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 andregusman-raiz/a-gusman-claude --skill data-dictionarygit clone --depth 1 https://github.com/andregusman-raiz/a-gusman-claudeWrote 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/andregusman-raiz/a-gusman-claude/data-dictionary)<a href="https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/data-dictionary"><img src="https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/data-dictionary/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/andregusman-raiz/a-gusman-claude/data-dictionary"><img src="https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/data-dictionary.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 4 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00054 | $0.01823 |
| Opus 5.5 | $0.00022 | $0.00729 |
| Sonnet 5.5 | $0.00011 | $0.00365 |
| Haiku 4.5 | $0.00005 | $0.00182 |
Grade A, and why
data-dictionary 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Dictionary Skill
Gerar dicionarios de dados completos a partir de schemas existentes.
Output Format
Cada tabela/entidade gera uma secao markdown:
### users
Usuarios do sistema.
| Column | Type | Nullable | Default | Description | FK |
|--------|------|----------|---------|-------------|----|
| id | uuid | No | gen_random_uuid() | Primary key | - |
| name | varchar(100) | No | - | Nome completo | - |
| email | varchar(255) | No | - | Email unico | - |
| role_id | uuid | Yes | null | Perfil do usuario | roles.id |
| created_at | timestamptz | No | now() | Data de criacao | - |
| updated_at | timestamptz | No | now() | Data de atualizacao | - |
**Indexes**: `users_email_key` (UNIQUE on email), `users_role_id_idx` (on role_id)
**RLS**: Enabled — users can only read their own row
From Drizzle Schema
// Parse pgTable definitions
import { pgTable, uuid, varchar, timestamp, boolean } from 'drizzle-orm/pg-core';
export const users = pgTable('users', {
id: uuid('id').defaultRandom().primaryKey(),
name: varchar('name', { length: 100 }).notNull(),
email: varchar('email', { length: 255 }).notNull().unique(),
roleId: uuid('role_id').references(() => roles.id),
active: boolean('active').default(true).notNull(),
createdAt: timestamp('created_at', { withTimezone: true }).defaultNow().notNull(),
});
Extrair:
- Nome da tabela: primeiro arg de
pgTable() - Colunas: cada propriedade do objeto
- Tipo:
uuid(),varchar(),timestamp(), etc. - Nullable: presenca de
.notNull()(default e nullable) - Default:
.default(),.defaultRandom(),.defaultNow() - FK:
.references(() => table.column) - Constraints:
.unique(),.primaryKey()
From Prisma Schema
model User {
id String @id @default(uuid())
name String
email String @unique
role Role? @relation(fields: [roleId], references: [id])
roleId String?
posts Post[]
active Boolean @default(true)
createdAt DateTime @default(now())
updatedAt DateTime @updatedAt
@@index([roleId])
@@map("users")
}
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 · 273 lines · 54 tokens per session scan A 8565271ad8f5
data-dictionary is a skill published in the GitHub repository andregusman-raiz/a-gusman-claude (18 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 1,823 once invoked, about $0.0002 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.
Other skills, from other repositories
lark-base
A guide for managing Lark Base, Feishu's spreadsheet-like database and workspace tool. It covers tables, fields, records, views, formulas, forms, dashboards, applications, workflows, and permissions.
smart-data-collection
A workflow for extracting structured information from images and documents such as PDFs, Word files, and spreadsheets, then storing it in a database.
entity-model
Creates entity model documents with Mermaid.js ER diagrams and attribute tables defining entities, relationships, data types, and validation rules. Use when the user asks to "create an entity model", "design a data model", "draw an ERD", "define database schema", "model entities", or mentions entity-relationship…
siyuan-mcp-import-migration
MCP staged playbook for Markdown and external database migration with explicit targets, mappings, bounded writes, and layered readback.
paper-interpretation
A Chinese-language workflow for explaining an academic paper from a PDF file or URL in accessible Markdown, with illustrations and critical assessment.
siyuan-mcp-data-files
Database and file workflow for SiYuan MCP. Use for attribute views, columns, rows, cells, assets, uploads, OCR, templates, Markdown export, document extraction, and resource export.