backend-data-masking

backend-data-masking is a skill for Claude Code, Codex from j4flmao/agent-skills. It costs 112 tokens per session (5,351 once invoked), scanned A, original, MIT.

A guide for protecting sensitive personal data, such as names, addresses, or payment details. It covers masking, encryption, tokenization, anonymization, redaction, and data classification for privacy requirements such as GDPR and CCPA.

In plain words
What is it for?
Use it to identify and classify sensitive fields, choose how each field should be protected, implement the protection, record access, and support deletion requests.
Why use it?
It helps reduce the risk of exposing personal information and provides a way to match protection methods to different kinds of sensitive data.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument; mentions Codex.

Good fit Use it to identify and classify sensitive fields, choose how each field should be protected, implement the protection, record access, and support deletion requests.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/j4flmao/agent-skills/data-masking
Install

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.

Any agent
npx skills add j4flmao/agent-skills --skill data-masking
Clone the repo
git clone --depth 1 https://github.com/j4flmao/agent-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for backend-data-masking

README.md
[![agentmods](https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-masking/github.svg)](https://agentmods.dev/skills/j4flmao/agent-skills/data-masking)
Your own site
<a href="https://agentmods.dev/skills/j4flmao/agent-skills/data-masking"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-masking/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.

agentmods 80×15 button for backend-data-masking

Your own site · 80×15
<a href="https://agentmods.dev/skills/j4flmao/agent-skills/data-masking"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-masking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 112 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,351 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium analysis-evasion · line 1
    Suspicious Unicode normalization or mixed-script content
    Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00112 $0.05351
Opus 5 $0.00056 $0.02676
Sonnet 5 $0.00022 $0.01070
Haiku 4.5 $0.00011 $0.00535

Measured 8d ago against content hash ada555f55a48, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

backend-data-masking 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 8d 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.

skills/backend/universal/data-masking/SKILL.md · 561 lines

How it starts

The opening of the file, as written. The whole thing — 561 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Backend Data Masking

Purpose

Protect personally identifiable information (PII) using masking, encryption, tokenization, and anonymization techniques for GDPR/CCPA compliance.

Agent Protocol

Trigger

Exact user phrases: "data masking", "PII", "personally identifiable information", "encryption", "anonymization", "GDPR", "CCPA", "data privacy", "field-level encryption", "tokenization", "redaction", "data classification", "mask sensitive data".

Input Context

  • Data model — which fields contain sensitive data.
  • Classification of each field (PII, PCI, PHI, internal).
  • Regulatory requirements (GDPR, CCPA, HIPAA, PCI-DSS).

Output Artifact

Data masking configuration or implementation code. No file unless requested.

Response Format

Field: {field name}
Classification: {PII|PCI|PHI|Internal}
Protection: {mask|encrypt|tokenize|anonymize|redact}

Completion Criteria

  • All sensitive fields identified and classified.
  • Protection strategy chosen per classification.
  • Masking/encryption implemented at the application or database layer.
  • Audit log of data access recorded.
  • Right-to-deletion workflow for GDPR Article 17.

Max Response Length

3 lines per field. 15 lines for full plan.

Architecture Decision Tree

Which Protection Strategy?

Is the data needed for business operations?
  ├── Yes → Does the business need the original value?
  │         ├── Yes → Encrypt (symmetric or asymmetric)
  │         └── No → Tokenize (map to surrogate value)
  ├── No → Is the data needed for display/UI?
  │         ├── Yes → Mask on read (partial display)
  │         └── No → Is the data needed for analytics?
  │                  ├── Yes → Anonymize (statistically useful, not reversible)
  │                  └── No → Redact (remove entirely)

Where to Apply Protection?

Is the data at rest in a database?
  ├── Yes → Field-level encryption OR tokenization at write time
  ├── No → Is the data in transit in an API response?
  │         ├── Yes → Mask or redact at the presentation layer
  │         └── No → Is the data in logs?
  │                  ├── Yes → Redact entirely — never log PII
  │                  └── No → Is the data in a backup/export?
  │                           ├── Yes → Anonymize or encrypt
  │                           └── No → Apply at the closest boundary

Read the full file on GitHub · 561 lines

Changes

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.

  1. 8d ago First seen · 561 lines · 112 tokens per session scan A ada555f55a48

Subscribe to this mod's changes

backend-data-masking is a skill published in the GitHub repository j4flmao/agent-skills (23 stars, last pushed 5d ago), licensed MIT. It adds 112 tokens to every session and 5,351 once invoked, about $0.0006 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 2026-09-03.

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