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 migrating-to-amazon-redshiftgit 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/migrating-to-amazon-redshift)<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/migrating-to-amazon-redshift"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/migrating-to-amazon-redshift/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/migrating-to-amazon-redshift"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/migrating-to-amazon-redshift.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 62 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Rogue Agent · line 64 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00212 | $0.02583 |
| Opus 5.5 | $0.00085 | $0.01033 |
| Sonnet 5.5 | $0.00042 | $0.00517 |
| Haiku 4.5 | $0.00021 | $0.00258 |
Grade A, and why
migrating-to-amazon-redshift 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.
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Migrating to Amazon Redshift
What this skill is
This skill is AI guidance, not an execution framework. It is entirely Markdown knowledge (rules, mappings, patterns, best practices) — no executable code. All execution — conversion, the discovery/migration/validation runners, dependencies, and infrastructure — you (the AI) generate at runtime from this knowledge, tailored to the customer's environment.
Principle: knowledge over shipped code → less drift, nothing for the customer to run or depend on, reliable first-time results. Do not look for a pyproject, a tools package, an orchestrator engine, or shipped scripts — there are none by design; you generate execution.
Runtime: this skill works with or without the AWS MCP server — step guidance uses AWS CLI syntax. Running it with the AWS MCP server is recommended for sandboxed execution and audit logging; without it, the AI runs the generated scripts on the host shell (assumes Bash, Python 3, and AWS CLI + credentials). Do not assume MCP-only tools are available.
Source routing
This skill migrates a supported source data warehouse to Amazon Redshift. First identify the
source system, then load that source's knowledge under references/<source>/:
- Teradata (Vantage) →
references/teradata/— supported (all references below). - Other sources (e.g. Snowflake, Oracle) — unsupported; each is added as its own
references/<source>/set when ready.
The workflow is source-agnostic (discovery → convert → migrate → validate → performance → report); only the conversion knowledge is source-specific. Everything below is the Teradata set.
When to use
- Migrating a Teradata system (Vantage) to Amazon Redshift.
- Converting Teradata DDL, SQL, stored procedures, macros, or BTEQ to Redshift/RSQL.
- Assessing Teradata→Redshift migration complexity/effort.
Operating principles
- Discovery is strictly read-only (SELECT-only) on the source. Never change production
state: no DDL/DML, and never enable logging (
BEGIN/REPLACE QUERY LOGGING). If DBQL is empty, mark itunavailableand fall back to always-onDBC.AMPUsageV— seereferences/teradata/discovery-queries.md. - Skill provides knowledge; you generate execution. Read the
references/to reason and convert — apply the rules inreferences/teradata/conversion-rules.mddirectly for conversion, and generate the discovery/migration/validation runners (and the read-only discovery collector fromreferences/teradata/discovery-queries.md) tailored to the environment. - Generate, don't assume a framework. Assume the environment has Bash, Python 3, and AWS
CLI + credentials. Any Python lib a generated script needs (
teradatasql,boto3, …) ispip install-ed on demand by that script / its run-instructions — pin exact versions. Teradata TTU (BTEQ/TPT) is Linux/Windows-only — not macOS; prefer WRITE_NOS +teradatasql(cross-platform, no client) for discovery/extract unless a TTU/Linux host exists. - Credentials: use a read-only Teradata user; prefer IAM roles over IAM users. For
production, reference credentials from AWS Secrets Manager or Systems Manager
Parameter Store. For local development only, a git-ignored
.envfile or profile may be used — never commit it. Never hard-code or echo secrets. In a portable bundle, reference a co-located credentials file and ship acredentials.env.exampletemplate — the real file is git-ignored. - Persist state in files. All generated output goes under a git-ignored
output/in the user's working dir; keepoutput/state.mdcurrent so work is resumable.
What ships with it
13 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/teradata/architecture-mapping.md 5.8 KB
- references/teradata/bteq-to-rsql.md 4.1 KB
- references/teradata/common-errors.md 3.7 KB
- references/teradata/conversion-rules.md 17 KB
- references/teradata/data-migration-patterns.md 13 KB
- references/teradata/data-type-mapping.md 3.4 KB
- references/teradata/discovery-queries.md 15 KB
- references/teradata/orchestration.md 7.1 KB
- references/teradata/performance.md 6.6 KB
- references/teradata/reporting.md 4.2 KB
- references/teradata/sizing.md 7.3 KB
- references/teradata/stored-procedure-migration.md 5.1 KB
- references/teradata/validation-patterns.md 5.9 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.
- 1mo ago First seen · 160 lines · 212 tokens per session scan A f56a480455b2
migrating-to-amazon-redshift 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 212 tokens to every session and 2,583 once invoked, about $0.0008 per session on Opus 5.5. 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 2026-09-03.
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