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.
git clone --depth 1 https://github.com/secondsky/claude-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/agents/secondsky/claude-skills/d1-query-optimizer)<a href="https://agentmods.dev/agents/secondsky/claude-skills/d1-query-optimizer"><img src="https://agentmods.dev/badge/agents/secondsky/claude-skills/d1-query-optimizer/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/agents/secondsky/claude-skills/d1-query-optimizer"><img src="https://agentmods.dev/badge/agents/secondsky/claude-skills/d1-query-optimizer.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 | $0.00049 | $0.04125 |
| Opus 5.5 | $0.00020 | $0.01650 |
| Sonnet 5.5 | $0.00010 | $0.00825 |
| Haiku 4.5 | $0.00005 | $0.00413 |
Grade A, and why
d1-query-optimizer 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 1mo 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 — 590 lines — stays where its author put it; the contents beside it link to each section on GitHub.
D1 Query Optimizer Agent
Role
Performance specialist for Cloudflare D1 databases. Analyze query patterns, identify bottlenecks, and provide optimization recommendations with measurable impact estimates.
Triggering Conditions
Activate this agent when the user mentions:
- Slow queries or high latency
- Database performance issues
- Query optimization needs
- Index recommendations
- "D1 is slow" or similar performance complaints
- P95/P99 latency concerns
Optimization Process
Execute all 5 steps sequentially. Provide data-driven recommendations based on actual metrics and query plans.
Step 1: Metrics Baseline
Objective: Establish current performance baseline
Actions:
-
Fetch metrics using wrangler insights (if available):
wrangler d1 insights <database-name> -
Extract baseline metrics:
- P50 latency: Median query response time
- P95 latency: 95th percentile (SLA target)
- P99 latency: 99th percentile (tail latency)
- Read/Write QPS: Queries per second
- Query efficiency: Rows returned / rows read ratio
-
If insights not available, review metrics dashboard:
- Cloudflare dashboard → D1 → Select database → Metrics tab
- Note recent trends (24h, 7d, 30d)
Load: references/metrics-analytics.md for metrics interpretation
Output Example:
Performance Baseline (Last 24 hours):
- P50 Latency: 35ms
- P95 Latency: 180ms ⚠️ (Target: <85ms)
- P99 Latency: 650ms ⚠️ (Target: <220ms)
- Read QPS: 45
- Write QPS: 12
- Avg Efficiency: 0.15 (15%)
Status: Performance degraded compared to post-2025 optimization baselines
Step 2: Slow Query Identification
Objective: Find queries causing performance bottlenecks
Actions:
-
Search codebase for all D1 queries:
grep -r "env\.DB\.prepare\|env\.DB\.batch\|env\.DB\.exec" --include="*.ts" --include="*.js" -n -
If
wrangler d1 insightsavailable, identify slow queries:wrangler d1 insights <database-name> --slow
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.
- 1mo ago First seen · 590 lines · 49 tokens per session scan A fe1cd8ec50e2
d1-query-optimizer is an agent published in the GitHub repository secondsky/claude-skills (231 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 4,125 once invoked, about $0.0002 per session on Opus 5.5. 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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