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/dean0x/devflow/databasenpx skills add dean0x/devflow --skill databasegit clone --depth 1 https://github.com/dean0x/devflowWhat 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 | $0.00020 | $0.01069 |
| Opus 5 | $0.00010 | $0.00535 |
| Sonnet 5 | $0.00004 | $0.00214 |
| Haiku 4.5 | $0.00002 | $0.00107 |
Grade A, and why
database 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.
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Patterns
Domain expertise for database design and optimization. Use alongside devflow:review-methodology for complete database reviews.
Iron Law
EVERY QUERY MUST HAVE AN EXECUTION PLAN
Never deploy a query without understanding its execution plan. Every WHERE clause needs an index analysis. Every JOIN needs cardinality consideration. "It works in dev" is not validation. Production data volumes will expose every missing index and inefficient join.
Database Categories
1. Schema Design Issues
| Issue | Problem | Solution |
|---|---|---|
| Missing Foreign Keys | No referential integrity, orphaned records | Add FK with ON DELETE action |
| Denormalization | Unnecessary duplication, update anomalies | Normalize unless performance requires |
| Poor Data Types | VARCHAR for everything, lost precision | Use appropriate types (DECIMAL, BOOLEAN, TIMESTAMP) |
| Missing Constraints | No data validation at DB level | Add NOT NULL, CHECK, UNIQUE constraints |
Example - Missing Constraints:
-- VIOLATION
CREATE TABLE products (id SERIAL, name VARCHAR(100), price DECIMAL);
-- CORRECT
CREATE TABLE products (
id SERIAL PRIMARY KEY,
name VARCHAR(100) NOT NULL CHECK (LENGTH(TRIM(name)) > 0),
price DECIMAL(10, 2) NOT NULL CHECK (price >= 0)
);
2. Query Optimization Issues
| Issue | Problem | Solution |
|---|---|---|
| N+1 Queries | Query per iteration, O(n) round trips | JOIN or batch with IN/ANY |
| Missing Indexes | Full table scans on large tables | Add indexes for WHERE/JOIN columns |
| Full Table Scans | Functions prevent index use | Functional indexes or query rewrite |
| Inefficient JOINs | Joining before filtering | Filter early, select specific columns |
Example - N+1 Query:
// VIOLATION: 101 queries for 100 users
for (const user of users) {
user.orders = await db.query('SELECT * FROM orders WHERE user_id = ?', [user.id]);
}
// CORRECT: 2 queries total
const orders = await db.query('SELECT * FROM orders WHERE user_id = ANY($1)', [userIds]);
What ships with it
3 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.
- yesterday First seen · 135 lines · 20 tokens per session scan A f87d12d7aa63
database is a skill published in the GitHub repository dean0x/devflow (19 stars, last pushed 2d ago), licensed MIT. It adds 20 tokens to every session and 1,069 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
data-engineering
Skill "data-engineering" from fengshao1227/ccg-workflow, covering 数据工程域 · data engineering, 域概览, 数据管道编排, 框架对比 and airflow 核心模式.
verify-change
变更校验关卡。分析代码变更，检测文档同步状态，评估变更影响范围。当用户提到变更检查、文档同步、代码审查、提交前检查、diff分析时使用。在设计级变更、重构完成时自动触发。.
verify-security
安全校验关卡。自动扫描代码安全漏洞，检测危险模式，确保安全决策有文档记录。当用户提到安全扫描、漏洞检测、安全审计、代码安全、OWASP、注入检测、敏感信息泄露时使用。在新建模块、安全相关变更、攻防任务、重构完成时自动触发。.
liquid-glass
Apple Liquid Glass design system. Use when building UI with translucent, depth-aware glass morphism following Apple's design language. Provides CSS tokens, component patterns, dark/light mode, and animation specs.
gen-docs
文档生成器。自动分析模块结构，生成 README.md 和 DESIGN.md 骨架。当用户提到生成文档、创建README、创建DESIGN、文档骨架、文档模板时使用。在新建模块开始时自动触发。.
verify-module
模块完整性校验关卡。扫描目录结构、检测缺失文档、验证代码与文档同步。当用户提到模块校验、文档检查、结构完整性、README检查、DESIGN检查时使用。在新建模块完成时自动触发。.