Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/tmolavi/mcp-agent-skills-hub/cloud-sql-postgres-datanpx skills add tmolavi/mcp-agent-skills-hub --skill cloud-sql-postgres-datagit clone --depth 1 https://github.com/tmolavi/mcp-agent-skills-hubWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/cloud-sql-postgres-data)<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/cloud-sql-postgres-data"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/cloud-sql-postgres-data.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00039 | $0.02106 |
| Opus 5 | $0.00019 | $0.01053 |
| Sonnet 5 | $0.00008 | $0.00421 |
| Haiku 4.5 | $0.00004 | $0.00211 |
Grade A, and why
cloud-sql-postgres-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to alloydb-postgres-data — 208 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Usage
All scripts can be executed using Node.js. Replace <param_name> and
<param_value> with actual values.
Bash: node <skill_dir>/scripts/<script_name>.js '{"<param_name>": "<param_value>"}'
PowerShell: node <skill_dir>/scripts/<script_name>.js '{\"<param_name>\": \"<param_value>\"}'
Note: The scripts automatically load the environment variables from various .env files. Do not ask the user to set vars unless skill executions fails due to env var absence.
Scripts
execute_sql
Use this tool to execute a single SQL statement.
Parameters
| Name | Type | Description | Required | Default |
|---|---|---|---|---|
| sql | string | The sql to execute. | Yes |
list_indexes
Lists available user indexes in the database, excluding system schemas (pg_catalog, information_schema). For each index, the following properties are returned: schema name, table name, index name, index type (access method), a boolean indicating if it's a unique index, a boolean indicating if it's for a primary key, the index definition, index size in bytes, the number of index scans, the number of index tuples read, the number of table tuples fetched via index scans, and a boolean indicating if the index has been used at least once.
Parameters
| Name | Type | Description | Required | Default |
|---|---|---|---|---|
| schema_name | string | Optional: a text to filter | No | `` |
| : : : results by schema name. The : : : | ||||
| : : : input is used within a LIKE : : : | ||||
| : : : clause. : : : | ||||
| table_name | string | Optional: a text to filter | No | `` |
| : : : results by table name. The : : : | ||||
| : : : input is used within a LIKE : : : | ||||
| : : : clause. : : : | ||||
| index_name | string | Optional: a text to filter | No | `` |
| : : : results by index name. The : : : | ||||
| : : : input is used within a LIKE : : : | ||||
| : : : clause. : : : | ||||
| only_unused | boolean | Optional: If true, only returns | No | false |
| : : : indexes that have never been : : : | ||||
| : : : used. : : : | ||||
| limit | integer | Optional: The maximum number of | No | 50 |
| : : : rows to return. Default is 50 : : : |
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- scripts/execute_sql.js 3.1 KB runs code
- scripts/list_indexes.js 3.1 KB runs code
- scripts/list_schemas.js 3.1 KB runs code
- scripts/list_sequences.js 3.1 KB runs code
- scripts/list_stored_procedure.js 3.1 KB runs code
- scripts/list_tables.js 3.1 KB runs code
- scripts/list_triggers.js 3.1 KB runs code
- scripts/list_views.js 3.1 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 204 lines · 39 tokens per session scan A 6d14fdec1c2e
cloud-sql-postgres-data is a skill published in the GitHub repository tmolavi/mcp-agent-skills-hub (8 stars, last pushed 9d ago), licensed MIT. It adds 39 tokens to every session and 2,106 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to alloydb-postgres-data, differing in 208 lines, and is treated as a copy.
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cloud-sql-basics
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sql-queries
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
postgres-optimization
PostgreSQL optimization including indexes, query plans, partitioning, JSONB operations, and connection pooling.