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 seb1n/awesome-ai-agent-skills --skill database-seedinggit clone --depth 1 https://github.com/seb1n/awesome-ai-agent-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/skills/seb1n/awesome-ai-agent-skills/database-seeding)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/database-seeding"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/database-seeding/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/seb1n/awesome-ai-agent-skills/database-seeding"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/database-seeding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.02810 |
| Opus 5 | $0.00019 | $0.01405 |
| Sonnet 5 | $0.00008 | $0.00562 |
| Haiku 4.5 | $0.00004 | $0.00281 |
Grade A, and why
database-seeding 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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- database-seeding — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Seeding
This skill enables an AI agent to generate and insert realistic test data into databases for development, testing, and staging environments. The agent creates idempotent seed scripts using deterministic generators or faker libraries, handles relational data with proper foreign key ordering, supports environment-specific seed profiles (minimal dev data vs. large-scale load testing), and ensures seeds can be run repeatedly without duplicating data.
Workflow
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Analyze the target schema: Inspect the database schema to identify all tables, their columns, data types, constraints (NOT NULL, UNIQUE, CHECK, foreign keys), and relationships. Determine the correct insertion order to satisfy foreign key dependencies — parent tables must be seeded before child tables.
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Design the seed data strategy: Choose the appropriate approach based on the use case. Use deterministic data with fixed seeds for reproducible test suites. Use faker-based generation for realistic-looking development data. Use anonymized production snapshots for staging environments that need realistic data distributions. Define the volume of data for each table.
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Generate seed scripts: Write seed scripts in the project's language (Python, JavaScript, SQL, etc.) that create data matching all schema constraints. Use the Faker library or equivalent for realistic names, emails, addresses, and dates. Handle unique constraints by generating unique values or using sequence-based patterns. Wrap inserts in transactions for atomicity.
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Ensure idempotency: Design scripts to be safely re-runnable. Use INSERT ON CONFLICT DO NOTHING, UPSERT patterns, or truncate-then-insert strategies. Check for existing data before inserting to avoid duplicates or constraint violations on repeated runs.
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Support environment-specific profiles: Create different seed profiles — a small dataset (10-50 records per table) for local development, a medium dataset (1,000-10,000 records) for integration testing, and a large dataset (100K+ records) for performance testing. Control the profile via environment variables or command-line arguments.
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.
- 10d ago First seen · 223 lines · 38 tokens per session scan A 02741952d391
database-seeding is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 2,810 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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