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 mexmarv/ai-genie-factory --skill testing-scaffoldgit clone --depth 1 https://github.com/mexmarv/ai-genie-factoryWrote 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/mexmarv/ai-genie-factory/testing-scaffold)<a href="https://agentmods.dev/skills/mexmarv/ai-genie-factory/testing-scaffold"><img src="https://agentmods.dev/badge/skills/mexmarv/ai-genie-factory/testing-scaffold/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/mexmarv/ai-genie-factory/testing-scaffold"><img src="https://agentmods.dev/badge/skills/mexmarv/ai-genie-factory/testing-scaffold.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00080 | $0.01092 |
| Opus 5 | $0.00040 | $0.00546 |
| Sonnet 5 | $0.00016 | $0.00218 |
| Haiku 4.5 | $0.00008 | $0.00109 |
Grade A, and why
testing-scaffold 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 11d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Testing Scaffold
Every generated app must include tests/test_data.py and tests/test_logic.py.
Tests must be present even if minimal — this enforces testability from day one.
Rules
- Data layer tests mock
data.WorkspaceClient— never hit Unity Catalog or the warehouse - Logic layer tests use small hardcoded
pandasDataFrames - Always use
pytest— nounittestdirectly - Test function names:
test_<function>_<condition> - No Spark, no live Databricks SDK calls in any test
tests/test_data.py
"""Tests for data.py — all WorkspaceClient calls are mocked."""
import pytest
import pandas as pd
from unittest.mock import patch, MagicMock
from data import DataAccessError, load_orders
def _make_result(rows, cols, state="SUCCEEDED", error_message=None):
"""Helper: build a mock Statement Execution result."""
result = MagicMock()
result.status.state.value = state
result.status.error.message = error_message
result.manifest.schema.columns = [MagicMock(name=c) for c in cols]
for column, name in zip(result.manifest.schema.columns, cols):
column.name = name
result.result.data_array = rows
return result
@patch("data.WorkspaceClient")
def test_load_orders_returns_dataframe(mock_client_cls):
mock_client = mock_client_cls.return_value
mock_client.statement_execution.execute_statement.return_value = _make_result(
rows=[["2024-01-01", "North", "100.0", "1", "1"]],
cols=["order_date", "region", "amount", "order_id", "customer_id"],
)
config = {"table_name": "prod.gold.orders", "warehouse_id": "wh-1", "row_limit": 1000}
df = load_orders(config, "2024-01-01", "2024-01-31")
assert isinstance(df, pd.DataFrame)
assert list(df.columns) == ["order_date", "region", "amount", "order_id", "customer_id"]
assert len(df) == 1
@patch("data.WorkspaceClient")
def test_load_orders_raises_data_access_error_on_failure(mock_client_cls):
mock_client = mock_client_cls.return_value
mock_client.statement_execution.execute_statement.side_effect = Exception("Warehouse timeout")
config = {"table_name": "prod.gold.orders", "warehouse_id": "wh-1", "row_limit": 1000}
with pytest.raises(DataAccessError, match="unavailable"):
load_orders(config, "2024-01-01", "2024-01-31")
@patch("data.WorkspaceClient")
def test_load_orders_raises_on_non_gold_schema(mock_client_cls):
config = {"table_name": "prod.silver.orders", "warehouse_id": "wh-1", "row_limit": 1000}
with pytest.raises(DataAccessError, match="Gold"):
load_orders(config, "2024-01-01", "2024-01-31")
mock_client_cls.return_value.statement_execution.execute_statement.assert_not_called()
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.
- 11d ago First seen · 135 lines · 80 tokens per session scan A 779607bb74b9
testing-scaffold is a skill published in the GitHub repository mexmarv/ai-genie-factory (5 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 1,092 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
go-testing
Trigger: Go tests, go test coverage, Bubbletea teatest, golden files. Apply focused Go testing patterns.
nw-fp-clojure
Clojure language-specific patterns, data-first modeling, REPL-driven development, and spec.
mobiai-ios-testing
Use when writing or running tests in an iOS project — unit tests, UI tests, snapshot tests, choosing the right framework.
restore-internals-seams-in-finally-blocks-after-each-test
When delegating a task affected by this skill, include.
unit-test-parameterized
Provides parameterized testing patterns with JUnit 5, generates data-driven unit tests using @ParameterizedTest, @ValueSource, @CsvSource, @MethodSource. Creates tests that run the same logic with multiple input values. Use when writing data-driven Java tests, multiple test cases from single method, or boundary value…
testing-llm
LLM and AI testing patterns — mock responses, evaluation with DeepEval/RAGAS, structured output validation, and agentic test patterns (generator, healer, planner). Use when testing AI features, validating LLM outputs, or building evaluation pipelines.