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/wewpellex21/code-sensei/databasesnpx skills add wewpellex21/code-sensei --skill databasesgit clone --depth 1 https://github.com/wewpellex21/code-senseiWhat 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 | $0.00035 | $0.00703 |
| Opus 5 | $0.00017 | $0.00351 |
| Sonnet 5 | $0.00007 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade A, and why
databases 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 2d 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.
This is a copy
100% identical to databases — 0 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Databases — CodeSensei Teaching Module
What is a Database?
- Analogy: A super-powered spreadsheet that your app can read and write to automatically. Each "table" is like a sheet, each "row" is an entry, each "column" is a field.
- Key insight: Without a database, your app forgets everything when it restarts. Databases make data PERMANENT.
SQL (Structured Query Language)
- Analogy: SQL is the language you use to talk to the database. Like asking a librarian: "Find me all books by this author, sorted by year."
- Essential operations:
SELECT— "Show me data" →SELECT * FROM users(show all users)INSERT— "Add new data" →INSERT INTO users (name, email) VALUES ('Juan', '[email protected]')UPDATE— "Change existing data" →UPDATE users SET name = 'Juan C' WHERE id = 1DELETE— "Remove data" →DELETE FROM users WHERE id = 1WHERE— "But only the ones that match this condition"
- Quiz: "Which SQL word would you use to find all users who signed up today?"
Types of Databases
- PostgreSQL — the reliable all-rounder. Most vibecoded apps use this. Think of it as Excel but 1000x more powerful.
- SQLite — a lightweight database stored in a single file. Good for small projects. Like a notepad vs a full filing system.
- MongoDB — stores data as flexible documents (JSON-like) instead of rigid tables. Like storing folders of papers vs a spreadsheet.
ORMs (Object-Relational Mappers)
- Analogy: An ORM is a translator between your code and the database. Instead of writing SQL directly, you write code in your programming language and the ORM converts it to SQL.
- Prisma (most common in vibecoded projects):
prisma.user.findMany()→ becomesSELECT * FROM usersprisma.user.create({ data: { name: "Juan" } })→ becomesINSERT INTO users...
- Key insight: ORMs make databases easier to work with, but the SQL is still happening underneath.
Schemas & Migrations
- Schema — the blueprint of your database. Defines what tables exist and what columns each has.
- Migration — a change to the blueprint. Like remodeling a room — you write instructions for the change, then apply them.
- Analogy: The schema is your building's floor plan. A migration is the renovation permit that says "add a new room here."
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.
- 2d ago First seen · 45 lines · 35 tokens per session scan A 39fa5dbec91f
databases is a skill published in the GitHub repository wewpellex21/code-sensei (3 stars, last pushed 3d ago), licensed MIT. It adds 35 tokens to every session and 703 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to databases, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
agent-squad-python
Use when building or modifying a Python app that uses the agent-squad Python package — async multi-agent orchestration for Python 3.11+: orchestrator, agents (BedrockLLMAgent, AnthropicAgent, OpenAIAgent, SupervisorAgent, GroundedAgent, ChainAgent, and more), classifier routing (Bedrock, Anthropic, OpenAI), storage…
agent-squad-swift
Use when building or modifying a Swift app that uses the AgentSquad Swift framework — on-device multi-agent orchestration for iOS 16+ / macOS 14+: orchestrator, agents (Agent, GroundedAgent), classifier routing, LLM clients (OpenAI-compatible), tools (native + MCP), tool UIs/widgets, on-device storage, tracing, and…
agent-squad-typescript
Use when building or modifying a Node.js / TypeScript app that uses the agent-squad npm package — multi-agent orchestration: orchestrator, agents (all built-in types + GroundedAgent), classifier routing (Bedrock / Anthropic / OpenAI), storage (in-memory / DynamoDB / SQL), retrievers (Amazon KB / Dakera), and tools…
run-integ
Run integration tests (deploy + destroy) against real AWS. Use when you need to verify cdkd works end-to-end with actual AWS resources.
verify-pr
Comprehensive PR readiness check before merge. Run quality checks, tests, CI, documentation, AWS resource cleanup, and code review.
review-pr
Recommend the right reviewer count for a PR based on size + bias factors. Outputs a concrete plan (inline spot-check / 1 reviewer / 3-axis parallel) plus ready-to-paste Agent dispatch prompts when reviewers are warranted.