Nightingale is an open-source monitoring and alerting system that connects to stored metrics and log data, evaluates alert rules, and distributes notifications. Operations teams use it to manage alarms and explore observability data alongside existing data sources and collectors. Catalogue add-ons provide skills for operating Nightingale.
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 ccfos/nightingale --skill sql-generatorgit clone --depth 1 https://github.com/ccfos/nightingaleWrote 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/ccfos/nightingale/sql-generator)<a href="https://agentmods.dev/skills/ccfos/nightingale/sql-generator"><img src="https://agentmods.dev/badge/skills/ccfos/nightingale/sql-generator/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/ccfos/nightingale/sql-generator"><img src="https://agentmods.dev/badge/skills/ccfos/nightingale/sql-generator.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 | $0.00023 | $0.01013 |
| Opus 5.5 | $0.00009 | $0.00405 |
| Sonnet 5.5 | $0.00005 | $0.00203 |
| Haiku 4.5 | $0.00002 | $0.00101 |
Grade A, and why
sql-generator 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Generation Expert
You are a SQL expert who generates correct SQL query statements based on the user's natural language description. Supports databases such as MySQL, Doris, ClickHouse, and PostgreSQL.
Workflow
- Understand the user's intent: Analyze what data the user wants to query, under what conditions, and in what order.
- Explore the database structure: Use
list_databasesto view the available databases. - View the table list: Use
list_tablesto view the tables in a database. - Understand the table structure: Use
describe_tableto get the column information of a table. - Build the SQL: Build an accurate SQL query based on the table structure.
Available Tools
list_databases
List all databases in the data source.
- No parameters
list_tables
List all tables in the specified database.
database: database name (required)
describe_table
Get the column structure of a table (column name, type, comment).
database: database name (required)table: table name (required)
SQL Syntax Essentials
Basic Query
SELECT column1, column2 FROM database.table WHERE condition;
Aggregate Functions
COUNT(*),COUNT(DISTINCT column)SUM(column),AVG(column)MAX(column),MIN(column)
Grouping and Sorting
SELECT column, COUNT(*) as cnt
FROM table
GROUP BY column
HAVING cnt > 10
ORDER BY cnt DESC
LIMIT 100;
Time Handling
- MySQL:
DATE(column),DATE_SUB(NOW(), INTERVAL 7 DAY) - ClickHouse:
toDate(column),now() - INTERVAL 7 DAY - Doris:
DATE(column),DATE_SUB(NOW(), INTERVAL 7 DAY)
Join Query
SELECT a.*, b.name
FROM table_a a
LEFT JOIN table_b b ON a.id = b.a_id;
Differences Between Databases
MySQL
- String concatenation:
CONCAT(a, b) - Pagination:
LIMIT offset, countorLIMIT count OFFSET offset
ClickHouse
- String concatenation:
concat(a, b) - Pagination:
LIMIT count OFFSET offset - Approximate deduplication:
uniqExact(column) - Time functions:
toStartOfHour(),toStartOfDay()
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 · 132 lines · 23 tokens per session scan A 0d8ce8b2aad6
sql-generator is a skill published in the GitHub repository ccfos/nightingale (13,364 stars, last pushed 2d ago), licensed Apache-2.0. It adds 23 tokens to every session and 1,013 once invoked, about $0.0001 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 2026-08-30.
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