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 agentmods add skills/aws/agent-toolkit-for-aws/aws-databasenpx skills add aws/agent-toolkit-for-aws --skill aws-databasegit 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/aws-database)<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/aws-database"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/aws-database.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00141 | $0.01750 |
| Opus 5 | $0.00071 | $0.00875 |
| Sonnet 5 | $0.00028 | $0.00350 |
| Haiku 4.5 | $0.00014 | $0.00175 |
Grade A, and why
aws-database 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AWS Database
STOP — Do not answer from general knowledge. Before responding to any database question, match the user's request against the sub-skill registry below and follow its procedure. If the procedure says to hand off to a service skill, you MUST load that skill before providing operational guidance. Never skip the routing step.
AWS Databases comprise 15+ fully-managed database engines and offer a high-performance, secure, and reliable foundation to power agentic AI and data-driven applications. Each AWS database is optimized for a specific workload shape or data model — relational (Aurora, DSQL, RDS), key-value (DynamoDB), wide-column (Keyspaces), document (DocumentDB), graph (Neptune), time-series (Timestream), and in-memory (ElastiCache, MemoryDB). For relational workloads, AWS supports PostgreSQL (Aurora, DSQL, RDS), MySQL (Aurora, RDS), MariaDB (RDS), Oracle (RDS, ODB@AWS), SQL Server (RDS), and Db2 (Db2).
Use this skill as the entry point for any actions or questions related to databases on AWS. It helps match a workload to the right AWS database service, or hand off to a service-specific skill for operational questions or actions.
This skill works with or without the AWS MCP server. When available, the AWS MCP server is recommended for sandboxed execution and audit logging.
Global rules
-
Match the user's language. Respond in the same language the user writes in. Default to non-technical explanations. Only escalate technical depth when they've shown fluency — by using the terms themselves, stating a technical role, or answering a plain question with a technical answer.
-
Revise when new information arrives. If the user pushes back or adds new details, re-check the sub-skill registry triggers before responding. Pushback that matches
report-issuetriggers (e.g., "that's wrong", "it's wrong", "you picked the wrong service") must route toreport-issue— do not defend your prior recommendation or ask the user to justify their objection. The goal is the right answer, not consistency with your first response.
What ships with it
20 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.
- assets/aurora-dsql.md 1.3 KB
- assets/aurora-mysql.md 1.6 KB
- assets/aurora-postgresql.md 2.2 KB
- assets/documentdb.md 1.4 KB
- assets/dynamodb.md 2.1 KB
- assets/elasticache.md 3.6 KB
- assets/keyspaces.md 2.0 KB
- assets/memorydb.md 1.7 KB
- assets/neptune.md 2.1 KB
- assets/odb-aws.md 1.6 KB
- assets/rds-db2.md 1.1 KB
- assets/rds-mariadb.md 1.5 KB
- assets/rds-mysql.md 1.2 KB
- assets/rds-oracle.md 1.5 KB
- assets/rds-postgresql.md 1.4 KB
- assets/rds-sqlserver.md 1.3 KB
- assets/timestream.md 1.7 KB
- references/handoff.md 4.1 KB
- references/report-issue.md 3.9 KB
- references/select.md 19 KB
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.
- 4d ago First seen · 67 lines · 141 tokens per session scan A f9afc2f64c97
aws-database is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,498 stars, last pushed yesterday), licensed Apache-2.0. It adds 141 tokens to every session and 1,750 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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