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/v0idos/performance-deity/databasenpx skills add v0idOS/performance-deity --skill databasegit clone --depth 1 https://github.com/v0idOS/performance-deityWhat 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 | $0.00034 | $0.00308 |
| Opus 5 | $0.00017 | $0.00154 |
| Sonnet 5 | $0.00007 | $0.00062 |
| Haiku 4.5 | $0.00003 | $0.00031 |
Grade A, and why
database 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 yesterday.
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.
What it actually says
Execute all three phases in order.
Phase 1 — Analysis
- Take the slow query or ORM code.
- Generate the equivalent raw SQL.
- Either instruct the user to run
EXPLAIN QUERY PLAN(SQLite) orEXPLAIN ANALYZE(Postgres/MySQL), or infer missing indexes directly from theWHERE,JOIN, andORDER BYclauses.
Phase 2 — N+1 Audit
Check whether queries are issued inside a loop. If yes, rewrite using:
IN (...)batch clause- SQL
JOIN - ORM eager loading:
.include()— Prisma.populate()— Mongooseselect_related()/prefetch_related()— Django
Phase 3 — Rewrite
- Provide the optimized SQL or ORM code.
- Provide the exact
CREATE INDEXstatements required. - Explain the disk I/O reduction: Full Table Scan O(N) → Index Lookup O(log N).
Before/after query plan summary:
| Metric | Before | After |
|---|---|---|
| Scan type | Full Table Scan | Index Lookup |
| Complexity | O(N) | O(log N) |
| Query count (per page load) | N+1 | 1 |
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.
- yesterday First seen · 36 lines · 34 tokens per session scan A 252f2245b25e
database is a skill published in the GitHub repository v0idOS/performance-deity (2 stars, last pushed 4mo ago), licensed MIT. It adds 34 tokens to every session and 308 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 2026-08-31.
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