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 sawrus/agent-guides --skill test-data-managementgit clone --depth 1 https://github.com/sawrus/agent-guidesWrote 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/sawrus/agent-guides/test-data-management)<a href="https://agentmods.dev/skills/sawrus/agent-guides/test-data-management"><img src="https://agentmods.dev/badge/skills/sawrus/agent-guides/test-data-management/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/sawrus/agent-guides/test-data-management"><img src="https://agentmods.dev/badge/skills/sawrus/agent-guides/test-data-management.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 | $0.00022 | $0.01144 |
| Opus 5 | $0.00011 | $0.00572 |
| Sonnet 5 | $0.00004 | $0.00229 |
| Haiku 4.5 | $0.00002 | $0.00114 |
Grade A, and why
test-data-management 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 5d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Data Management Skill
Expertise: Factory functions, database isolation, seed data strategies, test pollution prevention.
Factory Pattern (Python — pytest)
# tests/factories.py
from faker import Faker
from decimal import Decimal
import pytest_asyncio
fake = Faker()
def build_user(**overrides) -> dict:
"""Build a user dict — does NOT write to DB"""
return {
"email": fake.email(domain="example-test.com"), # Never real domains
"name": fake.name(),
"role": "viewer",
"password_hash": "hashed_test_password",
**overrides,
}
def build_order(**overrides) -> dict:
return {
"status": "pending",
"total_amount": Decimal("99.99"),
"currency": "USD",
**overrides,
}
# Async factory fixture — writes to DB
@pytest_asyncio.fixture
async def create_user(db_session):
created = []
async def _create(**overrides):
user = User(**build_user(**overrides))
db_session.add(user)
await db_session.flush() # Get ID without committing
created.append(user)
return user
yield _create
# Cleanup is handled by transaction rollback (see isolation below)
# Usage in test
async def test_user_can_view_own_profile(create_user, client):
user = await create_user(role="viewer")
response = await client.get(f"/users/{user.id}", headers=auth_headers(user))
assert response.status_code == 200
assert response.json()["email"] == user.email
Database Isolation Strategies
Option 1: Transaction rollback (fastest — no cleanup needed)
# conftest.py
@pytest_asyncio.fixture
async def db_session(engine):
async with engine.connect() as conn:
transaction = await conn.begin()
session = AsyncSession(bind=conn)
yield session
await transaction.rollback() # Rollback after each test — zero pollution
await session.close()
Option 2: Truncate tables (compatible with most ORM features)
@pytest_asyncio.fixture(autouse=True)
async def clean_tables(db_session):
yield
# After test: truncate in reverse FK order
await db_session.execute(text("TRUNCATE order_items, orders, users RESTART IDENTITY CASCADE"))
await db_session.commit()
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.
- 5d ago First seen · 162 lines · 22 tokens per session scan A 448b262cd9dd
test-data-management is a skill published in the GitHub repository sawrus/agent-guides (17 stars, last pushed 9d ago), licensed MIT. It adds 22 tokens to every session and 1,144 once invoked, about $0.0001 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 2026-09-03.
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deprecation-and-migration
Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when migrating a database schema in production, such as renaming or dropping a column without downtime (expand/contract). Use when deciding whether to maintain or sunset…
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Use when an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.
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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
api-fuzzing-bug-bounty
Provide comprehensive techniques for testing REST, SOAP, and GraphQL APIs during bug bounty hunting and penetration testing engagements. Covers vulnerability discovery, authentication bypass, IDOR exploitation, and API-specific attack vectors.