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Official agent skill

Migrating To Amazon Redshift

by aws in aws/agent-toolkit-for-aws

Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting.

OfficialApache-2.0Auto-check: notesDatabases

Install Migrating To Amazon Redshift

skills CLI
$ npx skills add aws/agent-toolkit-for-aws --skill migrating-to-amazon-redshift -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aws/agent-toolkit-for-aws migrating-to-amazon-redshift --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/specialized-skills/analytics-skills/migrating-to-amazon-redshift .claude/skills/migrating-to-amazon-redshift && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
migrating-to-amazon-redshift
GitHub stars
2.8k
Token cost
~2.7k tokens
SKILL.md length
1,024 words
Files
14 (incl. references)
Skills in repo
138
Repo updated
First seen
Licence
Apache-2.0

At a glance

Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting.

  • Works in 6 steps: Discovery — inventory the source. →… → Conversion — schema + code. Apply the… → Data migration — extract → S3 → COPY,… → …
  • Tasks that involve Data warehousing
  • SKILL.md covers What this skill is, Source routing, When to use and Operating principles, plus 6 more sections
  • Calls pip

What it does

Migrating To Amazon Redshift is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Source-routed via references/<source/; Teradata (Vantage) is the supported source; additional sources are added as their own references/<source/ sets. Text-only knowledge (no executable code) — the AI generates all execution at runtime. Applies when a user wants to migrate Teradata to Amazon Redshift, convert Teradata…

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including reference files (for example `references/teradata/architecture-mapping.md`, `references/teradata/bteq-to-rsql.md` and `references/teradata/common-errors.md`).

It sits in Databases, covering Data warehousing. It works with SQL, Amazon Web Services, Snowflake and Databricks. The repository describes itself as: Official, AWS-supported MCP servers, skills, and plugins to help AI agents build on AWS. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Data warehousing

Example prompts

  • “Use the migrating-to-amazon-redshift skill to guide an end-to-end data-warehouse migration to Amazon Redshift — discovery…”
  • “/migrating-to-amazon-redshift”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Discovery — inventory the source. → references/teradata/discovery-queries.md (read-only collection SQL + BTEQ driver template the AI…
  2. Conversion — schema + code. Apply the conversion rules directly, flag the
  3. Data migration — extract → S3 → COPY, restartable. → references/teradata/data-migration-patterns.md
  4. Validation — counts/aggregates/sampling. → references/teradata/validation-patterns.md
  5. Performance — baseline vs Redshift; size the target. → references/teradata/performance.md, references/teradata/sizing.md
  6. Reporting — aggregate all phases. → references/teradata/reporting.md

What it can do on your machine

Read from SKILL.md and the folder at commit 188af2f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Migrating To Amazon Redshift loads about 2.7k tokens when it runs, and up to ~28k if it reads all its reference files. Until then it costs about 252 tokens; SKILL.md has 1,024 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~252
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~28k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:60
    local development only**, a git-ignored `.env` file or profile may

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aws/agent-toolkit-for-aws at commit 188af2f, republished under its Apache-2.0 licence (© aws). 1,024 words, ~2,722 tokens.

Download SKILL.mdSave it as .claude/skills/migrating-to-amazon-redshift/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.
name
migrating-to-amazon-redshift
description
Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Source-routed via `references/<source>/`; Teradata (Vantage) is the supported source; additional sources are added as their own `references/<source>/` sets. Text-only knowledge (no executable code) — the AI generates all execution at runtime. Applies when a user wants to migrate Teradata to Amazon Redshift, convert Teradata DDL/SQL/stored procedures/macros/BTEQ to Redshift/RSQL, or assess Teradata-to-Redshift migration complexity. Applies only to migrations targeting Amazon Redshift; migrations to other platforms (Snowflake, BigQuery, Databricks, etc.) are out of scope regardless of source. Does not cover general Redshift administration, performance tuning, or troubleshooting of existing Redshift clusters (no migration involved), or sources not listed under references/.
version
1

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 it unavailable and fall back to always-on DBC.AMPUsageV — see references/teradata/discovery-queries.md.
  • Skill provides knowledge; you generate execution. Read the references/ to reason and convert — apply the rules in references/teradata/conversion-rules.md directly for conversion, and generate the discovery/migration/validation runners (and the read-only discovery collector from references/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, …) is pip 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 .env file 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 a credentials.env.example template — 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; keep output/state.md current so work is resumable.

