Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sun2443/designer-skillsnpx agentmods add skills/sun2443/designer-skills/write-queryWrote 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/sun2443/designer-skills/write-query)<a href="https://agentmods.dev/skills/sun2443/designer-skills/write-query"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/write-query/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/sun2443/designer-skills/write-query"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/write-query.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.01082 |
| Opus 5.5 | $0.00026 | $0.00433 |
| Sonnet 5.5 | $0.00013 | $0.00216 |
| Haiku 4.5 | $0.00006 | $0.00108 |
Grade A, and why
write-query 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 13d 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 write-query — 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/write-query - Write Optimized SQL
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Write a SQL query from a natural language description, optimized for your specific SQL dialect and following best practices.
Usage
/write-query <description of what data you need>
Workflow
1. Understand the Request
Parse the user's description to identify:
- Output columns: What fields should the result include?
- Filters: What conditions limit the data (time ranges, segments, statuses)?
- Aggregations: Are there GROUP BY operations, counts, sums, averages?
- Joins: Does this require combining multiple tables?
- Ordering: How should results be sorted?
- Limits: Is there a top-N or sample requirement?
2. Determine SQL Dialect
If the user's SQL dialect is not already known, ask which they use:
- PostgreSQL (including Aurora, RDS, Supabase, Neon)
- Snowflake
- BigQuery (Google Cloud)
- Redshift (Amazon)
- Databricks SQL
- MySQL (including Aurora MySQL, PlanetScale)
- SQL Server (Microsoft)
- DuckDB
- SQLite
- Other (ask for specifics)
Remember the dialect for future queries in the same session.
3. Discover Schema (If Warehouse Connected)
If a data warehouse MCP server is connected:
- Search for relevant tables based on the user's description
- Inspect column names, types, and relationships
- Check for partitioning or clustering keys that affect performance
- Look for pre-built views or materialized views that might simplify the query
4. Write the Query
Follow these best practices:
Structure:
- Use CTEs (WITH clauses) for readability when queries have multiple logical steps
- One CTE per logical transformation or data source
- Name CTEs descriptively (e.g.,
daily_signups,active_users,revenue_by_product)
Performance:
- Never use
SELECT *in production queries -- specify only needed columns - Filter early (push WHERE clauses as close to the base tables as possible)
- Use partition filters when available (especially date partitions)
- Prefer
EXISTSoverINfor subqueries with large result sets - Use appropriate JOIN types (don't use LEFT JOIN when INNER JOIN is correct)
- Avoid correlated subqueries when a JOIN or window function works
- Be mindful of exploding joins (many-to-many)
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.
- 13d ago First seen · 123 lines · 64 tokens per session scan A 4c7ef791db1f
write-query is a skill published in the GitHub repository sun2443/designer-skills (0 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,082 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to write-query, differing in 0 lines, and is treated as a copy.
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