Chdb SQL
vemetric/vemetric
A skill your agent uses when the user wants to run SQL — especially analytical SQL — on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse…
Execute and manage Athena SQL queries across default and federated catalogs (Glue, S3 Tables, Redshift).
Install Querying Data Lake
$ npx skills add aws/agent-toolkit-for-aws --skill querying-data-lake -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aws/agent-toolkit-for-aws querying-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/querying-data-lake .claude/skills/querying-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 "querying-data-lake" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/querying-data-lake into .claude/skills/querying-data-lake/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "querying-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/querying-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 querying-data-lake -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aws/agent-toolkit-for-aws querying-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/querying-data-lake .agents/skills/querying-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 "querying-data-lake" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/querying-data-lake into .agents/skills/querying-data-lake/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "querying-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 querying-data-lake -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aws/agent-toolkit-for-aws querying-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/querying-data-lake .cursor/skills/querying-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 "querying-data-lake" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/querying-data-lake into .cursor/skills/querying-data-lake/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "querying-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/querying-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 querying-data-lake -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aws/agent-toolkit-for-aws querying-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/querying-data-lake .gemini/skills/querying-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 "querying-data-lake" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/querying-data-lake into .gemini/skills/querying-data-lake/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "querying-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 querying-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 querying-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/querying-data-lake .github/skills/querying-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 "querying-data-lake" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/querying-data-lake into .github/skills/querying-data-lake/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "querying-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 querying-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 querying-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/querying-data-lake .opencode/skills/querying-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 "querying-data-lake" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/querying-data-lake into .opencode/skills/querying-data-lake/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "querying-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
querying-data-lake- GitHub stars
- 2.8k
- Token cost
- ~1.9k tokens
- SKILL.md length
- 888 words
- Files
- 3 (incl. references)
- Skills in repo
- 138
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Execute and manage Athena SQL queries across default and federated catalogs (Glue, S3 Tables, Redshift).
- Works in 7 steps: Verify Dependencies → Resolve Workgroup → Resolve the Target Asset → …
- Phrases like: query data
- SKILL.md covers Overview, Common Tasks, Troubleshooting and Additional Resources
- Calls aws
What it does
Querying Data Lake is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Execute and manage Athena SQL queries across default and federated catalogs (Glue, S3 Tables, Redshift). Triggers on phrases like: query data, run SQL, athena query, analyze table, SQL query, workgroup status, profile table, query Redshift catalog, query S3 Tables. Do NOT use for finding specific data assets (use finding-data-lake-assets), full catalog audits (use exploring-data-catalog), importing data (use ingesting-into-data-lake).
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/query-patterns.md` and `references/workgroup-selection.md`).
It sits in Databases, covering SQL, File uploads and storage and Data warehousing. It works with SQL, Amazon Web Services and Model Context Protocol. 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
- Phrases like: query data
- Workgroup status
- Query Redshift catalog
- Query S3 Tables
Example prompts
- “/querying-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 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:
aws
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
docs.aws.amazon.com
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
Querying Data Lake loads about 1.9k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 114 tokens; SKILL.md has 888 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 188af2f, republished under its Apache-2.0 licence (© aws). 888 words, ~1,934 tokens.
Download SKILL.mdSave it as
.claude/skills/querying-data-lake/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- querying-data-lake
- description
- Execute and manage Athena SQL queries across default and federated catalogs (Glue, S3 Tables, Redshift). Triggers on phrases like: query data, run SQL, athena query, analyze table, SQL query, workgroup status, profile table, query Redshift catalog, query S3 Tables. Do NOT use for finding specific data assets (use finding-data-lake-assets), full catalog audits (use exploring-data-catalog), importing data (use ingesting-into-data-lake).
- metadata.version
- 1
- metadata.argument-hint
- '[SQL-query|query-name|workgroup-name|catalog-name|''profile TABLE_NAME'']'
Query Data Lake
Execute SQL queries on Amazon Athena across default and federated catalogs (Glue, S3 Tables, Redshift) with workgroup selection, statement classification, and error recovery.
Overview
Executes and manages Athena SQL queries across default and federated catalogs. Selects a workgroup, resolves target assets (delegating fuzzy references to finding-data-lake-assets), classifies statements for safety, and reports cost and data scanned. Use the AWS MCP server for sandboxed execution and audit logging; the same AWS CLI commands work directly when the MCP server is not available.
