Mfs Find
zilliztech/mfs
Search, grep, browse, and read across registered MFS data sources via the mfs CLI — codebases, docs, PDFs, web crawls, databases (postgres/mysql/mongo/snowflake/bigquery), issue trackers…
Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue…
Install Ingesting Into Data Lake
$ npx skills add aws/agent-toolkit-for-aws --skill ingesting-into-data-lake -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aws/agent-toolkit-for-aws ingesting-into-data-lake --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/aws-data-analytics/skills/ingesting-into-data-lake .claude/skills/ingesting-into-data-lake && rm -rf skills-srcUse ~/.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/
Install the "ingesting-into-data-lake" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/ingesting-into-data-lake into .claude/skills/ingesting-into-data-lake/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ingesting-into-data-lake", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/ingesting-into-data-lakeType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aws/agent-toolkit-for-aws --skill ingesting-into-data-lake -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aws/agent-toolkit-for-aws ingesting-into-data-lake --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/aws-data-analytics/skills/ingesting-into-data-lake .agents/skills/ingesting-into-data-lake && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ingesting-into-data-lake" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/ingesting-into-data-lake into .agents/skills/ingesting-into-data-lake/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ingesting-into-data-lake", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws/agent-toolkit-for-aws --skill ingesting-into-data-lake -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aws/agent-toolkit-for-aws ingesting-into-data-lake --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/aws-data-analytics/skills/ingesting-into-data-lake .cursor/skills/ingesting-into-data-lake && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ingesting-into-data-lake" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/ingesting-into-data-lake into .cursor/skills/ingesting-into-data-lake/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ingesting-into-data-lake", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aws/agent-toolkit-for-aws.git --path plugins/aws-data-analytics/skills/ingesting-into-data-lake--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aws/agent-toolkit-for-aws --skill ingesting-into-data-lake -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aws/agent-toolkit-for-aws ingesting-into-data-lake --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/aws-data-analytics/skills/ingesting-into-data-lake .gemini/skills/ingesting-into-data-lake && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ingesting-into-data-lake" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/ingesting-into-data-lake into .gemini/skills/ingesting-into-data-lake/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ingesting-into-data-lake", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aws/agent-toolkit-for-aws ingesting-into-data-lakeInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aws/agent-toolkit-for-aws --skill ingesting-into-data-lake -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/aws-data-analytics/skills/ingesting-into-data-lake .github/skills/ingesting-into-data-lake && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ingesting-into-data-lake" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/ingesting-into-data-lake into .github/skills/ingesting-into-data-lake/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ingesting-into-data-lake", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws/agent-toolkit-for-aws --skill ingesting-into-data-lake -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aws/agent-toolkit-for-aws ingesting-into-data-lake --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/aws-data-analytics/skills/ingesting-into-data-lake .opencode/skills/ingesting-into-data-lake && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ingesting-into-data-lake" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/ingesting-into-data-lake into .opencode/skills/ingesting-into-data-lake/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ingesting-into-data-lake", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
ingesting-into-data-lake- GitHub stars
- 2.8k
- Used in
- 1 other repo
- Token cost
- ~2.8k tokens
- SKILL.md length
- 1,064 words
- Files
- 26 (incl. references)
- Skills in repo
- 138
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue…
- Works in 7 steps: Verify Dependencies and Context → Classify the Source → Confirm Connection Exists (if applicable) → …
- Move data to AWS
- SKILL.md covers Philosophy, Common Tasks, Workflow and Argument Routing, plus 3 more sections
- Calls aws
What it does
Ingesting Into Data Lake is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL…
Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 26 other files, including reference files (for example `references/athena-loading.md`, `references/bigquery-ingest.md` and `references/catalog-migration.md`).
It sits in Databases, covering Data warehousing, NoSQL databases and File uploads and storage. It works with Amazon Web Services, Amazon DynamoDB, Snowflake and Google BigQuery. 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
- Move data to AWS
- Set up pipeline
- Pull from Snowflake
- Query BigQuery into S3
Example prompts
- “/ingesting-into-data-lake”
Workflow steps
7 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit bd49cc8. 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:
aws
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use aws, 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
Ingesting Into Data Lake loads about 2.8k tokens when it runs, and up to ~50k if it reads all its reference files. Until then it costs about 249 tokens; SKILL.md has 1,064 words of instructions outside code blocks.
