SQL Query Explainer
mohitagw15856/pm-claude-skills
Explains, optimises, writes, and documents SQL queries. An agent skill from mohitagw15856/pm-claude-skills.
Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery.
Install Connecting To Data Source
$ npx skills add aws/agent-toolkit-for-aws --skill connecting-to-data-source -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aws/agent-toolkit-for-aws connecting-to-data-source --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/connecting-to-data-source .claude/skills/connecting-to-data-source && 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 "connecting-to-data-source" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/connecting-to-data-source into .claude/skills/connecting-to-data-source/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "connecting-to-data-source", 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/connecting-to-data-sourceType 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 connecting-to-data-source -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aws/agent-toolkit-for-aws connecting-to-data-source --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/connecting-to-data-source .agents/skills/connecting-to-data-source && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "connecting-to-data-source" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/connecting-to-data-source into .agents/skills/connecting-to-data-source/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "connecting-to-data-source", 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 connecting-to-data-source -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aws/agent-toolkit-for-aws connecting-to-data-source --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/connecting-to-data-source .cursor/skills/connecting-to-data-source && 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 "connecting-to-data-source" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/connecting-to-data-source into .cursor/skills/connecting-to-data-source/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "connecting-to-data-source", 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/connecting-to-data-source--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 connecting-to-data-source -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aws/agent-toolkit-for-aws connecting-to-data-source --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/connecting-to-data-source .gemini/skills/connecting-to-data-source && 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 "connecting-to-data-source" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/connecting-to-data-source into .gemini/skills/connecting-to-data-source/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "connecting-to-data-source", 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 connecting-to-data-sourceInstalls 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 connecting-to-data-source -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/connecting-to-data-source .github/skills/connecting-to-data-source && 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 "connecting-to-data-source" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/connecting-to-data-source into .github/skills/connecting-to-data-source/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "connecting-to-data-source", 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 connecting-to-data-source -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 connecting-to-data-source --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/connecting-to-data-source .opencode/skills/connecting-to-data-source && 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 "connecting-to-data-source" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/connecting-to-data-source into .opencode/skills/connecting-to-data-source/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "connecting-to-data-source", 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
connecting-to-data-source- GitHub stars
- 2.8k
- Used in
- 1 other repo
- Token cost
- ~2.2k tokens
- SKILL.md length
- 959 words
- Files
- 8 (incl. references)
- Skills in repo
- 138
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery.
- Works in 8 steps: Verify Dependencies and Context → Classify the Source → Gather Connection Hints from the User → …
- : connect to database
- SKILL.md covers Philosophy, Common Tasks, Workflow and Argument Routing, plus 3 more sections
- Calls aws
What it does
Connecting To Data Source is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery. Gathers connection hints from user, discovers existing connections and RDS/Redshift candidates, registers credentials in Secrets Manager or IAM DB auth, configures VPC, and tests. Triggers on: connect to database, set up Glue connection, register data source, connect to Snowflake/BigQuery/RDS, connection timeout, test connection, troubleshoot connection. Do NOT use for…
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `references/bigquery-setup.md`, `references/credential-security.md` and `references/discovery.md`).
It sits in Databases, covering Data warehousing. It works with Amazon Web Services, Snowflake, Google BigQuery and Microsoft SQL Server. 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
- : connect to database
- Set up Glue connection
- Register data source
- Connect to Snowflake/BigQuery/RDS
Example prompts
- “/connecting-to-data-source”
Workflow steps
8 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
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
Connecting To Data Source loads about 2.2k tokens when it runs, and up to ~9.4k if it reads all its reference files. Until then it costs about 193 tokens; SKILL.md has 959 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). 959 words, ~2,234 tokens.
Download SKILL.mdSave it as
.claude/skills/connecting-to-data-source/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.- name
- connecting-to-data-source
- description
- Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery. Gathers connection hints from user, discovers existing connections and RDS/Redshift candidates, registers credentials in Secrets Manager or IAM DB auth, configures VPC, and tests. Triggers on: connect to database, set up Glue connection, register data source, connect to Snowflake/BigQuery/RDS, connection timeout, test connection, troubleshoot connection. Do NOT use for moving data (use ingesting-into-data-lake), creating tables (use creating-data-lake-table), queries (use querying-data-lake), catalog exploration (use exploring-data-catalog), or SaaS (Salesforce, ServiceNow, SAP, MongoDB, Kafka).
- metadata.version
- 1
- metadata.argument-hint
- '[source-type|connection-name|hostname]'
Connect to Data Source
Register an external data source with AWS Glue so downstream skills (ingesting-into-data-lake) can move data from it. A Glue connection stores the network config, driver, and credential reference for one source. Create once per source, reuse across jobs.
Philosophy
A connection is a named pipe, not a pipeline. This skill produces a tested, reusable Glue connection. It does not move data.
