Agent Toolkit for AWS is a collection of AWS-supported MCP servers, skills, plugins, commands, and hooks that help AI coding agents build, deploy, and manage applications on AWS. It is used by developers working with AWS services through agents such as Claude Code, Codex, Cursor, and Kiro. The catalogue entries are the toolkit's own agent extensions for AWS development and operations.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add aws/agent-toolkit-for-aws --skill amazon-aurora-postgresqlgit clone --depth 1 https://github.com/aws/agent-toolkit-for-awsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aws/agent-toolkit-for-aws/amazon-aurora-postgresql)<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/amazon-aurora-postgresql"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/amazon-aurora-postgresql/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/amazon-aurora-postgresql"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/amazon-aurora-postgresql.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00109 | $0.04467 |
| Opus 5.5 | $0.00044 | $0.01787 |
| Sonnet 5.5 | $0.00022 | $0.00893 |
| Haiku 4.5 | $0.00011 | $0.00447 |
Grade A, and why
amazon-aurora-postgresql scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to amazon-aurora-mysql — 68 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Aurora PostgreSQL
A modular toolkit for Aurora PostgreSQL organized as a registry of sub-skills. Each sub-skill handles one domain of Aurora PostgreSQL work. The router matches user intent to the right sub-skill, then loads only the references needed. (For Aurora MySQL, use the amazon-aurora-mysql skill.)
Operating procedure (follow in order)
- Route — match the request to a sub-skill using the Trigger phrases column (match on meaning, not exact wording), then confirm with the When to route here column.
- Load —
file_readthe matched sub-skill'sreferences/{id}-instructions.mdand announce the path. Do not answer a matched sub-skill from general knowledge alone. - Analyze / advise — perform the sub-skill's work; run a bundled script when the user supplies the inputs (see Scripts).
- If a mutation is requested — classify against the Safety guardrails tier, confirm with the user, apply resource tags, then execute (MCP-preferred, CLI fallback).
- Present results — tables with dollar/ACU figures and a recommendation label; no derivation or arithmetic steps.
Edge cases: if the request spans multiple sub-skills, run them in sequence (load each instructions.md in turn). If no sub-skill matches, answer directly from Aurora PostgreSQL knowledge. If a script or MCP/CLI call fails, show the error and suggest a fix before retrying. The numbered Global rules below are details that hang off these steps.
Sub-skill registry
Column semantics: Trigger phrases = the keyword index you match the request against (step 1). When to route here = the decision logic confirming the match. Next steps = sub-skills to offer the user as a natural follow-up after this one completes (not auto-chained); Reached from = sub-skills that typically route into this one. Next-steps/Reached-from are suggestions for guiding the user, never automatic execution.
| ID | Name | When to route here | Trigger phrases | Reached from | Next steps |
|---|---|---|---|---|---|
create |
Create Cluster | Routes Aurora PostgreSQL cluster creation requests. Express configuration (single API call, no VPC) is the default — routes to express-create. Routes to full configuration when VPC, custom KMS, custom params, or a specific engine version is required. |
create a cluster, new database, set up Aurora PostgreSQL, get started, need a PostgreSQL database, provision | — | express-create, serverless-advisory, io-optimized |
express-create |
Express Configuration | Provisions Aurora PostgreSQL serverless via the single-API-call express flow. AWS-managed connectivity (no customer VPC). IAM-only authentication via Internet Access Gateway — no master password. Post-creation connection is via IAM auth token (aws rds generate-db-auth-token). Use when no VPC, custom KMS, or custom parameter group is required. Routes back to create for full configuration needs. |
express configuration, express create, internet access gateway, single API call, Aurora PostgreSQL serverless quick start, no VPC, IAM auth token, how to connect to express cluster | create |
— |
serverless-advisory |
Aurora serverless Advisory | All Aurora serverless questions: ACU sizing, scale-to-zero behavior and compatibility, provisioned→serverless migration, capacity planning, and feature constraints. | ACU sizing, Aurora serverless, scale-to-zero, provisioned to serverless, how many ACUs, capacity, auto-scaling, RDS Proxy compatibility, scale-to-zero incompatibility, serverless limitations | create (optional) |
commitment-pricing |
io-optimized |
