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 rules/microsoft/data-formulator/dataframe-serializationgit clone --depth 1 https://github.com/microsoft/data-formulatorWhat 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 | $0.00369 | $0.00369 |
| Opus 5 | $0.00185 | $0.00185 |
| Sonnet 5 | $0.00074 | $0.00074 |
| Haiku 4.5 | $0.00037 | $0.00037 |
Grade A, and why
dataframe-serialization 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 yesterday.
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.
What it actually says
DataFrame Serialization
All DataFrame-to-records conversion for API responses, streaming events, or
frontend-visible data MUST use the centralized helpers in
data_formulator.datalake.parquet_utils:
| Source type | Helper |
|---|---|
pd.DataFrame |
df_to_safe_records(df) |
pa.Table (Arrow) |
get_sample_rows_from_arrow(table) |
Why
pandas.DataFrame.to_json(orient='records') defaults to date_format='epoch',
which serializes datetime columns as epoch milliseconds (e.g. 1773532800000).
The frontend interprets these as plain numbers and renders them with commas
(1,773,532,800,000) instead of formatted dates.
df_to_safe_records enforces date_format='iso' and default_handler=str,
ensuring datetimes become ISO-8601 strings and exotic types degrade gracefully.
Banned Patterns
# BAD — missing date_format, datetimes become epoch numbers
json.loads(df.to_json(orient='records'))
# BAD — to_dict returns Timestamp objects, not JSON-safe values
df.to_dict(orient='records')
# ACCEPTABLE but should be unified for consistency
json.loads(df.to_json(orient='records', date_format='iso'))
Correct Pattern
from data_formulator.datalake.parquet_utils import df_to_safe_records
rows = df_to_safe_records(df)
preview = df_to_safe_records(df.head(5))
Exceptions
Internal data processing that never reaches the frontend or JSON serialization
(e.g. Kusto SDK metadata parsing, Vega-Lite spec construction) may use
to_dict(orient='records') directly.
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.
- yesterday First seen · 49 lines · 369 tokens per session scan A 422675decc33
dataframe-serialization is a cursor rule published in the GitHub repository microsoft/data-formulator (17,048 stars, last pushed 3d ago), licensed MIT. It adds 369 tokens to every session, about $0.0018 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 cursor rules, from other repositories
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
typescript
Changes to these high-fan-out internals can affect every message, delta, element, or rerun. Keep work in them minimal, and benchmark changes with representative stress-test apps.
coolify-ai-docs
Master reference to all Coolify AI documentation in .ai/ directory.
python_lib
Tips and guidelines specific to the development of the Streamlit Python library, not applicable to scripts and e2e tests.
specs
This directory contains product and tech specs for Streamlit features.