={T}
**Full EXPLAIN ANALYZE VERBOSE output:**
\`\`\`
{full_plan_output}
\`\`\`Rules for the support template:
MUST include Query ID, full plan output, optimizer statistics, actual cardinalities, index definitions, DPU estimate
MUST NOT include actual customer data values from tables
Include only metadata, statistics, cardinalities, and plan output
Next Steps (closing block of every report)
End the report with this block so the user knows to come back for a reassessment:
## Next Steps
1. Apply the recommendations in order — Finding 1 first, then re-measure before deciding whether the subsequent findings still matter.
2. When any recommendation is in place, say **"reassess"** (or "I added the index" / "re-run the analysis"). I'll re-capture the plan, compare against the numbers above, and append an "Addendum: After-Change Performance" section to this report — so you can see the actual impact against the Expected Impact column.
3. If the observed change diverges significantly from the Expected Impact, I'll investigate the gap as a new finding rather than closing it out.Addendum: After-Change Performance (Phase 5)
When the user signals a reassessment, append a new H2 section to the same report — do not produce a separate report. The addendum has:
## Addendum: After-Change Performance
**Change applied:** {one-line description of what the user did, e.g., "Added composite index (clientid, _transactionstartdatetime) on associate"}
**Re-captured plan:** Query Identifier {new_query_id}, Execution Time {new_ms} ms, DPU {new_total}
| Metric | Before | After | Improvement |
| ---------------------- | ------------- | ------------ | ---------------- |
| Total Query Cost | {before_cost} | {after_cost} | {pct}% ↓ |
| Scan Type (main node) | {before_scan} | {after_scan} | {status} |
| Estimated Rows Scanned | {before_est} | {after_est} | {pct}% ↓ |
| Execution Time | {before_ms} | {after_ms} | {pct}% ↓ |
| DPU (Total) | {before_dpu} | {after_dpu} | {pct}% ↓ |
| Result Set | {before_rows} | {after_rows} | Unchanged / Diff |
**Match against Expected Impact:** {Yes — matches the N% latency reduction predicted in Finding 1 / No — only X% observed, investigating}.
**Remaining findings status:** {Finding 2 still applies / Findings 2–3 now trivial given this change}.If the Result Set row count changed, flag that prominently — the change should be performance-neutral semantically, and any row-count drift means the recommendation altered query correctness (which should never happen for an index addition, and indicates something else is wrong).
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server, Needs Python, Needs Node.js
Security
What our scanner noticed· Low risk
- Instructions that use the network
- Scripts that your agent may run
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Attribution and licence
- Published by
- AWS Labs
- Publisher
- AWS Labs (Official)
- Source
- awslabs/agent-plugins · plugins/databases-on-aws/skills/dsql · commit fbefb37
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- NOTICE (added by AutoGineer from NOTICE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by AWS Labs.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download dsql.zip