Workflow (phases)

Run in order; each phase's result/ feeds the next (see references/teradata/orchestration.md).

  1. Discovery — inventory the source. → references/teradata/discovery-queries.md (read-only collection SQL + BTEQ driver template the AI generates) → output/discovery/result/inventory.json
  2. Conversion — schema + code. Apply the conversion rules directly, flag the manual-rewrite long tail, and fix Redshift errors from the references. → references/teradata/conversion-rules.md, references/teradata/data-type-mapping.md, references/teradata/architecture-mapping.md, references/teradata/stored-procedure-migration.md, references/teradata/bteq-to-rsql.md, references/teradata/common-errors.md
  3. Data migration — extract → S3 → COPY, restartable. → references/teradata/data-migration-patterns.md
  4. Validation — counts/aggregates/sampling. → references/teradata/validation-patterns.md
  5. Performance — baseline vs Redshift; size the target. → references/teradata/performance.md, references/teradata/sizing.md
  6. Reporting — aggregate all phases. → references/teradata/reporting.md

Conversion (how the AI applies it)

There is no converter to run — convert by applying the rules in references/teradata/conversion-rules.md directly (with the type / architecture / stored-procedure / BTEQ references): apply the deterministic rules to the well-understood bulk, flag the manual-rewrite constructs with their suggested rewrites, assign a confidence per object, and fix any Redshift errors using references/teradata/common-errors.md. The reference docs are the single source of truth; conversion-rules.md includes golden input→output examples to match.

Show full SKILL.md (397 more words)Show less

Execution modes (connectivity)

  • Connected — your host can reach Teradata/Redshift → run the generated scripts in place.
  • Disconnected — it can't → generate a self-contained bundle under output/<phase>/ (script + co-located credentials template + relative result/ + run-instructions.md); the operator runs it on a reachable host and copies result/ back. The copied-back result/ is the durable state — read it (+ state.md) and continue.

Project-workspace layout (per migration run)

<project-workspace>/
  migration-config.yaml          # operator-authored: endpoints, scope, strategy
  .gitignore                     # ignores output/
  output/                        # everything generated (git-ignored)
    state.md                     # progress cursor
    discovery/   …  result/inventory.json
    conversion/  …  result/{ddl,sql,procedures,rsql}/  manual_review.json
    data_migration/ … result/{extract,load,templates}/  migration_manifest.json
    validation/  …  result/validation_report.json
    performance/ …  result/{perf_baseline,perf_compare}.json
    reporting/      result/migration_report.md

Security considerations

  • No shipped code or dependencies. This skill is text-only — the customer runs nothing from it. Any runner the AI generates MUST pin exact dependency versions, validate/sanitize inputs (file paths, SQL, shell args), and never print or log credentials, secrets, or PII.
  • Least privilege + ephemeral credentials. Use a read-only Teradata user for discovery. On AWS prefer IAM roles over IAM users and IAM auth over username/password. Keep secrets in AWS Secrets Manager / Parameter Store — never hard-code, echo, or commit them (credentials files are git-ignored; ship only *.example templates).
  • Data in transit / at rest. Use TLS to both engines; stage extracts in an encrypted S3 bucket (SSE) with a least-privilege bucket policy; load via COPY … IAM_ROLE (not access keys). Enable encryption on the target Redshift cluster.
  • Blast radius. Discovery is read-only by design. Migration writes to the target — validate against a throwaway / non-production Redshift first, and never point a generated write-path at production without explicit operator confirmation.
  • No secret leakage in artifacts. Generated output/… (manifests, reports, state.md) MUST NOT embed credentials or endpoints beyond what the operator supplies in migration-config.yaml.
  • COPY IAM_ROLE hardening. Scope the role's policy to the specific staging prefix (not bucket-wide s3:*), and include condition keys in its trust policy (aws:SourceAccount / aws:SourceArn, or sts:ExternalId for cross-account) to prevent confused-deputy assumption — per Redshift IAM-role authorization best practices.
  • Logging & monitoring. Enable CloudTrail (S3 data events on the staging bucket + Redshift management events), Redshift audit logging (connection/user-activity logs to S3 or CloudWatch), and CloudWatch alarms on COPY failures or unusual staging-bucket access during the migration.

The AWS MCP server (recommended runtime) additionally provides sandboxed execution and audit logging for the generated scripts.

