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/sqlwait-reviewnpx skills add vanterx/mssql-performance-skills --skill sqlwait-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/sqlwait-review)<a href="https://agentmods.dev/skills/vanterx/mssql-performance-skills/sqlwait-review"><img src="https://agentmods.dev/badge/skills/vanterx/mssql-performance-skills/sqlwait-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 | $0.00174 | $0.21915 |
| Opus 5 | $0.00087 | $0.10958 |
| Sonnet 5 | $0.00035 | $0.04383 |
| Haiku 4.5 | $0.00017 | $0.02192 |
Grade A, and why
sqlwait-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 5d 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 — 818 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Server Wait Statistics Review Skill
Purpose
Analyze SQL Server wait statistics and identify the dominant bottleneck using the Waits and Queues methodology. Applies 44 checks (V1–V44): V1–V18 classify each significant wait type into its root cause and produce a prioritized remediation plan; V19–V26 perform multi-snapshot trend analysis when 3+ time windows are provided — detecting worsening trends, spikes, peak periods, and emerging bottlenecks; V27–V29 cover specialized scenarios (PAGELATCH on user databases, backup I/O, cumulative skew from outlier events); V30–V36 cover modern feature wait types (In-Memory OLTP, Columnstore, Query Store, Transaction/DTC, Service Broker, Full Text Search, Parallel Redo); V37–V40 add DMV-level memory and I/O detail — forced memory grants, grant timeouts, stolen memory, and file-level I/O latency (requires optional capture queries); V41–V44 cover SQL 2019/2022 IQP/PSP/ADR feature-specific wait types and TempDB memory-optimized metadata contention (SQL 2019+).
The Waits and Queues methodology is based on how SQL Server's thread scheduler works: threads are always in one of three states — RUNNING (on CPU), RUNNABLE (queued for CPU), or SUSPENDED (waiting for a resource). Every time a thread suspends, SQL Server records the wait type and duration. Analyzing the top accumulated waits reveals the dominant bottleneck — not by guessing, but by measuring exactly what the server spent its time waiting for.
Wait analysis answers the question execution plans cannot: why is the server slow when no individual query has a bad plan? The answer is almost always in the wait types — I/O, locks, CPU, memory, or network.
Input
Accept any of:
- Output from the
sys.dm_os_wait_statscapture query below (paste the result grid) - Output from
sys.dm_exec_requestsfor current active session waits - A
.txtor.csvfile containing either of the above - A natural language description of the top wait types ("PAGEIOLATCH_SH is 78% of waits, CXPACKET is 12%")
What ships with it
10 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.
- assets/.gitkeep 0 B
- evals/evals.json 1016 B
- examples/wait_stats-analysis.md 19 KB
- examples/wait_stats.txt 3.0 KB
- examples/wait_trend-analysis.md 15 KB
- examples/wait_trend.txt 6.3 KB
- references/check-explanations.md 95 KB
- references/README.md 1.1 KB
- scripts/01_capture_wait_stats.sql 9.5 KB
- scripts/02_capture_active_requests.sql 4.2 KB
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.
- 5d ago First seen · 818 lines · 174 tokens per session scan A 48e631567354
sqlwait-review is a skill published in the GitHub repository vanterx/mssql-performance-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 174 tokens to every session and 21,915 once invoked, about $0.0009 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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