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 passed
The automated check found no risky patterns in SKILL.md.
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 bd49cc8, republished under its Apache-2.0 licence (© aws). 1,064 words, ~2,769 tokens.
Download SKILL.mdSave it as
.claude/skills/ingesting-into-data-lake/SKILL.md (or your agent's skills folder). This skill also uses 25 other files; get the full folder from GitHub.- name
- ingesting-into-data-lake
- description
- Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.
- metadata.version
- 1
- metadata.argument-hint
- '[source-path|connection-name|table-name] [--target s3-tables|iceberg|parquet]'
Ingest into Data Lake
Move data from a source into a queryable table in the data lake. This skill assumes the source connection (if one is needed) already exists. For Glue connection setup or troubleshooting, delegate to connecting-to-data-source.
Philosophy
Default to S3 Tables unless the environment says otherwise. S3 Tables is the recommended target for new data lake work. If the user's catalog inventory shows they haven't adopted S3 Tables, recommend standard Iceberg on their existing general-purpose bucket instead of forcing them to change posture.
Common Tasks
You MUST execute commands using AWS MCP server tools when connected -- they provide validation, sandboxed execution, and audit logging. Fall back to AWS CLI only if MCP is unavailable. You MUST explain each step before executing.
Workflow
1. Verify Dependencies and Context
- You MUST check whether AWS MCP tools or AWS CLI are available and inform the user if missing
- You MUST confirm target AWS region and verify credentials with
aws sts get-caller-identity - For SageMaker Unified Studio project roles, note that target tables and connections may be scoped to the project. See the caller ARN detection pattern in
querying-data-lake.
2. Classify the Source
| User says... | Source type | Reference |
|---|---|---|
| "upload my file", "local CSV", "move to S3" | Local file | local-upload.md |
| "load from S3", "import CSV/JSON/Parquet from s3://" | S3 files | s3-files.md |
| "import from Oracle/Postgres/MySQL/SQL Server/Redshift/RDS/Aurora" | JDBC | jdbc-ingest.md |
| "pull from Snowflake", "Snowflake table to S3" | Snowflake | snowflake-ingest.md |
| "import from BigQuery", "GCP analytics to S3" | BigQuery | bigquery-ingest.md |
| "export DynamoDB", "DynamoDB to data lake" | DynamoDB | dynamodb-ingest.md |
| "migrate Glue table", "convert Hive to Iceberg" | Catalog migration | catalog-migration.md |
If the user names Salesforce, ServiceNow, SAP, MongoDB, Kafka, or another SaaS/streaming source, decline -- these are not supported in this release.
If the source table is referenced by a fuzzy or business name ("migrate our orders table", "pull from the sales warehouse"), delegate to finding-data-lake-assets to resolve before proceeding.
3. Confirm Connection Exists (if applicable)
For JDBC, Snowflake, and BigQuery sources, a Glue connection is required. Check:
aws glue get-connection --name <CONNECTION_NAME> --region <REGION>If the connection does not exist, stop and delegate to connecting-to-data-source to create and test it. Do not proceed with ingest until the connection is verified.
Local files, S3 files, DynamoDB, and catalog migration do not need a Glue connection.
4. Clarify the Target
You MUST ask the user (or suggest based on catalog inventory) before creating or writing to any table:
- Database/namespace: Does a specific target database exist? Or should one be created?
- Table: Existing table (append/merge) or new table (delegate to
creating-data-lake-table)? - Format: S3 Tables (default), standard Iceberg, or raw Parquet?
Inventory-aware defaults:
If you have already run exploring-data-catalog or can quickly check, use what exists:
- Account has an
s3tablescatalogfederated catalog and active table buckets: recommend S3 Tables - Account has general-purpose buckets with Iceberg tables and no S3 Tables usage: recommend standard Iceberg on their existing bucket
- Account uses Parquet/ORC on S3 without Iceberg metadata: ask whether to adopt Iceberg now (recommend yes) or continue with raw files
Do not force S3 Tables on customers who haven't adopted it. See iceberg-catalog-config-and-usage.md.