References (specialized knowledge)

FileTopic
references/teradata/orchestration.mdphase workflow + state model
references/teradata/conversion-rules.mdthe 72 conversion rules (source of truth)
references/teradata/data-type-mapping.mdTD→RS type mapping
references/teradata/architecture-mapping.mdPI→DISTKEY, PPI→SORTKEY, Join Index→MV
references/teradata/stored-procedure-migration.mdSP → PL/pgSQL
references/teradata/bteq-to-rsql.mdBTEQ → RSQL
references/teradata/common-errors.mdcommon Redshift errors + fixes
references/teradata/discovery-queries.mdDBC system-view inventory queries
references/teradata/data-migration-patterns.mdCOPY/TPT/micro-batch/checkpoint
references/teradata/validation-patterns.mdrow-count/aggregate/sample compare
references/teradata/performance.mdrepresentative-query extraction + compare
references/teradata/sizing.mdRG node type + count from the source profile
references/teradata/reporting.mdmigration status-report generation

© aws, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 13 other files (references) in skills/specialized-skills/analytics-skills/migrating-to-amazon-redshift of aws/agent-toolkit-for-aws.

  • SKILL.md
  • references/teradata/architecture-mapping.md
  • references/teradata/bteq-to-rsql.md
  • references/teradata/common-errors.md
  • references/teradata/conversion-rules.md
  • references/teradata/data-migration-patterns.md
  • references/teradata/data-type-mapping.md
  • references/teradata/discovery-queries.md
  • references/teradata/orchestration.md
  • references/teradata/performance.md
  • references/teradata/reporting.md
  • references/teradata/sizing.md
  • references/teradata/stored-procedure-migration.md
  • references/teradata/validation-patterns.md

Open the folder on GitHubat commit 188af2f

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Works with

Categories

Questions about Migrating To Amazon Redshift

What does Migrating To Amazon Redshift do?

Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Migrating To Amazon Redshift is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting.

When should I use Migrating To Amazon Redshift?

Migrating To Amazon Redshift fits situations like: tasks that involve Data warehousing.

How do I install Migrating To Amazon Redshift in Claude Code?

Run `npx skills add aws/agent-toolkit-for-aws --skill migrating-to-amazon-redshift -a claude-code`. Or copy the skill folder (skills/specialized-skills/analytics-skills/migrating-to-amazon-redshift in aws/agent-toolkit-for-aws) into .claude/skills/migrating-to-amazon-redshift in your project. Claude Code loads it when a task matches its description.

How do I install Migrating To Amazon Redshift in Codex?

Run `npx skills add aws/agent-toolkit-for-aws --skill migrating-to-amazon-redshift -a codex`. Or copy the skill folder (skills/specialized-skills/analytics-skills/migrating-to-amazon-redshift in aws/agent-toolkit-for-aws) into .agents/skills/migrating-to-amazon-redshift in your project. Codex loads it when a task matches its description.

Can I use Migrating To Amazon Redshift in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aws/agent-toolkit-for-aws --skill migrating-to-amazon-redshift -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/migrating-to-amazon-redshift, .gemini/skills/migrating-to-amazon-redshift, .github/skills/migrating-to-amazon-redshift and .opencode/skills/migrating-to-amazon-redshift in your project.

What does Migrating To Amazon Redshift need to run?

Going by SKILL.md and its folder, Migrating To Amazon Redshift needs the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Migrating To Amazon Redshift access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Migrating To Amazon Redshift safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Migrating To Amazon Redshift use?

Migrating To Amazon Redshift is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Migrating To Amazon Redshift use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 25k tokens, read only when the agent opens those files.

What are the alternatives to Migrating To Amazon Redshift?

Skills that share tags, products or a category with Migrating To Amazon Redshift: SQL Queries (w95/awesome-claude-corporate-skills, 237 stars), Airflow State Store (astronomer/agents, 451 stars), Semantic Analyst (sidequery/sidemantic, 129 stars) and Rocky New Adapter (rocky-data/rocky, 304 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Migrating To Amazon Redshift?

aws (a GitHub organization, an official publisher) maintains it in aws/agent-toolkit-for-aws, which has 2,825 GitHub stars. The repository holds 138 skills in this directory. The repository was last updated on October 7, 2026.

Source: aws/agent-toolkit-for-aws on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.