Data Formulator is an interactive workspace where AI agents connect to data sources, answer questions, and create visualizations. It is used to explore and analyze data while branching into related questions without losing the context of each investigative path.
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.
git clone --depth 1 https://github.com/microsoft/data-formulatorWrote 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/rules/microsoft/data-formulator/dataframe-serialization)<a href="https://agentmods.dev/rules/microsoft/data-formulator/dataframe-serialization"><img src="https://agentmods.dev/badge/rules/microsoft/data-formulator/dataframe-serialization/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/rules/microsoft/data-formulator/dataframe-serialization"><img src="https://agentmods.dev/badge/rules/microsoft/data-formulator/dataframe-serialization.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 | $0.00369 | $0.00369 |
| Opus 5.5 | $0.00148 | $0.00148 |
| Sonnet 5.5 | $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 1mo 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.
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.
- 1mo ago 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 (17,610 stars, last pushed today), licensed MIT. It adds 369 tokens to every session, about $0.0015 per session on Opus 5.5. 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.
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