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 seb1n/awesome-ai-agent-skills --skill sql-query-generationgit clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skillsWrote 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/seb1n/awesome-ai-agent-skills/sql-query-generation)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/sql-query-generation"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/sql-query-generation/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/seb1n/awesome-ai-agent-skills/sql-query-generation"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/sql-query-generation.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 Prompt Injection · line 164 Subtle instructions detected that may alter agent decision-making or introduce hidden biases.Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
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 | $0.00059 | $0.02184 |
| Opus 5.5 | $0.00024 | $0.00874 |
| Sonnet 5.5 | $0.00012 | $0.00437 |
| Haiku 4.5 | $0.00006 | $0.00218 |
Grade A, and why
sql-query-generation 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- sql-query-generation — 94% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Query Generation
This skill enables an AI agent to translate natural language questions into correct, efficient SQL queries. The agent maps user intent to the appropriate query constructs — joins, aggregations, window functions, CTEs, and subqueries — while respecting the target database schema. It also analyzes query performance with EXPLAIN plans and recommends optimizations such as indexing, predicate pushdown, and query restructuring.
Workflow
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Parse the natural language request. Extract the analytical intent: what metric is being asked for, which entities are involved, what filters apply, and how results should be ordered or grouped. Distinguish between requests for aggregated summaries versus row-level detail.
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Map to the database schema. Identify the relevant tables and columns from the schema. Resolve ambiguous references (e.g., "sales" could mean the
orderstable or therevenuecolumn). Determine the join path between tables using foreign key relationships, avoiding unnecessary joins that inflate result sets. -
Select the appropriate query constructs. Choose between simple aggregation, window functions, CTEs, or subqueries based on complexity. Use CTEs for multi-step calculations to improve readability. Use window functions for running totals, rankings, and comparisons within partitions. Prefer explicit JOINs over implicit comma-separated joins.
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Generate the SQL query. Write syntactically correct SQL with consistent formatting: uppercase keywords, lowercase identifiers, aliased tables, and indented clauses. Include comments for complex logic. Always specify column aliases for computed expressions.
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Validate and optimize. Run EXPLAIN (or EXPLAIN ANALYZE) on the generated query to inspect the execution plan. Look for full table scans, hash joins on large tables, and sort operations on unindexed columns. Recommend indexes or query rewrites when the estimated cost is high.
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Return results with explanation. Present the query alongside a plain-language explanation of what it does, the expected output format, and any assumptions made about the schema or data.
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 · 178 lines · 59 tokens per session scan A 38d8f55f4490
sql-query-generation is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (231 stars, last pushed 2mo ago), licensed MIT. It adds 59 tokens to every session and 2,184 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 2026-08-30.
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