Fs Fixture
privatenumber/fs-fixture
Create disposable file system test fixtures from objects, templates, or empty directories with automatic cleanup.
Populate databases with realistic, reproducible test data for development, testing, and staging environments.
$ npx skills add seb1n/awesome-ai-agent-skills --skill database-seeding -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills database-seeding --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/database/database-seeding .claude/skills/database-seeding && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "database-seeding" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/database/database-seeding into .claude/skills/database-seeding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "database-seeding", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/seb1n/awesome-ai-agent-skills/tree/main/database/database-seedingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add seb1n/awesome-ai-agent-skills --skill database-seeding -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills database-seeding --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/database/database-seeding .agents/skills/database-seeding && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "database-seeding" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/database/database-seeding into .agents/skills/database-seeding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "database-seeding", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add seb1n/awesome-ai-agent-skills --skill database-seeding -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills database-seeding --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/database/database-seeding .cursor/skills/database-seeding && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "database-seeding" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/database/database-seeding into .cursor/skills/database-seeding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "database-seeding", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/seb1n/awesome-ai-agent-skills.git --path database/database-seeding--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add seb1n/awesome-ai-agent-skills --skill database-seeding -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills database-seeding --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/database/database-seeding .gemini/skills/database-seeding && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "database-seeding" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/database/database-seeding into .gemini/skills/database-seeding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "database-seeding", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install seb1n/awesome-ai-agent-skills database-seedingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add seb1n/awesome-ai-agent-skills --skill database-seeding -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/database/database-seeding .github/skills/database-seeding && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "database-seeding" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/database/database-seeding into .github/skills/database-seeding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "database-seeding", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add seb1n/awesome-ai-agent-skills --skill database-seeding -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills database-seeding --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/database/database-seeding .opencode/skills/database-seeding && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "database-seeding" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/database/database-seeding into .opencode/skills/database-seeding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "database-seeding", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
database-seedingPopulate databases with realistic, reproducible test data for development, testing, and staging environments.
Database Seeding is an agent skill from seb1n/awesome-ai-agent-skills. Populate databases with realistic, reproducible test data for development, testing, and staging environments. Use when the user requests database seeding or provides relevant inputs for this workflow.
Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Testing & QA, covering Test data and fixtures. The repository describes itself as: 103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf, and other Agent Skills-compatible tools. Complete SKILL.md… The licence is MIT.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 75865a5. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python and sql).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Database Seeding loads about 2.8k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 784 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 784 words, ~2,829 tokens.
.claude/skills/database-seeding/SKILL.md (or your agent's skills folder).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.
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.
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.
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.
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.
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.
Execute and verify: Run the seed script against the target database, verify row counts match expectations, and confirm relational integrity by checking that all foreign keys reference existing rows. Log the seeding results with counts per table.
Provide the database schema (or point to your migration files) and specify the target environment and desired data volume. The agent will generate a complete seed script that respects all constraints and relationships. You can request specific data characteristics (e.g., "include users from multiple time zones" or "create orders spanning the last 12 months").
Request: Seed a PostgreSQL database with users, products, and orders for development.
"""seed.py — Seed development database with realistic test data."""
import random
from datetime import datetime, timedelta
from faker import Faker
import psycopg2
fake = Faker()
Faker.seed(42) # Deterministic output for reproducibility
random.seed(42)
DB_CONFIG = {
"host": "localhost",
"port": 5432,
"dbname": "dev_db",
"user": "dev_user",
"password": "dev_password",
}
NUM_USERS = 50
NUM_PRODUCTS = 30
NUM_ORDERS = 100
def seed():
conn = psycopg2.connect(**DB_CONFIG)
cur = conn.cursor()
# Seed users
user_ids = []
for _ in range(NUM_USERS):
cur.execute(
"""INSERT INTO users (email, password_hash, full_name, created_at)
VALUES (%s, %s, %s, %s)
ON CONFLICT (email) DO NOTHING
RETURNING id""",
(
fake.unique.email(),
fake.sha256(),
fake.name(),
fake.date_time_between(start_date="-2y", end_date="now"),
),
)
row = cur.fetchone()
if row:
user_ids.append(row[0])
# Seed products
product_ids = []
for i in range(NUM_PRODUCTS):
cur.execute(
"""INSERT INTO products (name, description, price, stock_quantity, sku)
VALUES (%s, %s, %s, %s, %s)
ON CONFLICT (sku) DO NOTHING
RETURNING id""",
(
fake.catch_phrase(),
fake.paragraph(nb_sentences=3),
round(random.uniform(9.99, 499.99), 2),
random.randint(0, 500),
f"SKU-{i+1:05d}",
),
)
row = cur.fetchone()
if row:
product_ids.append(row[0])
# Seed orders with order items
statuses = ["pending", "confirmed", "shipped", "delivered"]
for _ in range(NUM_ORDERS):
user_id = random.choice(user_ids)
status = random.choice(statuses)
items = random.sample(product_ids, k=random.randint(1, 5))
total = 0.0
cur.execute(
"""INSERT INTO orders (user_id, status, total_amount, shipping_address, ordered_at)
VALUES (%s, %s, 0, %s, %s) RETURNING id""",
(user_id, status, fake.address(), fake.date_time_between("-1y", "now")),
)
order_id = cur.fetchone()[0]
for pid in items:
qty = random.randint(1, 4)
price = round(random.uniform(9.99, 499.99), 2)
total += qty * price
cur.execute(
"""INSERT INTO order_items (order_id, product_id, quantity, unit_price)
VALUES (%s, %s, %s, %s)""",
(order_id, pid, qty, price),
)
cur.execute(
"UPDATE orders SET total_amount = %s WHERE id = %s", (round(total, 2), order_id)
)
conn.commit()
cur.close()
conn.close()
print(f"Seeded {len(user_ids)} users, {len(product_ids)} products, {NUM_ORDERS} orders.")
