Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Hainrixz/claude-dbnpx agentmods add skills/hainrixz/claude-db/seedWrote 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/hainrixz/claude-db/seed)<a href="https://agentmods.dev/skills/hainrixz/claude-db/seed"><img src="https://agentmods.dev/badge/skills/hainrixz/claude-db/seed.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 | $0.00098 | $0.00653 |
| Opus 5 | $0.00049 | $0.00327 |
| Sonnet 5 | $0.00020 | $0.00131 |
| Haiku 4.5 | $0.00010 | $0.00065 |
Grade A, and why
seed 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 8d 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 — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/claude-db:seed
Generates FK-aware, deterministic sample/seed INSERTs from a schema. Read-only — it never connects to or writes to a database. The output is dev/test data only: predictable values derived from the row index (no RNG), suitable for fixtures, local development, and CI — never for production.
$ARGUMENTS = <path-to-schema> [flags]. The target is a schema/DDL, ORM model, or migration file. If no path is given, detect one in the working directory (look for *.sql, schema.prisma, models.py, migration dirs). If nothing is found, say so plainly and suggest pointing at a file or running /claude-db:design to create one.
What to do
- Resolve the schema path (from
$ARGUMENTSor detection above). - Run the generator:
node scripts/gen-seed.mjs --file <schema> --rows N [--format sql|json]--rows Ndefaults to 5 (clamped 1–1000).--formatdefaults tosql;jsonreturns the statements as an array plus metadata.- The script topologically sorts tables so each table's FK parents are inserted first, fills FK columns with valid parent ids, and skips auto-increment/identity PKs.
- Present the result:
- The insert order (dependency order, parents → children) and per-table row counts.
- The generated
INSERTstatements, grouped by table in that order. - The parser confidence and any tables skipped or cycles ignored.
- Offer to save to a file (e.g.
seed.sqlorfixtures.json) — only write if the user confirms a path. Otherwise leave it inline.
Notes
- Deterministic by design: re-running with the same
--rowsyields byte-identical output, so seeds are diff-friendly and reproducible. - Values are illustrative placeholders (emails like
[email protected], fixed timestamps, sequential ids) — not realistic or privacy-safe production data. - Cyclic FKs are ignored for ordering; deferred-constraint or self-referential cases may need a manual pass after generation.
For dev/test fixtures only. Never run generated seeds against a production database. Respond in the user's language (EN/ES).
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.
- 8d ago First seen · 34 lines · 98 tokens per session scan A ff6a4e8220e0
seed is a skill published in the GitHub repository Hainrixz/claude-db (19 stars, last pushed 2mo ago), licensed MIT. It adds 98 tokens to every session and 653 once invoked, about $0.0005 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.
Other skills, from other repositories
dynamodb
Use when modeling or operating a DynamoDB table: deriving partition/sort keys from access patterns, single-table vs table-per-entity, adding a GSI/LSI, on-demand vs provisioned capacity, or diagnosing hot-partition throttling. NOT relational schema/SQL/EXPLAIN (that is postgresdb), NOT aggregation-pipeline document…
malloy-lookml-review
Analyze LookML files as prior art for Malloy modeling. Used during Step 1 (DISCOVER) when .lkml files are present. Coordinates reference files that extract business logic, relationships, and curation decisions. Works with or without a database connection.
convex-migrations
Schema migration strategies for evolving applications including adding new fields, backfilling data, removing deprecated fields, index migrations, and zero-downtime migration patterns.
database-design-patterns
Database schema design patterns and optimization strategies for relational and NoSQL databases. Use when designing database schemas, optimizing query performance, or implementing data persistence layers at scale.
database-sql
Design database schemas, write efficient SQL queries, create migrations, and optimize database performance. Use when working with databases, writing queries, or designing data models.
data-design
Data modeling, schema design, and data architecture.