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
2. Classify the Source
Ask the user which source type they want to connect to, or infer from hints:
| User says... | Source type | Connection type | Reference |
|---|---|---|---|
| "Oracle", "SQL Server", "Postgres", "MySQL", "RDS <engine>" | JDBC database | JDBC | jdbc-setup.md |
| "Redshift", "my cluster", "my data warehouse on AWS" | Redshift | JDBC | jdbc-setup.md (Redshift section) |
| "Snowflake" | Snowflake | SNOWFLAKE | snowflake-setup.md |
| "BigQuery", "Google analytics warehouse" | BigQuery | BIGQUERY | bigquery-setup.md |
If the user names DynamoDB or a local file, stop and tell them: DynamoDB is read directly by Glue without a connection, and local files belong in the ingesting-into-data-lake skill's local-upload workflow.
3. Gather Connection Hints from the User
You MUST ask for hints the user can provide -- do not guess.
For all sources:
- Desired connection name (lowercase, hyphens:
oracle-prod-sales,snowflake-analytics) - Existing Secrets Manager secret, or create one
- Is source reachable from a Glue VPC (same, peered, VPN, Direct Connect)
JDBC: hostname/endpoint, port, database, whether RDS/Aurora/self-managed, IAM DB auth enabled (Aurora/RDS MySQL/Postgres), SSL required.
Snowflake: account identifier, warehouse, role, default database, auth (password, key-pair, OAuth).
BigQuery: GCP project ID, location, whether service account JSON is provisioned.
4. Discover Existing Connections and Candidate Sources
Check what exists before creating.
Existing Glue connections:
aws glue get-connections --filter ConnectionType=<TYPE> --region <REGION>If a suitable one exists, confirm and skip to Step 7.
Candidate sources in account (JDBC/Redshift only):
- RDS:
aws rds describe-db-instances - Aurora:
aws rds describe-db-clusters - Redshift:
aws redshift describe-clusters
Present candidates to user; let them pick. See discovery.md.
5. Register Credentials
You MUST encourage AWS Secrets Manager over plaintext passwords. You SHOULD prefer IAM database authentication where supported (Aurora/RDS MySQL and PostgreSQL, Redshift). See credential-security.md.
- You MUST confirm with user before creating a new Secrets Manager secret
- You MUST NOT write plaintext credentials into chat or logs
- For IAM DB auth, no secret is needed
6. Create the Glue Connection
Follow the source-specific reference for connection properties:
aws glue create-connection --connection-input '<JSON>' --region <REGION>Private sources require PhysicalConnectionRequirements (SubnetId, SecurityGroupIdList, AvailabilityZone). See network-setup.md.
7. Test the Connection
You MUST test before handing off. Testing is two-phase: a quick API check, then an engine-level verification.
Phase A: Glue TestConnection (network and credential sanity check)
aws glue test-connection --connection-name <NAME> --region <REGION>This validates that Glue can reach the source and authenticate. It does NOT prove the connection works end-to-end with the query engine the user plans to use.
Show full SKILL.md (454 more words)Show less
Phase B: Engine-level verification
After TestConnection passes, verify the connection works with the user's intended engine by running a minimal query through it:
- Glue ETL (default): Run a smoke-test Glue job that reads one row via the connection. See troubleshooting.md.
- Athena: If the user plans to query via Athena with a federated connector, run a
SELECT 1through the Athena connection to confirm the Lambda-based connector can reach the source. - Glue Crawler: If the user plans to crawl the source, run a test crawl on a single table.
Phase B catches issues that TestConnection misses: driver compatibility at job runtime, catalog configuration, Spark-level serialization, and engine-specific auth flows (e.g., Snowflake SNOWFLAKE type works in ETL but not via JDBC crawlers).
On success in both phases, tell user the connection name is ready for ingesting-into-data-lake. On failure in either phase, Step 8.
8. Troubleshoot (only if test failed)
Diagnose in order: network, credentials, driver. See troubleshooting.md.
Constraints:
- You MUST check VPC routing, security groups, and S3 VPC endpoint before blaming credentials
- You MUST verify Glue role can read the Secrets Manager secret
- You MUST NOT rotate credentials without user confirmation
Argument Routing
- No args: Walk through Steps 1-7 interactively
- Source type keyword (e.g.,
snowflake,oracle): Skip to Step 2 with the type prefilled - Existing connection name: Skip to Step 7 (test) then Step 8 if failing
- Hostname or RDS endpoint: Skip to Step 4 with the candidate prefilled
Gotchas
- Glue's
SNOWFLAKEconnection type is distinct fromJDBCconfigured for Snowflake. You MUST useSNOWFLAKEfor Spark ETL jobs; do not use JDBC. - Connection names are immutable. Choose carefully.
