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/deepsqlai/deepsql/workload-analysisnpx skills add DeepSQLAI/deepsql --skill workload-analysisgit clone --depth 1 https://github.com/DeepSQLAI/deepsqlWhat 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 | $0.00031 | $0.00684 |
| Opus 5 | $0.00015 | $0.00342 |
| Sonnet 5 | $0.00006 | $0.00137 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade A, and why
workload-analysis 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 2d 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 — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workload Analysis
Use for whole-workload questions: "what's slow right now?", "what regressed this week?", "which customer is driving load?", "what's growing fast?" For a single named query, use slow-query-optimize.
Procedure
-
Resolve the connection (
list_connections→ UUID). -
Find the hotspots.
get_slow_query_insights(connectionId, kind="all", window=…)returns pre-computed AI insights grouped ashotspots(most total DB time),remediation(actionable fixes),tail-risk(p95/max outliers),plan-drift(plan changed),skew(one tenant overloaded).analyze_slow_queriesandlist_tracked_queriesgive the raw fingerprint list with call counts and mean/max times. -
Catch regressions.
get_query_regressions(connectionId)ranks queries that got slower on the latest analysis run by slowdown factor. Drill into one withget_slow_query_timeline(queryId)to see the day-by-day trend. -
Attribute load.
get_slow_query_customers(connectionId)ranks tenants/customers by total slow-query time (with resolved customer name when configured) — answers "who is driving the load?" -
Watch growth.
get_table_growth(connectionId)for size/row-count trends;get_growth_anomalies(connectionId)for tables growing abnormally. These predict the next performance cliff before it hits. -
Synthesize, then route. Lead with the few things that matter most (biggest total-time consumer, worst regression, fastest-growing table). For each, hand off to the right next step: a specific slow query →
slow-query-optimize; an indexing opportunity →index-advisor.
Guardrails
- Rank by total impact (
calls × mean_exec_time), not by single-execution worst case — a 5-second query run twice a day matters less than a 200ms query run a million times. - Start with compact persisted analytics (
get_latest_slow_query_analysis,get_slow_query_insights,list_tracked_queries). If a persisted payload is too large/truncated to inspect cleanly, pivot to the compact endpoints rather than trying to parse the oversized blob. - Use
analyze_slow_querieswhen you need a fresh top-N snapshot for the last 24 hours; say explicitly that this triggers fresh analysis work, unlike the persisted analytics endpoints. - Everything here is read-only and analytics-store backed unless you intentionally call
analyze_slow_queriesfor a fresh collection. Say so if the user worries about adding load. get_query_samplesexposes literal bind values — treat the output as potentially sensitive 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.
- 2d ago First seen · 37 lines · 31 tokens per session scan A eafbe05f9fe7
workload-analysis is a skill published in the GitHub repository DeepSQLAI/deepsql (23 stars, last pushed 2d ago), licensed Apache-2.0. It adds 31 tokens to every session and 684 once invoked, about $0.0002 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 2026-08-30.
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