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/personamanagmentlayer/pcl/sql-expertnpx skills add personamanagmentlayer/pcl --skill sql-expertgit clone --depth 1 https://github.com/personamanagmentlayer/pclWrote 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/personamanagmentlayer/pcl/sql-expert)<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/sql-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/sql-expert.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 | $0.00026 | $0.05250 |
| Opus 5 | $0.00013 | $0.02625 |
| Sonnet 5 | $0.00005 | $0.01050 |
| Haiku 4.5 | $0.00003 | $0.00525 |
Grade A, and why
sql-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 858 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Expert
You are an expert in SQL databases with deep knowledge of database design, query optimization, indexing strategies, and administration. You write efficient, maintainable SQL queries and design robust database schemas.
Core Expertise
Database Design
Entity-Relationship Design:
-- Users table
CREATE TABLE users (
id SERIAL PRIMARY KEY,
username VARCHAR(50) UNIQUE NOT NULL,
email VARCHAR(255) UNIQUE NOT NULL,
password_hash VARCHAR(255) NOT NULL,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
is_active BOOLEAN DEFAULT true,
CONSTRAINT check_email CHECK (email ~* '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z]{2,}$')
);
-- Posts table
CREATE TABLE posts (
id SERIAL PRIMARY KEY,
user_id INTEGER NOT NULL REFERENCES users(id) ON DELETE CASCADE,
title VARCHAR(200) NOT NULL,
content TEXT NOT NULL,
status VARCHAR(20) DEFAULT 'draft' CHECK (status IN ('draft', 'published', 'archived')),
published_at TIMESTAMP,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
INDEX idx_user_id (user_id),
INDEX idx_status (status),
INDEX idx_published_at (published_at)
);
-- Comments table (one-to-many with posts)
CREATE TABLE comments (
id SERIAL PRIMARY KEY,
post_id INTEGER NOT NULL REFERENCES posts(id) ON DELETE CASCADE,
user_id INTEGER NOT NULL REFERENCES users(id) ON DELETE CASCADE,
content TEXT NOT NULL,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
INDEX idx_post_id (post_id),
INDEX idx_user_id (user_id)
);
-- Tags table (many-to-many with posts)
CREATE TABLE tags (
id SERIAL PRIMARY KEY,
name VARCHAR(50) UNIQUE NOT NULL,
slug VARCHAR(50) UNIQUE NOT NULL,
INDEX idx_slug (slug)
);
CREATE TABLE post_tags (
post_id INTEGER NOT NULL REFERENCES posts(id) ON DELETE CASCADE,
tag_id INTEGER NOT NULL REFERENCES tags(id) ON DELETE CASCADE,
PRIMARY KEY (post_id, tag_id),
INDEX idx_tag_id (tag_id)
);
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.
- 4d ago First seen · 858 lines · 26 tokens per session scan A 506bdf099a7f
sql-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (41 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 5,250 once invoked, about $0.0001 per session on Opus 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 2026-08-30.
Other skills, from other repositories
Database Schema Reviewer
Reviews database schemas for normalization issues, missing indexes, naming inconsistencies, and scalability risks.
graphjin-eval
Create, extend, run, baseline, and diagnose GraphJin agent evaluations through the graphjin eval CLI.
graphjin-env
Use when setting up a training or evaluation loop against a GraphJin agent environment — running the container, reading /health, driving episodes hosted or step-by-step or with your own agent over MCP, splitting train from eval, exporting trajectories, and deciding whether two rewards can be compared.
graphjin-env-workflows
Use when running GraphJin's own environment and evaluation workflows in this repository — generating a suite, cloning or minting a world, authoring tasks, serving graded episodes, sampling, exporting trajectories, publishing a benchmark run — or when changing code those workflows depend on.
add-graphjin-database
Use when adding a new GraphJin database, warehouse, or CQL/NoSQL backend; building a simulator because no live service is available; wiring a dialect, discovery, tests, scripts, README/CONFIG/FEATURES, or website database support surfaces.
database-sql
Design database schemas, write efficient SQL queries, create migrations, and optimize database performance. Use when working with databases, writing queries, or designing data models.