Constraints for parameter acquisition:
- You MUST accept a single optional argument: SQL text, a named-query name, a workgroup name, a catalog name, or
profile TABLE_NAME - You MUST accept the argument as direct text or a pointer to a file containing SQL
- You MUST ask the user for the target AWS region if not already set
- You MUST confirm the output S3 location before executing any non-trivial query
- You MUST respect the user's decision to abort at any step
Common Tasks
1. Verify Dependencies
Check for required tools and AWS access before running queries.
Constraints:
- You MUST verify AWS MCP server tools are available (
aws___call_aws) and run queries through them when present; fall back to AWS CLI only if the MCP server is unavailable - You MUST NOT fall back to shell or Bash for query execution — results must be captured via the MCP tool or
aws athenaCLI so output location and cost are tracked - You MUST confirm credentials with
aws sts get-caller-identityand inform the user about any missing tools
2. Resolve Workgroup
Check caller identity, list workgroups, auto-select the best one (see workgroup-selection.md).
Constraints:
- You MUST select a workgroup before submitting any query (prevents output-location errors)
- You MUST present the selected workgroup and its output location to the user
- You MUST NOT auto-escalate to a different workgroup on failure without user confirmation
3. Resolve the Target Asset
If the user refers to a table by name, by business concept ("our quarterly report", "the sales data"), by S3 path, or by catalog without specifying the table, delegate to finding-data-lake-assets to return the concrete database.table (and catalog if non-default).
Constraints:
- You MUST NOT attempt to resolve fuzzy asset references with
athena list-data-catalogsor by iteratingget-tables— those miss federated catalogs and waste tokens - You SHOULD skip this step only when the user provides a fully-qualified reference (exact
database.table) or raw SQL they want executed as-is - You MUST state the resolved asset explicitly before building the query: "Found [table] in [catalog]. Using this for the query."
- You SHOULD default to the default Glue catalog unless the user mentions "federated", "Redshift", "S3 Tables", or
finding-data-lake-assetsreturns a different catalog
4. Discover Schema
For analytical queries, You SHOULD profile the target table before building the final query. You MUST show sample rows (SELECT ... LIMIT 5) as part of profiling.
5. Build Query
Table addressing depends on catalog type:
- Default Glue catalog:
database.table(omit the catalog prefix for single-catalog queries). In cross-catalog queries, qualify default-catalog tables with"awsdatacatalog".database.table. - Registered data source:
datasource.database.table - Unregistered Glue catalog:
"catalog/subcatalog".database.table
Show full SKILL.md (383 more words)Show less
6. Classify and Execute
Classify the SQL statement before executing:
| Statement | Behavior |
|---|---|
SELECT, SHOW, DESCRIBE, EXPLAIN | Safe — execute |
INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, TRUNCATE, MERGE | Destructive — warn the user and require explicit confirmation |
| Unsure | Treat as destructive; confirm |
Example tool call (via AWS MCP server):
aws___call_aws(command="aws athena start-query-execution --work-group <WORKGROUP_NAME> --query-string '<sql>' --query-execution-context Database=<db>")For federated or S3 Tables catalogs, also set Catalog=<CATALOG_PATH> in the execution context (e.g. Catalog=s3tablescatalog/<BUCKET_NAME>).
Constraints:
- You MUST warn the user before executing when the target is Redshift-federated ("No partition pruning — every query scans the full table")
- You MUST warn the user before executing a cross-catalog join ("Cross-catalog joins incur network overhead and may be slow")
- You MUST confirm the output S3 location before executing
- You MUST explain which tool is being called before executing
- You MUST respect the user's decision to abort
7. Present and Recover
Present results with cost, data scanned, duration, and actionable insights. On failure, list available workgroups and let the user choose which to retry with.
