Claude Code Guide is a reference collection for configuring and using Claude Code, Anthropic’s command-line coding agent. Developers use it to learn commands, skills, agents, MCP, automation, security, integrations, and troubleshooting. Its catalogue add-ons provide many of the documented skills and agents.
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 zebbern/claude-code-guide --skill sql-insightgit clone --depth 1 https://github.com/zebbern/claude-code-guideWrote 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/zebbern/claude-code-guide/sql-insight)<a href="https://agentmods.dev/skills/zebbern/claude-code-guide/sql-insight"><img src="https://agentmods.dev/badge/skills/zebbern/claude-code-guide/sql-insight/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/zebbern/claude-code-guide/sql-insight"><img src="https://agentmods.dev/badge/skills/zebbern/claude-code-guide/sql-insight.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00064 | $0.01966 |
| Opus 5.5 | $0.00026 | $0.00786 |
| Sonnet 5.5 | $0.00013 | $0.00393 |
| Haiku 4.5 | $0.00006 | $0.00197 |
Grade A, and why
sql-insight 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sql-insight
SQL query assistant — natural language to SQL translation, query optimization analysis, and EXPLAIN plan interpretation.
Capabilities
| Feature | Description |
|---|---|
| Schema Extraction | Extracts database table structure (columns, types, indexes, foreign keys, sample data) to provide context for NL→SQL |
| Natural Language → SQL | Translates natural language descriptions into SQL queries using schema context |
| Query Optimization Analysis | Detects SQL anti-patterns based on 13 rules and provides optimization suggestions |
| EXPLAIN Interpretation | Runs EXPLAIN and interprets the query plan, identifying full table scans, missing indexes, and more |
Workflow
Natural Language → SQL
- Use the
schemacommand to extract the database table structure - Use the schema as context to translate the user's natural language request into SQL
- Use the
optimizecommand to check if the generated SQL can be improved - Use the
explaincommand to verify the query execution plan
# Step 1: Extract schema (compact mode, suitable for LLM context)
python3 scripts/sql_query_helper.py --db-path data.db schema --compact
# Step 2: Analyze SQL optimization suggestions
python3 scripts/sql_query_helper.py optimize "SELECT * FROM orders WHERE user_id = 100"
# Step 3: View EXPLAIN execution plan
python3 scripts/sql_query_helper.py --db-path data.db explain "SELECT * FROM orders WHERE user_id = 100"
Quick Start
Schema Extraction
# Extract full schema (JSON format, with sample data)
python3 scripts/sql_query_helper.py --db-path data.db schema
# Compact mode (plain text, suitable for embedding in prompts)
python3 scripts/sql_query_helper.py --db-path data.db schema --compact
# Skip data sampling
python3 scripts/sql_query_helper.py --db-path data.db schema --sample-rows 0
# PostgreSQL
python3 scripts/sql_query_helper.py --db-type postgres --dsn "host=localhost dbname=mydb user=reader" schema --compact
What ships with it
2 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 · 212 lines · 64 tokens per session scan A d3350c171fb6
sql-insight is a skill published in the GitHub repository zebbern/claude-code-guide (4,669 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 1,966 once invoked, about $0.0003 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
database-expert
Advanced database design and administration for PostgreSQL, MongoDB, and Redis. Use when designing schemas, optimizing queries, managing database performance, or implementing data patterns.
agent-database-expert
Specialist subagent: Expert database architect and engineer for PostgreSQL, MongoDB, Redis, and all major databases. Use when designing schemas, optimizing queries, planning migrations, implementing caching, or troubleshooting database performance.
dev-supabase
Backend development with Supabase. Trigger when the user wants to configure auth, the database, or Supabase storage.
ops-database
Database schema design. Trigger when the user wants to create tables, migrations, or optimize queries.
postgres-pro
Use when optimizing PostgreSQL queries, configuring replication, or implementing advanced database features. Invoke for EXPLAIN analysis, JSONB operations, extension usage, VACUUM tuning, performance monitoring.
cli-audit-data
Audit PostgreSQL database safety in Rust/SQLx applications. Use for schemas, migrations, constraints, indexes, transactions, repositories, state transitions, idempotency, concurrency, queues, multi-tenancy, soft deletion, ledgers, auditability, repair, database incidents, or whenever SQLx and PostgreSQL changes could…