PhysicalConnectionRequirements.AvailabilityZoneMUST match the subnet's AZ or the connection fails at job runtime, not creation time.- IAM database authentication tokens expire in 15 minutes. The Glue job generates a fresh token on each connection; do not cache.
- An S3 VPC gateway endpoint MUST exist in the VPC used by private-source connections. Without it, Glue jobs cannot read their scripts or write results to S3.
Troubleshooting
| Error | Likely cause | Fix |
|---|---|---|
Connect timed out | VPC routing, SG rule, or NAT gateway missing | See troubleshooting.md |
Access denied for user / ORA-01017 | Credentials wrong, Secrets Manager access missing, or IAM DB auth misconfigured | See troubleshooting.md |
No suitable driver found | Custom driver JAR not set or wrong class name | See troubleshooting.md |
SSL handshake failed | JDBC_ENFORCE_SSL mismatch between Glue and source | See troubleshooting.md |
UnableToFindVpcEndpoint | S3 VPC endpoint missing | Create S3 gateway endpoint in the connection's VPC |
References
- jdbc-setup.md -- Oracle, SQL Server, PostgreSQL, MySQL, RDS, Redshift
- snowflake-setup.md -- Glue
SNOWFLAKEtype, auth modes - bigquery-setup.md -- Glue
BIGQUERYtype, GCP service accounts - discovery.md -- Finding existing connections and candidate sources
- credential-security.md -- Secrets Manager and IAM DB auth
- network-setup.md -- VPC, subnets, security groups, endpoints
- troubleshooting.md -- Connection errors and diagnostic flow
© 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 7 other files (references) in plugins/aws-data-analytics/skills/connecting-to-data-source of aws/agent-toolkit-for-aws.
- SKILL.md
- references/bigquery-setup.md
- references/credential-security.md
- references/discovery.md
- references/jdbc-setup.md
- references/network-setup.md
- references/snowflake-setup.md
- references/troubleshooting.md
Open the folder on GitHubat commit 188af2f
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
Connecting To Data Source 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 |
|---|---|---|---|---|---|---|
| Connecting To Data Source this skillaws/agent-toolkit-for-aws | 2.8k | 1 repos | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| SQL Query Explainermohitagw15856/pm-claude-skills | 1.4k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Mfs Findzilliztech/mfs | 151 | — | ~4k | Automated safety check: Pass | Apache-2.0 | |
| Mfs Ingestzilliztech/mfs | 151 | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| Altimate Data Warehouse DelegateAltimateAI/data-engineering-skills | 128 | — | ~1.4k | Automated safety check: Pass | MIT | |
| SQL Queriesw95/awesome-claude-corporate-skills | 237 | 3 repos | ~2.8k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Connecting To Data Source
What does Connecting To Data Source do?
Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery. Connecting To Data Source is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery.
When should I use Connecting To Data Source?
Connecting To Data Source fits situations like: : connect to database; set up Glue connection; register data source; connect to Snowflake/BigQuery/RDS.
How do I install Connecting To Data Source in Claude Code?
Run `npx skills add aws/agent-toolkit-for-aws --skill connecting-to-data-source -a claude-code`. Or copy the skill folder (plugins/aws-data-analytics/skills/connecting-to-data-source in aws/agent-toolkit-for-aws) into .claude/skills/connecting-to-data-source in your project. Claude Code loads it when a task matches its description.
How do I install Connecting To Data Source in Codex?
Run `npx skills add aws/agent-toolkit-for-aws --skill connecting-to-data-source -a codex`. Or copy the skill folder (plugins/aws-data-analytics/skills/connecting-to-data-source in aws/agent-toolkit-for-aws) into .agents/skills/connecting-to-data-source in your project. Codex loads it when a task matches its description.
Can I use Connecting To Data Source 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 connecting-to-data-source -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/connecting-to-data-source, .gemini/skills/connecting-to-data-source, .github/skills/connecting-to-data-source and .opencode/skills/connecting-to-data-source in your project.
What does Connecting To Data Source need to run?
Going by SKILL.md and its folder, Connecting To Data Source needs the command-line tools its instructions call (aws).
Does Connecting To Data Source 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 Connecting To Data Source 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 Connecting To Data Source use?
Connecting To Data Source 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 Connecting To Data Source use?
About 2.2k tokens (SKILL.md is roughly 8.9k 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 7.2k tokens, read only when the agent opens those files.
What are the alternatives to Connecting To Data Source?
Skills that share tags, products or a category with Connecting To Data Source: SQL Query Explainer (mohitagw15856/pm-claude-skills, 1.4k stars), Mfs Find (zilliztech/mfs, 151 stars), Mfs Ingest (zilliztech/mfs, 151 stars) and Altimate Data Warehouse Delegate (AltimateAI/data-engineering-skills, 128 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Connecting To Data Source?
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.