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 agentmods add skills/vanterx/mssql-performance-skills/sqlquerystore-reviewnpx skills add vanterx/mssql-performance-skills --skill sqlquerystore-reviewgit clone --depth 1 https://github.com/vanterx/mssql-performance-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/vanterx/mssql-performance-skills/sqlquerystore-review)<a href="https://agentmods.dev/skills/vanterx/mssql-performance-skills/sqlquerystore-review"><img src="https://agentmods.dev/badge/skills/vanterx/mssql-performance-skills/sqlquerystore-review.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 | $0.00080 | $0.09097 |
| Opus 5 | $0.00040 | $0.04548 |
| Sonnet 5 | $0.00016 | $0.01819 |
| Haiku 4.5 | $0.00008 | $0.00910 |
Grade A, and why
sqlquerystore-review 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 6d 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 — 498 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Server Query Store Review Skill
Purpose
Analyze SQL Server Query Store (sys.query_store_* DMV) output to identify the most impactful queries in a workload, detect performance regressions, surface plan instability, flag resource hotspots, audit Query Store configuration health, and detect SQL 2019/2022 IQP/PSP/DOP/CE feedback signals. Applies 32 checks across six categories: regressed queries (Q1–Q6), plan stability (Q7–Q12), resource hotspots (Q13–Q18), query-level waits (Q19–Q22), operational health (Q23–Q25), and modern IQP/feedback checks (Q26–Q32).
Query Store is the most powerful built-in monitoring tool in SQL Server 2016+. It persists query execution history, plan history, runtime statistics, and wait statistics across server restarts — enabling trend analysis without external monitoring tools. This skill is the diagnostic counterpart to sqlplan-review: Query Store tells you which queries need attention; execution plan review tells you why.
Based on Microsoft Query Store DMV documentation and SQL Server community best practices.
Input
Accept any of:
- Raw
sys.query_store_runtime_stats+sys.query_store_query+sys.query_store_planquery output (paste result grid) sys.query_store_wait_statsoutput (SQL 2017+, optional)- Query Store configuration output from
sys.database_query_store_options - A
.csvor.txtfile containing any of the above - A natural language description of Query Store findings ("3 queries regressed after the deployment, Proc_Report went from 200ms to 8s")
Recommended capture queries
Run these in SSMS and paste the output. The primary query (A) is required; queries B and C provide richer analysis.
Query A — Top Resource Consumers (SQL 2016+)
-- Replace the date range as needed. Default: last 7 days.
DECLARE @start_date datetimeoffset = DATEADD(DAY, -7, GETUTCDATE());
DECLARE @end_date datetimeoffset = GETUTCDATE();
DECLARE @top_n integer = 20;
SELECT TOP (@top_n)
database_name = DB_NAME(),
query_sql_text = TRY_CAST(qt.query_sql_text AS nvarchar(200)),
object_name = OBJECT_NAME(q.object_id),
query_id = q.query_id,
query_hash = q.query_hash,
plan_count = COUNT(DISTINCT p.plan_id),
total_executions = SUM(rs.count_executions),
avg_duration_ms = SUM(rs.avg_duration) / NULLIF(SUM(rs.count_executions), 0) / 1000.0,
avg_cpu_ms = SUM(rs.avg_cpu_time) / NULLIF(SUM(rs.count_executions), 0) / 1000.0,
avg_logical_reads = SUM(rs.avg_logical_io_reads) / NULLIF(SUM(rs.count_executions), 0),
avg_physical_reads = SUM(rs.avg_physical_io_reads) / NULLIF(SUM(rs.count_executions), 0),
avg_logical_writes = SUM(rs.avg_logical_io_writes) / NULLIF(SUM(rs.count_executions), 0),
avg_memory_grant_mb = SUM(rs.avg_query_max_used_memory) / NULLIF(SUM(rs.count_executions), 0) * 8.0 / 1024.0,
max_duration_ms = MAX(rs.max_duration) / 1000.0,
min_duration_ms = MIN(rs.min_duration) / 1000.0,
max_cpu_ms = MAX(rs.max_cpu_time) / 1000.0,
min_cpu_ms = MIN(rs.min_cpu_time) / 1000.0,
last_execution_time = MAX(rs.last_execution_time),
is_forced_plan = MAX(CASE WHEN p.is_forced_plan = 1 THEN 1 ELSE 0 END),
force_failure_count = MAX(p.force_failure_count),
last_force_failure_reason_desc = MAX(p.last_force_failure_reason_desc),
aborted_count = SUM(CASE WHEN rs.execution_type = 3 THEN rs.count_executions ELSE 0 END),
exception_count = SUM(CASE WHEN rs.execution_type = 4 THEN rs.count_executions ELSE 0 END),
avg_tempdb_mb = SUM(rs.avg_tempdb_space_used) / NULLIF(SUM(rs.count_executions), 0) * 8.0 / 1024.0
FROM sys.query_store_query AS q
JOIN sys.query_store_query_text AS qt
ON q.query_text_id = qt.query_text_id
JOIN sys.query_store_plan AS p
ON q.query_id = p.query_id
JOIN sys.query_store_runtime_stats AS rs
ON p.plan_id = rs.plan_id
WHERE rs.last_execution_time >= @start_date
AND rs.last_execution_time < @end_date
AND rs.execution_type IN (0, 3, 4) -- 0=regular, 3=aborted (client-initiated), 4=exception
GROUP BY qt.query_sql_text, q.query_id, q.query_hash, q.object_id
HAVING SUM(rs.count_executions) > 0
ORDER BY SUM(rs.avg_cpu_time * rs.count_executions) DESC;
What ships with it
7 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.
- 6d ago First seen · 498 lines · 80 tokens per session scan A 65f1787a532c
sqlquerystore-review is a skill published in the GitHub repository vanterx/mssql-performance-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 9,097 once invoked, about $0.0004 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 2026-08-31.
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