I/O-Optimized Storage | Evaluates whether to switch from Aurora Standard to I/O-Optimized (aurora-iopt1). Uses the 25% I/O cost threshold rule. | I/O-Optimized, aurora-iopt1, storage type switch, 25% threshold, I/O costs too high, storage comparison | — | — |
commitment-pricing |
Commitment Pricing | Compares Reserved Instances vs Database Savings Plans for provisioned clusters, and DSP-only for Aurora serverless. 1yr vs 3yr analysis. | Reserved Instance, RI, Savings Plan, DSP, 1yr vs 3yr, commitment, cost optimization, overpaying | serverless-advisory (optional) |
— |
upgrade-planning |
Upgrade Planning | Major and minor version upgrade planning for Aurora PostgreSQL. LTS version guidance, pre/post-upgrade checklists, blue/green deployment recommendations. | upgrade, version, LTS, pre-upgrade checklist, post-upgrade, major version, minor version, end of life, deprecation | — | — |
What ships with it
41 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/commitment-pricing-basics.md 5.4 KB
- references/commitment-pricing-instructions.md 7.3 KB
- references/commitment-pricing-mechanics.md 6.0 KB
- references/commitment-pricing-scenarios.md 4.4 KB
- references/commitment-pricing-skipped-cluster.md 4.1 KB
- references/commitment-pricing-worked-examples.md 5.8 KB
- references/create-instructions.md 7.3 KB
- references/express-create-comparison.md 7.6 KB
- references/express-create-connect-iam.md 2.8 KB
- references/express-create-constraints.md 9.4 KB
- references/express-create-documentation-links.md 3.8 KB
- references/express-create-feature-overview.md 7.4 KB
- references/express-create-instructions.md 7.8 KB
- references/express-create-migration-pgdump.md 2.0 KB
- references/express-create-migration.md 7.5 KB
- references/express-create-use-cases.md 7.7 KB
- references/io-optimized-data-collection.md 4.2 KB
- references/io-optimized-instructions.md 6.9 KB
- references/io-optimized-pricing-tables.md 6.3 KB
- references/io-optimized-pricing.md 3.0 KB
- references/io-optimized-worked-examples.md 4.6 KB
- references/serverless-advisory-concepts.md 3.2 KB
- references/serverless-advisory-formulas-and-examples.md 4.5 KB
- references/serverless-advisory-instructions.md 7.0 KB
- references/serverless-advisory-migration.md 6.9 KB
- references/serverless-advisory-worked-examples.md 7.2 KB
- references/shared-foundation-security-considerations.md 5.4 KB
- references/upgrade-planning-documentation-links.md 2.1 KB
- references/upgrade-planning-instructions.md 5.6 KB
- references/upgrade-planning-lts-recommendation.md 4.9 KB
- references/upgrade-planning-mode-b-discovery.md 4.9 KB
- references/upgrade-planning-mode-b-prechecks-checklists.md 4.7 KB
- references/upgrade-planning-post-checklist.md 2.9 KB
- references/upgrade-planning-post-upgrade-detail.md 6.4 KB
- references/upgrade-planning-post-upgrade-validation.md 4.2 KB
- references/upgrade-planning-pre-checklist.md 2.2 KB
- references/upgrade-planning-prechecks-postgresql.md 7.0 KB
- references/upgrade-planning-query-load-postgresql.md 6.6 KB
- scripts/acu_calculator.py 36 KB runs code
- scripts/commitment_pricing_analyzer.py 33 KB runs code
- scripts/io_optimized_analyzer.py 26 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 165 lines · 109 tokens per session scan A 756b1e84a83b
amazon-aurora-postgresql is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,886 stars, last pushed yesterday), licensed Apache-2.0. It adds 109 tokens to every session and 4,467 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to amazon-aurora-mysql, differing in 68 lines, and is treated as a copy.
Other skills, from other repositories
alloydb-basics
Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB Model Context Protocol (MCP) tools for automated database operations. Use when creating, configuring, or administering AlloyDB databases. Do NOT use for general PostgreSQL instances (e.g. Cloud SQL) or other GCP databases.
postgresql-table-design
Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features.
db-repair
Auto-fix gbrain's Postgres access so the brain stays available. When any gbrain command or MCP tool result carries a GBRAINDBACCESS marker (or an operator reports the brain database is down), run the hardcoded gbrain db-repair ladder: diagnose, apply the safe tier, verify. The action is ALWAYS the hardcoded command …
volcengine-rds-postgresql
A tool for operating PostgreSQL databases hosted by Volcano Engine's managed database service. PostgreSQL is a relational database used to store structured application data.
dsql
Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key…
analyzing-insights-across-teams
Analyze PostHog insights, dashboards, or teams beyond the current project by querying the prod Postgres replicas synced into the dogfood data warehouse (US project 2, "PostHog App + Website"). Use when asked to analyze insights across all teams or projects, another team's insights, or fleet-wide insight/dashboard…