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 skills add EliasOulkadi/shokunin --skill db-sculptorgit clone --depth 1 https://github.com/EliasOulkadi/shokuninWrote 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/eliasoulkadi/shokunin/db-sculptor)<a href="https://agentmods.dev/skills/eliasoulkadi/shokunin/db-sculptor"><img src="https://agentmods.dev/badge/skills/eliasoulkadi/shokunin/db-sculptor/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/skills/eliasoulkadi/shokunin/db-sculptor"><img src="https://agentmods.dev/badge/skills/eliasoulkadi/shokunin/db-sculptor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 194 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00123 | $0.03140 |
| Opus 5.5 | $0.00049 | $0.01256 |
| Sonnet 5.5 | $0.00025 | $0.00628 |
| Haiku 4.5 | $0.00012 | $0.00314 |
Grade A, and why
db-sculptor 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 — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DB Sculptor
Design performant database schemas. Model for access patterns first, normalize later. Based on PostgreSQL internals, Prisma/Drizzle best practices, and production patterns from PlanetScale, Neon, and pganalyze.
Sub-Commands
| Command | Description |
|---|---|
design |
Design a schema from access patterns and data volume estimates |
index |
Analyze queries and recommend/create optimal indexes |
optimize |
Diagnose slow queries with EXPLAIN ANALYZE and fix them |
migrate |
Create a safe, zero-downtime migration (expand/contract) |
audit |
Audit existing schema against best practices and anti-patterns |
Workflow
Step 1: Model for access patterns
| Question | Determine |
|---|---|
| Read/write ratio? | How many indexes can the table support |
| Data volume? | Current rows, growth rate/month |
| Consistency requirements? | ACID vs eventual, read replicas OK? |
| Latency budget? | p50 < 5ms, p95 < 50ms, p99 < 200ms |
Decision tree:
- High write volume, simple reads → Normalize (3NF). Minimum indexes.
- High read volume, complex joins → Denormalize strategically. Add composite + covering indexes.
- Time-series data → Partition by time (monthly). BRIN indexes on timestamp.
- Full-text search needed → GIN index with
tsvector+tsquery. - JSON queries → GIN index on JSONB column.
Step 2: Primary key strategy
| PK type | Pros | Cons | When |
|---|---|---|---|
| UUIDv7 | Time-sortable, globally unique, no collision risk | Larger than bigint (16 bytes vs 8) | Default for user-facing. Distributed systems. |
| UUIDv4 | Random, globally unique | Non-sortable = index fragmentation | Legacy. Avoid for new schemas. |
| bigint (auto-increment) | Fast, compact, sequential | Predictable, not globally unique | Internal/analytics tables. Never for user-facing. |
| ULID | Time-sortable, URL-safe | 26 chars | When human-readable time ordering matters. |
Default: UUIDv7 for all user-facing tables.
What ships with it
5 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.
- 1mo ago First seen · 308 lines · 123 tokens per session scan A 350f486a8f59
db-sculptor is a skill published in the GitHub repository EliasOulkadi/shokunin (112 stars, last pushed 23d ago), licensed MIT. It adds 123 tokens to every session and 3,140 once invoked, about $0.0005 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 2026-08-30.
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