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 j4flmao/agent-skills --skill data-maskinggit clone --depth 1 https://github.com/j4flmao/agent-skillsWrote 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/j4flmao/agent-skills/data-masking)<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.
<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>- NVIDIA SkillSpector warn
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 contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00112 | $0.05351 |
| Opus 5 | $0.00056 | $0.02676 |
| Sonnet 5 | $0.00022 | $0.01070 |
| Haiku 4.5 | $0.00011 | $0.00535 |
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.
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
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/data-classification.md 6.0 KB
- references/data-masking-advanced.md 7.1 KB
- references/data-masking-audit.md 7.7 KB
- references/data-masking-compliance.md 6.4 KB
- references/data-masking-fundamentals.md 5.7 KB
- references/data-masking-performance.md 7.3 KB
- references/data-masking-techniques.md 5.3 KB
- references/encryption-strategies.md 1.6 KB
- references/masking-patterns.md 1.7 KB
- references/pii-detection.md 5.1 KB
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.
- 8d ago First seen · 561 lines · 112 tokens per session scan A ada555f55a48
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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