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 agentmods add skills/datenoio/iterabledata/testing-patternsnpx skills add datenoio/iterabledata --skill testing-patternsgit clone --depth 1 https://github.com/datenoio/iterabledataWhat 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 | $0.00030 | $0.01327 |
| Opus 5 | $0.00015 | $0.00664 |
| Sonnet 5 | $0.00006 | $0.00265 |
| Haiku 4.5 | $0.00003 | $0.00133 |
Grade A, and why
testing-patterns 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 2d 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Testing Patterns
Test Structure
File Naming
- Test files:
test_*.pyintests/directory - One test file per format/feature:
test_csv.py,test_parquet.py - Test classes:
Test*(e.g.,TestCSV,TestParquet) - Test functions:
test_*(e.g.,test_read,test_write)
Running Tests
# All tests
pytest --verbose
# Specific test file
pytest tests/test_csv.py -v
# Specific test function
pytest tests/test_csv.py::TestCSV::test_read -v
# Parallel execution
pytest -n auto
# With coverage
pytest --cov=iterable --cov-report=html
Test Patterns
Basic Read Test
def test_read(self):
with open_iterable('testdata/test.csv') as source:
rows = list(source)
assert len(rows) > 0
assert isinstance(rows[0], dict)
Basic Write Test
def test_write(self, tmp_path):
output = tmp_path / 'output.csv'
data = [{'col1': 'val1', 'col2': 'val2'}]
with open_iterable(output, 'w') as dest:
dest.write_bulk(data)
# Verify written data
with open_iterable(output) as source:
rows = list(source)
assert rows == data
Compression Tests
def test_gzip_compression(self):
with open_iterable('testdata/test.csv.gz') as source:
rows = list(source)
assert len(rows) > 0
Bulk Operations Test
def test_read_bulk(self):
with open_iterable('testdata/test.csv') as source:
chunks = list(source.read_bulk(size=100))
assert len(chunks) > 0
assert all(isinstance(chunk, list) for chunk in chunks)
Edge Cases
def test_empty_file(self):
with open_iterable('testdata/empty.csv') as source:
rows = list(source)
assert rows == []
def test_malformed_data(self):
with pytest.raises(ValueError):
with open_iterable('testdata/malformed.csv') as source:
list(source)
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.
- 2d ago First seen · 246 lines · 30 tokens per session scan A 4b5d5bf4609d
testing-patterns is a skill published in the GitHub repository datenoio/iterabledata (37 stars, last pushed 12d ago), licensed MIT. It adds 30 tokens to every session and 1,327 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.
Other skills, from other repositories
fastapi
FastAPI best practices and conventions. Use when working with FastAPI APIs, Pydantic models, dependencies, streaming responses including Server-Sent Events (SSE), and serving frontend apps. Keeps FastAPI code clean and up to date with the latest features and patterns.
breaking-change-report
Run the japicmp binary-compatibility report for wiremock-core and produce a filtered summary covering only @PublishedAPI-annotated classes. Use this when asked to generate, refresh, or summarise the breaking-changes report.
geoserver-rest-api
Use when automating GeoServer management — programmatic workspace, datastore, and layer creation, style upload, service configuration via REST API. GeoServer REST API: manage GeoServer without GUI using curl, Python, or any HTTP client.
create-or-update-pr
Create a pull request for the current branch, or update the existing one if it already exists. Regenerates the title (Conventional Commits format) and the body (Objective + What was done) from the actual changes, in English. INVOKE when the user asks to "open a PR", "create a PR", "update the PR", "update PR…
adhx
ADHX - X/Twitter Post Reader workflow skill. Use this skill when the user needs Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required and the operator…
Data Analyzer
Statistical analysis and data transformation service for CSV and JSON datasets.