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 skills add tmolavi/mcp-agent-skills-hub --skill cloud-sql-postgres-vectorassistgit 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-vectorassist)<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/cloud-sql-postgres-vectorassist"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/cloud-sql-postgres-vectorassist/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/cloud-sql-postgres-vectorassist"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/cloud-sql-postgres-vectorassist.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00029 | $0.05123 |
| Opus 5.5 | $0.00012 | $0.02049 |
| Sonnet 5.5 | $0.00006 | $0.01025 |
| Haiku 4.5 | $0.00003 | $0.00512 |
Grade A, and why
cloud-sql-postgres-vectorassist 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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 413 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
apply_spec
This tool automatically executes all the SQL recommendations associated with a specific vector specification (spec_id) or table. It runs the necessary commands in the correct sequence to provision the workload, marking each step as applied once successful. Use this tool when the user has reviewed the generated recommendations from a defined (or modified) spec and is ready to apply the changes directly to their database instance to finalize the vector search setup. This tool can be used as a follow-up action after invoking the 'define_spec' or 'modify_spec' tool.
Parameters
| Name | Type | Description | Required | Default |
|---|---|---|---|---|
| spec_id | string | The unique ID of | No | |
| : : : the vector : : : | ||||
| : : : specification to : : : | ||||
| : : : apply. : : : | ||||
| table_name | string | The name of the | No | |
| : : : table to apply the : : : | ||||
| : : : vector : : : | ||||
| : : : specification to : : : | ||||
| : : : (in case of a : : : | ||||
| : : : single spec : : : | ||||
| : : : defined on the : : : | ||||
| : : : table). : : : | ||||
| column_name | string | The | No | |
| : : : text_column_name : : : | ||||
| : : : or : : : | ||||
| : : : vector_column_name : : : | ||||
| : : : of the spec to : : : | ||||
| : : : identify the exact : : : | ||||
| : : : spec in case there : : : | ||||
| : : : are multiple specs : : : | ||||
| : : : defined on a : : : | ||||
| : : : table. : : : | ||||
| schema_name | string | The schema name | No | |
| : : : for the table. : : : |
What ships with it
5 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.
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.
- 1mo ago First seen · 413 lines · 29 tokens per session scan A fa0d7e8ea1b9
cloud-sql-postgres-vectorassist is a skill published in the GitHub repository tmolavi/mcp-agent-skills-hub (8 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 5,123 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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