Delegations from this step:
- Target table doesn't exist ->
creating-data-lake-table - Target database named by fuzzy term ->
finding-data-lake-assets - User doesn't know what exists ->
exploring-data-catalog
5. Execute Source Workflow
Read the source-specific reference and follow its phases. Each is self-contained with job templates, gotchas, and troubleshooting:
- Local / S3 / JDBC / Snowflake / BigQuery / DynamoDB / catalog migration -- one reference per source
Common Glue 5.1 or higher job configuration and PySpark templates are shared in glue-job-config.md and glue-job-scripts.md.
6. Validate
Run all three, do not skip:
- Row count matches expected (source vs target)
- Null check on critical columns
- Spot-check 3-5 sample rows
See data-quality-validation.md.
7. Schedule (if recurring)
For recurring pipelines, create a Glue Trigger with a cron schedule. See testing-and-scheduling.md. Simple single-step pipelines use Glue Triggers; multi-step with branching uses MWAA.
Show full SKILL.md (437 more words)Show less
Argument Routing
- S3 path only: Infer one-time load, start Step 2 with S3 files
- Connection name: Start Step 3 with the named connection
- Table name: Start Step 4, ask whether this is source or target
--targetflag: Pre-fill the target format in Step 4- No args: Walk through interactively
Gotchas
- S3 Tables requires Glue 5.1 or higher and
--datalake-formats icebergjob argument - All
spark.sql.catalog.*config MUST go in--confjob arguments, never inspark.conf.set(). Glue 5.x throwsAnalysisException: Cannot modify the value of a static configotherwise. See iceberg-catalog-config-and-usage.md for correct catalog configs. - The
warehouseparameter is required in S3 Tables catalog config. Without it Spark fails with "Cannot derive default warehouse location". - Table and column names in S3 Tables MUST be all lowercase
overwritePartitions()only replaces partitions present in the DataFrame -- for full refresh with deletes, usecreateOrReplace()- Standard Iceberg targets MUST include a LOCATION clause; S3 Tables MUST NOT
- DynamoDB does not need a Glue connection -- do not attempt to create one
- Connection failures during ingest delegate back to
connecting-to-data-source; do not debug network/credentials in this skill - For target tables in SageMaker Unified Studio projects, ensure the project role has write access to the target namespace before the Glue job runs
Troubleshooting
| Error | Likely cause | Action |
|---|---|---|
| Access Denied on S3 | Missing IAM permissions | Check Glue role has s3:GetObject, s3:PutObject |
| Access Denied on S3 Tables | Missing s3tables:* permissions | Add S3 Tables inline policy to Glue role |
| CTAS timeout | Dataset too large for Athena | Switch to Glue ETL or batch with WHERE filters |
| JDBC connection timeout/auth failure | Connection-level issue | Delegate to connecting-to-data-source |
| Throughput exceeded (DynamoDB) | Read percent too high | Lower read.percent or use native export |
See error-handling.md for the full catalog.
References
Source-specific
- local-upload.md -- Local files
- s3-files.md -- S3 files (CSV, JSON, Parquet, Avro, ORC)
- jdbc-ingest.md -- Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora, Redshift
- snowflake-ingest.md -- Snowflake
- bigquery-ingest.md -- BigQuery
- dynamodb-ingest.md -- DynamoDB (export and Glue direct read)
- catalog-migration.md -- Existing Glue catalog tables (Hive, self-managed Iceberg)
Cross-cutting
- iceberg-catalog-config-and-usage.md -- S3 Tables, standard Iceberg, raw files: catalog config, engine access patterns
- glue-job-config.md -- Job sizing, monitoring, retry
- glue-job-scripts.md -- PySpark templates (append, upsert, custom SQL, full refresh)
- incremental-loading.md -- Watermark strategies
- testing-and-scheduling.md -- Glue Triggers, MWAA
- data-quality-validation.md -- Row counts, null checks, Glue Data Quality
- schema-evolution.md -- ALTER TABLE ADD COLUMNS, nested JSON
- type-transformations.md -- Type conflict resolution
- format-specific-loading.md -- CSV/JSON/Parquet/Avro/ORC specifics
- athena-loading.md -- Athena INSERT INTO as simple-load fallback
- error-handling.md -- Ingest errors (connection errors delegate to connecting-to-data-source)
- upload-options.md -- aws s3 cp vs sync, multipart
Migration-specific
- ctas-patterns.md -- Athena CTAS syntax and partition transforms
- glue-etl-migration.md -- Large-table migration via Glue 5.1 or higher PySpark
- migration-validation.md -- Full validation checklist
- migration-troubleshooting.md -- CTAS failures, visibility, partitions
JDBC-specific
- jdbc-schema-discovery.md -- Crawler, direct inspection, custom SQL
- jdbc-performance.md -- Parallel reads, partitioning
© 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 25 other files (references) in plugins/aws-data-analytics/skills/ingesting-into-data-lake of aws/agent-toolkit-for-aws.
