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/log-sanitization)<a href="https://agentmods.dev/rules/microsoft/data-formulator/log-sanitization"><img src="https://agentmods.dev/badge/rules/microsoft/data-formulator/log-sanitization/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/log-sanitization"><img src="https://agentmods.dev/badge/rules/microsoft/data-formulator/log-sanitization.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 | $0.00000 | $0.01027 |
| Opus 5.5 | $0.00000 | $0.00411 |
| Sonnet 5.5 | $0.00000 | $0.00205 |
| Haiku 4.5 | $0.00000 | $0.00103 |
Grade A, and why
log-sanitization 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.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Log Sanitization
Server-side logs must never contain passwords, tokens, API keys, or other credentials
in plain text. This rule complements error-response-safety.mdc (which covers client
responses) by addressing the logging side.
Architecture: Defense in Depth
Two layers protect against sensitive data in logs:
- Layer 1 — Explicit utilities (call-site): developers use
sanitize_url(),sanitize_params(),redact_token()fromsecurity/log_sanitizer.py. - Layer 2 — SensitiveDataFilter (global): a
logging.Filterregistered on all handlers inconfigure_logging()auto-redacts patterns that slip through.
Layer 1 is the primary defense; Layer 2 is a safety net. Both are required.
When to Use Each Utility
| Data Type | Utility | Example |
|---|---|---|
dict with password/secret/token keys |
sanitize_params(params) |
log.info("Init: %s", sanitize_params(params)) |
URL (may have user:pass@host or sensitive query params) |
sanitize_url(url) |
logger.info("Connecting to %s", sanitize_url(url)) |
| Bearer / access token | redact_token(token) |
logger.debug("Token: %s", redact_token(token)) |
| Normal string (no secrets) | Nothing needed | logger.info("Processing table %s", name) |
Rules
-
Never log raw
params/configdicts that may containpassword,secret,api_key,token, orconnection_stringkeys. Always usesanitize_params(). -
Never log full tokens or API keys. Use
redact_token()if you need to identify a token in logs. -
Prefer
type(exc).__name__overstr(exc)in warning/info logs when the exception may contain connection strings or upstream response bodies. Useexc_info=Truefor full tracebacks at ERROR level (the filter sanitizesexc_text). -
URL logging — use
sanitize_url()for any URL from configuration or environment variables (issuer URLs, JWKS URLs, database URLs, API base URLs). It masks embedded credentials and sensitive query parameters such aspassword,client_secret,token, andapi_key.
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 · 108 lines · 0 tokens per session scan A 22aa7feb8a62
log-sanitization is a cursor rule published in the GitHub repository microsoft/data-formulator (17,610 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,027 tokens. 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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