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 cnfeat/top-pm-skills --skill sql-query-explainergit clone --depth 1 https://github.com/cnfeat/top-pm-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/cnfeat/top-pm-skills/sql-query-explainer)<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/sql-query-explainer"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/sql-query-explainer.svg" alt="Measured on agentmods" 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 | $0.00100 | $0.01058 |
| Opus 5 | $0.00050 | $0.00529 |
| Sonnet 5 | $0.00020 | $0.00212 |
| Haiku 4.5 | $0.00010 | $0.00106 |
Grade A, and why
sql-query-explainer 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 4d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Query Explainer Skill
This skill explains SQL queries in plain language, identifies optimisation opportunities, and helps communicate data logic to non-technical stakeholders. It also writes and documents new queries from natural language descriptions.
Modes
Detect which mode the user needs based on their request:
- Explain — Translate existing SQL into plain English
- Optimise — Review SQL for performance issues and suggest improvements
- Write — Generate SQL from a natural language description
- Document — Produce a data dictionary or query documentation
Mode 1: Explain
When given a SQL query, produce:
Plain English Summary
[1–3 sentences. What does this query do? What data does it return? Write as if explaining to a business analyst, not a developer.]
Step-by-Step Walkthrough
Break the query into logical sections. For each section:
- Quote the SQL clause
- Explain what it does in plain English
- Flag any complexity (e.g. window functions, subqueries, CTEs)
What the Result Looks Like
[Describe the shape of the output: "Returns one row per user, with columns for X, Y, Z. Ordered by [field] descending."]
Potential Issues to Flag
- [Gotchas, edge cases, or implicit assumptions in this query]
- [e.g. "This will include NULLs in the user_id column if the LEFT JOIN finds no match"]
Mode 2: Optimise
When asked to optimise a query, produce:
Performance Assessment
Rate overall: 🟢 Well-optimised / 🟡 Some improvements possible / 🔴 Significant issues
Issues Found
For each issue:
Issue [N]: [Short name, e.g. "Missing index on join column"]
- What it is: [Plain explanation]
- Why it matters: [Performance impact — e.g. "Full table scan on a 10M row table"]
- Fix:
-- Before
[original snippet]
-- After
[improved snippet]
- Expected improvement: [Estimate if possible]
Optimisation Checklist
- SELECT * used? (Replace with specific columns)
- Implicit type conversions on JOIN/WHERE columns?
- Missing indexes on JOIN or WHERE columns?
- N+1 patterns (queries inside loops)?
- DISTINCT used where GROUP BY would be faster?
- Window functions used where a subquery would be clearer/faster?
- CTEs re-used or materialised unnecessarily?
- Large IN() lists that could use a JOIN instead?
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.
- 4d ago First seen · 144 lines · 100 tokens per session scan A 374ed34aacb5
sql-query-explainer is a skill published in the GitHub repository cnfeat/top-pm-skills (48 stars, last pushed 2mo ago), licensed MIT. It adds 100 tokens to every session and 1,058 once invoked, about $0.0005 per session on Opus 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.
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