if __name__ == "__main__":
seed()Request: Create a plain SQL seed file for a small development dataset.
-- seed.sql — Idempotent seed data for local development
-- Run with: psql -U dev_user -d dev_db -f seed.sql
BEGIN;
-- Users
INSERT INTO users (id, email, password_hash, full_name, created_at) VALUES
(1, 'alice@example.com', 'hash_alice', 'Alice Johnson', '2024-03-15 09:00:00'),
(2, 'bob@example.com', 'hash_bob', 'Bob Martinez', '2024-05-20 14:30:00'),
(3, 'carol@example.com', 'hash_carol', 'Carol Chen', '2024-07-01 11:15:00'),
(4, 'dave@example.com', 'hash_dave', 'Dave Okafor', '2024-09-10 08:45:00'),
(5, 'eve@example.com', 'hash_eve', 'Eve Andersson', '2024-11-28 16:00:00')
ON CONFLICT (id) DO NOTHING;
-- Products
INSERT INTO products (id, name, description, price, stock_quantity, sku) VALUES
(1, 'Wireless Keyboard', 'Bluetooth mechanical keyboard', 79.99, 150, 'SKU-00001'),
(2, 'USB-C Hub', '7-in-1 USB-C docking station', 49.99, 300, 'SKU-00002'),
(3, 'Noise-Cancelling Headphones', 'Over-ear ANC headphones', 199.99, 75, 'SKU-00003'),
(4, '4K Monitor', '27-inch IPS 4K display', 399.99, 40, 'SKU-00004'),
(5, 'Laptop Stand', 'Adjustable aluminum stand', 34.99, 200, 'SKU-00005')
ON CONFLICT (id) DO NOTHING;
-- Orders
INSERT INTO orders (id, user_id, status, total_amount, shipping_address, ordered_at) VALUES
(1, 1, 'delivered', 129.98, '123 Oak St, Portland, OR 97201', '2024-12-01 10:00:00'),
(2, 2, 'shipped', 199.99, '456 Elm Ave, Austin, TX 78701', '2025-01-05 14:20:00'),
(3, 3, 'confirmed', 484.98, '789 Pine Rd, Seattle, WA 98101', '2025-01-10 09:30:00'),
(4, 1, 'pending', 49.99, '123 Oak St, Portland, OR 97201', '2025-01-12 16:45:00')
ON CONFLICT (id) DO NOTHING;
-- Order items
INSERT INTO order_items (id, order_id, product_id, quantity, unit_price) VALUES
(1, 1, 1, 1, 79.99),
(2, 1, 2, 1, 49.99),
(3, 2, 3, 1, 199.99),
(4, 3, 4, 1, 399.99),
(5, 3, 5, 1, 34.99),
(6, 4, 2, 1, 49.99)
ON CONFLICT (id) DO NOTHING;
-- Reset sequences to avoid conflicts with future inserts
SELECT setval('users_id_seq', (SELECT MAX(id) FROM users));
SELECT setval('products_id_seq', (SELECT MAX(id) FROM products));
SELECT setval('orders_id_seq', (SELECT MAX(id) FROM orders));
SELECT setval('order_items_id_seq', (SELECT MAX(id) FROM order_items));
COMMIT;Faker.seed(42)) to produce deterministic data that makes test results reproducible and diffs in seed output meaningful.assert os.environ["ENV"] != "production") at the top of seed scripts as a safety guard.fake.unique.email() or append a counter to generated values to avoid duplicates. Reset the unique tracker between test runs with fake.unique.clear().© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in database/database-seeding of seb1n/awesome-ai-agent-skills.