Argument Routing
Resolve in this order; stop at the first match:
- Contains SQL keywords (
SELECT,SHOW,DESCRIBE,INSERT, etc.) — SQL text, execute directly profile TABLE_NAME— run comprehensive table profiling (see query-patterns.md)- Matches a known named query — look up and execute
- Matches a known workgroup — show workgroup status and recent queries
- Matches a known catalog — delegate to
exploring-data-catalogto enumerate databases and tables - No args — show recent query activity and available tables
Principles
- Always select workgroup before executing (prevents output-location errors)
- Profile unfamiliar tables before running analytical queries
- Present cost alongside results so users build cost awareness
- Suggest
LIMITfor exploratory queries on large tables - Never ask domain questions with obvious answers, but always confirm security-relevant actions (workgroup switches, output location changes, non-SELECT statements)
Troubleshooting
| Error | Cause | Fix |
|---|---|---|
| Redshift identifier error with mixed case | Redshift-federated names are lowercase only | Lowercase the identifier |
CatalogId validation failure | ARN passed instead of catalog name | Pass the catalog name, not the ARN |
Cross-catalog information_schema returns nothing | Missing catalog qualifier | Use catalog-qualified path: "catalog".information_schema.tables |
| Query fails with output-location error | Workgroup has no output location configured | Select a different workgroup with an output location, or configure one |
| Destructive statement executed without confirmation | Statement classification skipped | Always classify INSERT/UPDATE/DELETE/DROP/ALTER/CREATE/TRUNCATE/MERGE and confirm with the user |
Additional Resources
© 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 2 other files (references) in plugins/aws-data-analytics/skills/querying-data-lake of aws/agent-toolkit-for-aws.
- SKILL.md
- references/query-patterns.md
- references/workgroup-selection.md
Open the folder on GitHubat commit 188af2f
Compare with similar skills
Querying 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 |
|---|---|---|---|---|---|---|
| Querying Data Lake this skillaws/agent-toolkit-for-aws | 2.8k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Chdb SQLvemetric/vemetric | 394 | 1 repos | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Modelersidequery/sidemantic | 129 | — | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Semantic Analystsidequery/sidemantic | 129 | — | ~982 | Automated safety check: Pass | AGPL-3.0 | |
| Pytorch Clickhousepytorch/test-infra | 113 | — | ~2.8k | Automated safety check: Pass | Custom licence | |
| Imaging Data CommonsK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~7.8k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Querying Data Lake
What does Querying Data Lake do?
Execute and manage Athena SQL queries across default and federated catalogs (Glue, S3 Tables, Redshift). Querying Data Lake is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Execute and manage Athena SQL queries across default and federated catalogs (Glue, S3 Tables, Redshift).
When should I use Querying Data Lake?
Querying Data Lake fits situations like: phrases like: query data; workgroup status; query Redshift catalog; query S3 Tables.
How do I install Querying Data Lake in Claude Code?
Run `npx skills add aws/agent-toolkit-for-aws --skill querying-data-lake -a claude-code`. Or copy the skill folder (plugins/aws-data-analytics/skills/querying-data-lake in aws/agent-toolkit-for-aws) into .claude/skills/querying-data-lake in your project. Claude Code loads it when a task matches its description.
How do I install Querying Data Lake in Codex?
Run `npx skills add aws/agent-toolkit-for-aws --skill querying-data-lake -a codex`. Or copy the skill folder (plugins/aws-data-analytics/skills/querying-data-lake in aws/agent-toolkit-for-aws) into .agents/skills/querying-data-lake in your project. Codex loads it when a task matches its description.
Can I use Querying 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 querying-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/querying-data-lake, .gemini/skills/querying-data-lake, .github/skills/querying-data-lake and .opencode/skills/querying-data-lake in your project.
What does Querying Data Lake need to run?
Going by SKILL.md and its folder, Querying Data Lake needs the command-line tools its instructions call (aws).
Does Querying Data Lake access the network?
SKILL.md names 1 domain. As links in the text: docs.aws.amazon.com. This is read from the text; nothing was executed.
Is Querying 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 Querying Data Lake use?
Querying 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 Querying Data Lake use?
About 1.9k tokens (SKILL.md is roughly 7.7k 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 2.2k tokens, read only when the agent opens those files.
What are the alternatives to Querying Data Lake?
Skills that share tags, products or a category with Querying Data Lake: Chdb SQL (vemetric/vemetric, 394 stars), Modeler (sidequery/sidemantic, 129 stars), Semantic Analyst (sidequery/sidemantic, 129 stars) and Pytorch Clickhouse (pytorch/test-infra, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Querying Data Lake?
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.