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/instructions/yaml-config.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/yaml-config)<a href="https://agentmods.dev/instructions/data-privacy-stack/presidio/yaml-config"><img src="https://agentmods.dev/badge/instructions/data-privacy-stack/presidio/yaml-config/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/yaml-config"><img src="https://agentmods.dev/badge/instructions/data-privacy-stack/presidio/yaml-config.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 | $0.01345 | $0.01345 |
| Opus 5.5 | $0.00538 | $0.00538 |
| Sonnet 5.5 | $0.00269 | $0.00269 |
| Haiku 4.5 | $0.00135 | $0.00135 |
Grade A, and why
presidio yaml-config.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 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YAML configuration & pydantic validation layer
Rules for the layer that translates YAML configuration into Presidio instances:
the pydantic models in presidio_analyzer/input_validation/
(yaml_recognizer_models.py, schemas.py), the loaders in
recognizer_registry/, and the shipped configs in conf/.
This layer is a public contract. YAML files written by users years ago must keep parsing, and every field a user can write must actually reach the object it configures. When reviewing, lead with:
- A YAML-reachable field that silently goes nowhere (schema/constructor drift).
- A change that makes existing YAML files stop parsing or change meaning.
- A validation failure surfacing as a distant
TypeErrorinstead of a parse-time error with an actionable message.
Schema/constructor sync
Every constructor parameter that should be settable from YAML needs a matching
pydantic field. In every contribution, check that constructor parameters and
schema fields have not drifted apart — a mismatch means a value a user writes
in YAML never reaches the object, or reaches it unvalidated. As of today the
consequence is silent: PredefinedRecognizerConfig ignores unknown YAML keys,
so a constructor kwarg without a schema field is dropped without any error and
the recognizer falls back to its defaults (the failure
LangExtractRecognizerConfig exists to prevent; see its docstring). Even if
that extra behavior changes, the no-mismatch rule stands.
- A recognizer whose constructor takes model-specific kwargs needs a dedicated
config model registered in
CONFIG_MODEL_MAP(keyed byclass_nameorname), followingHuggingFaceRecognizerConfig/GLiNERRecognizerConfig/LangExtractRecognizerConfig. - When a PR adds a constructor parameter to a recognizer that already has a config model, require the matching field in that model — otherwise YAML users cannot set it and get no error telling them so.
extra must be a deliberate choice
extra="forbid"for closed configs (TextChunkerConfig,RecognizerRegistryConfig): typos fail fast at parse time with a clear message.extra="allow"for pass-through configs whose kwargs flow to a constructor (HuggingFace, GLiNER, LangExtract).- Flag a new model that leaves pydantic's default (
extra="ignore") without justification — silent ignoring is almost never the intended behavior.
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 · 109 lines · 1,345 tokens per session scan A f1b3c94eb1b0
presidio yaml-config.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 1,345 tokens to every session, about $0.0054 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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