Open the folder on GitHubat commit 75865a5
Database Seeding next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Database Seeding this skillseb1n/awesome-ai-agent-skills | 206 | — | ~2.8k | Automated safety check: Pass | MIT | |
| Fs Fixtureprivatenumber/fs-fixture | 100 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Dev Tenant APInightscout/nocturne | 139 | — | ~1.4k | Automated safety check: Pass | None | |
| Rsibench Data Factoryevolvent-ai/RSIBench-Data | 169 | — | ~640 | Automated safety check: Notes | None | |
| Eval Designagentscope-ai/OpenJudge | 868 | — | ~2.8k | Automated safety check: Warn | Apache-2.0 | |
| Data GenerationRed-Hat-AI-Innovation-Team/sdg_hub | 164 | — | ~381 | Automated safety check: Pass | Apache-2.0 |
privatenumber/fs-fixture
Create disposable file system test fixtures from objects, templates, or empty directories with automatic cleanup.
nightscout/nocturne
Interact with Nocturne's local dev-only API: seed a loginable tenant preloaded with realistic sample data, obtain a browser session (loginLink) or bearer token headlessly, export/re-seed the dev…
evolvent-ai/RSIBench-Data
Use inside RSIBench-Data when testing whether an automation agent can improve a target model on a configured benchmark through synthetic Tinker SFT data, Tinker sampling, and E2B-based Harbor…
agentscope-ai/OpenJudge
A skill your agent uses when the user needs to design evaluation datasets, create test cases, stratify samples, generate adversarial examples, extract eval dimensions from traces/specs, or build a…
Red-Hat-AI-Innovation-Team/sdg_hub
A skill your agent uses when the user wants to run synthetic data generation via scripts — detect environment, execute a flow, and present results.
ad-repo/nullplayer
Launch, configure, drive, screenshot and measure the running NullPlayer app.
seb1n/awesome-ai-agent-skills
Plan, execute, document, and retest authorized security assessments of AI agents and multi-agent workflows using safe adversarial cases, synthetic identities, canaries, and evidence-based findings.
seb1n/awesome-ai-agent-skills
Build a preliminary, evidence-based EU AI Act readiness assessment across AI-system inventory, territorial scope, operator roles, prohibited-practice screening, risk classification, transparency…
seb1n/awesome-ai-agent-skills
Design and verify auditable human oversight, approval gates, escalation paths, and safe state transitions for AI agent workflows.
seb1n/awesome-ai-agent-skills
Design, implement, harden, and verify Model Context Protocol (MCP) servers with precise tool contracts, least-privilege authorization, safe transports, structured errors, and interoperability tests.
seb1n/awesome-ai-agent-skills
Audit agent skills, plugins, prompts, manifests, scripts, dependencies, and bundled assets for provenance, prompt-injection, permission, execution, exfiltration, persistence, and update risk.
seb1n/awesome-ai-agent-skills
Inspect, profile, clean, reconcile, analyze, visualize, and verify spreadsheet data while preserving formulas, formatting, types, and source files.
Categories
Populate databases with realistic, reproducible test data for development, testing, and staging environments. Database Seeding is an agent skill from seb1n/awesome-ai-agent-skills. Populate databases with realistic, reproducible test data for development, testing, and staging environments.
Database Seeding fits situations like: the user requests database seeding; provides relevant inputs for this workflow.
Run `npx skills add seb1n/awesome-ai-agent-skills --skill database-seeding -a claude-code`. Or copy the skill folder (database/database-seeding in seb1n/awesome-ai-agent-skills) into .claude/skills/database-seeding in your project. Claude Code loads it when a task matches its description.
Run `npx skills add seb1n/awesome-ai-agent-skills --skill database-seeding -a codex`. Or copy the skill folder (database/database-seeding in seb1n/awesome-ai-agent-skills) into .agents/skills/database-seeding in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add seb1n/awesome-ai-agent-skills --skill database-seeding -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/database-seeding, .gemini/skills/database-seeding, .github/skills/database-seeding and .opencode/skills/database-seeding in your project.
SKILL.md names no scripts, command-line tools or credentials: Database Seeding is instructions for the agent only. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Database Seeding is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Database Seeding: Fs Fixture (privatenumber/fs-fixture, 100 stars), Dev Tenant API (nightscout/nocturne, 139 stars), Rsibench Data Factory (evolvent-ai/RSIBench-Data, 169 stars) and Eval Design (agentscope-ai/OpenJudge, 868 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
seb1n (a GitHub user) maintains it in seb1n/awesome-ai-agent-skills, which has 206 GitHub stars. The repository holds 91 skills in this directory. The repository was last updated on August 9, 2026.
Source: seb1n/awesome-ai-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.