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 catpilotai/catpilot-ai-guardrails --skill pii-and-test-datagit clone --depth 1 https://github.com/catpilotai/catpilot-ai-guardrailsWrote 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/catpilotai/catpilot-ai-guardrails/pii-and-test-data)<a href="https://agentmods.dev/skills/catpilotai/catpilot-ai-guardrails/pii-and-test-data"><img src="https://agentmods.dev/badge/skills/catpilotai/catpilot-ai-guardrails/pii-and-test-data/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/catpilotai/catpilot-ai-guardrails/pii-and-test-data"><img src="https://agentmods.dev/badge/skills/catpilotai/catpilot-ai-guardrails/pii-and-test-data.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 | $0.00097 | $0.04282 |
| Opus 5 | $0.00048 | $0.02141 |
| Sonnet 5 | $0.00019 | $0.00856 |
| Haiku 4.5 | $0.00010 | $0.00428 |
Grade A, and why
pii-and-test-data 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 — 472 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Why
Production data carries legal, contractual, and operational obligations that test environments are not designed to honor. Every time a real customer record appears in a fixture file, a comment, a screenshot, a chat transcript, or a debug log, the organization picks up the same obligations for that copy as for the original — without the controls that protect the original.
The failures this skill blocks share a common shape: a real customer's data ends up somewhere the customer's data was never authorized to be.
- A test fixture committed to git uses a real customer's email, phone, or address. The repository becomes a partial copy of the customer database, retained for the life of the project.
- A debug log includes a real request body — full names, SSN-like identifiers, payment-card-shaped numbers — and the log is shipped to a third-party observability vendor.
- A demo recording shows the production app with a real customer account loaded. The recording is shared on a public marketing page.
- "Just for testing," a developer pulls a row from the production
userstable into a local SQLite database. The local database is now production data without production controls. - An error message embeds the user's email or phone for debugging. The error is logged, indexed, and surfaced in an analytics dashboard a customer-success team can search.
This is a tractable problem: every domain that uses real identifiers has a reserved test range or synthetic-data tool. The agent's job is to default to the synthetic option and refuse the real one.
When to apply
Apply this skill before the agent writes, recommends, or commits any of the following:
- Test fixtures, factories, seed files, demo data,
db/seeds.rb,fixtures/,tests/fixtures/,__tests__/data/, or any other data file consumed by an automated test or local dev environment. - Code comments, docstrings, README examples, or documentation that includes an illustrative user record.
- Error messages, log lines, telemetry payloads, or anything shipped to an observability vendor.
- Screenshots, screen recordings, marketing assets, or anything shared outside the organization.
- Migration scripts, ETL pipelines, or anything moving rows
between environments — particularly
prod→ anything-else. - LLM prompts, fine-tuning datasets, evaluation suites, or RAG ingestion corpora.
- Bug-report templates and incident-response artifacts where reproducers might include real data.
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 · 472 lines · 97 tokens per session scan A fb0e8397b62a
pii-and-test-data is a skill published in the GitHub repository catpilotai/catpilot-ai-guardrails (2 stars, last pushed 2mo ago), licensed MIT. It adds 97 tokens to every session and 4,282 once invoked, about $0.0005 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.
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