- SKILL.md
- references/athena-loading.md
- references/bigquery-ingest.md
- references/catalog-migration.md
- references/ctas-patterns.md
- references/data-quality-validation.md
- references/dynamodb-ingest.md
- references/error-handling.md
- references/format-specific-loading.md
- references/glue-etl-migration.md
- references/glue-job-config.md
- references/glue-job-scripts.md
- references/iceberg-catalog-config-and-usage.md
- references/incremental-loading.md
- references/jdbc-ingest.md
- references/jdbc-performance.md
- references/jdbc-schema-discovery.md
- references/local-upload.md
- references/migration-troubleshooting.md
- references/migration-validation.md
- … and 6 more
Open the folder on GitHubat commit bd49cc8
Used in 1 other repository
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in aws/agent-toolkit-for-aws, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Ingesting Into Data Lake next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Ingesting Into Data Lake this skillaws/agent-toolkit-for-aws | 2.8k | 1 repos | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Mfs Findzilliztech/mfs | 150 | — | ~4k | Automated safety check: Pass | Apache-2.0 | |
| Mfs Ingestzilliztech/mfs | 150 | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| AWS CLI Beastgiuseppe-trisciuoglio/developer-kit | 355 | — | ~1.7k | Automated safety check: Notes | MIT | |
| AWSkid-sid/claude-spellbook | 189 | — | ~4.9k | Automated safety check: Warn | MIT | |
| SQL Query Explainermohitagw15856/pm-claude-skills | 1.4k | — | ~1.6k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Ingesting Into Data Lake
What does Ingesting Into Data Lake do?
Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue…. Ingesting Into Data Lake is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration).
When should I use Ingesting Into Data Lake?
Ingesting Into Data Lake fits situations like: move data to AWS; set up pipeline; pull from Snowflake; query BigQuery into S3.
How do I install Ingesting Into Data Lake in Claude Code?
Run `npx skills add aws/agent-toolkit-for-aws --skill ingesting-into-data-lake -a claude-code`. Or copy the skill folder (plugins/aws-data-analytics/skills/ingesting-into-data-lake in aws/agent-toolkit-for-aws) into .claude/skills/ingesting-into-data-lake in your project. Claude Code loads it when a task matches its description.
How do I install Ingesting Into Data Lake in Codex?
Run `npx skills add aws/agent-toolkit-for-aws --skill ingesting-into-data-lake -a codex`. Or copy the skill folder (plugins/aws-data-analytics/skills/ingesting-into-data-lake in aws/agent-toolkit-for-aws) into .agents/skills/ingesting-into-data-lake in your project. Codex loads it when a task matches its description.
Can I use Ingesting Into Data Lake 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 ingesting-into-data-lake -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ingesting-into-data-lake, .gemini/skills/ingesting-into-data-lake, .github/skills/ingesting-into-data-lake and .opencode/skills/ingesting-into-data-lake in your project.
What does Ingesting Into Data Lake need to run?
Going by SKILL.md and its folder, Ingesting Into Data Lake needs the command-line tools its instructions call (aws).
Does Ingesting Into Data Lake access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Ingesting Into Data Lake safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Ingesting Into Data Lake use?
Ingesting Into Data Lake 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 Ingesting Into Data Lake use?
About 2.8k 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 47k tokens, read only when the agent opens those files.
What are the alternatives to Ingesting Into Data Lake?
Skills that share tags, products or a category with Ingesting Into Data Lake: Mfs Find (zilliztech/mfs, 150 stars), Mfs Ingest (zilliztech/mfs, 150 stars), AWS CLI Beast (giuseppe-trisciuoglio/developer-kit, 355 stars) and AWS (kid-sid/claude-spellbook, 189 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Ingesting Into Data Lake?
aws (a GitHub organization, an official publisher) maintains it in aws/agent-toolkit-for-aws, which has 2,816 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.