Presidio is an open-source privacy framework that finds sensitive personal information in text, images, and structured data, then redacts, masks, or anonymizes it. Developers use it to build customizable data-protection and de-identification pipelines.
Borrowing it
Nothing to install: this file belongs to data-privacy-stack/presidio. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/data-privacy-stack/presidio/main/.github/copilot-instructions.mdgit clone --depth 1 https://github.com/data-privacy-stack/presidioWrote 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/instructions/data-privacy-stack/presidio/copilot-instructions)<a href="https://agentmods.dev/instructions/data-privacy-stack/presidio/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/data-privacy-stack/presidio/copilot-instructions/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/instructions/data-privacy-stack/presidio/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/data-privacy-stack/presidio/copilot-instructions.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 | $0.01679 | $0.01679 |
| Opus 5.5 | $0.00672 | $0.00672 |
| Sonnet 5.5 | $0.00336 | $0.00336 |
| Haiku 4.5 | $0.00168 | $0.00168 |
Grade A, and why
presidio copilot-instructions.md 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 today.
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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Presidio Development & Review Instructions
Presidio is a Python-based data protection and de-identification SDK with multiple components for detecting and anonymizing PII (Personally Identifiable Information) in text and images.
Domain-specific rules live in path-scoped instruction files and apply on top of this file when a change touches the matching paths:
.github/instructions/recognizers.instructions.md— adding or modifying PII recognizers..github/instructions/yaml-config.instructions.md— the pydantic layer that translates YAML configuration into Presidio instances.
Core Philosophy
Data privacy is paramount. This is a PII detection and anonymization system used in sensitive contexts; security and correctness are non-negotiable.
- Accuracy first: false negatives (missed PII) and false positives (incorrect detections) both damage trust.
- Security by default: never log PII values, use non-reversible anonymization, validate all inputs.
- Presidio is a library: changes to shared code alter results for users who wrote no new code. Backward compatibility is the prime directive.
- Stateless design: modules that process records are stateless for scalability — avoid adding state.
- Cross-component awareness: Presidio is a multi-component system; changes ripple across boundaries.
- Documentation integrity: code and docs must stay synchronized — outdated docs are dangerous.
Backward Compatibility
Before changing anything outside a brand-new file, the PR description must state what existing behavior changes. These count as behavior changes even without a signature change:
- Default values on shared base classes (
Noneto[]changes truthiness for every subclass). - Properties on abstract interfaces — custom implementations inherit the new default and may break.
- Anything altering which entities are returned, or their scores, for text that previously worked.
Prefer additive changes: new parameters get defaults preserving current behavior; public APIs are never broken without a deprecation path.
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.
- today Changed af276dc41e43
- 1mo ago First seen · 171 lines · 1,679 tokens per session scan A da9bc2a72f45
presidio copilot-instructions.md is an instructions file published in the GitHub repository data-privacy-stack/presidio (11,528 stars, last pushed yesterday), licensed MIT. It adds 1,679 tokens to every session, about $0.0067 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 2026-08-31.
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