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 codebygarv/Ai-skills --skill data-anonymizer-specifiergit clone --depth 1 https://github.com/codebygarv/Ai-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/codebygarv/ai-skills/data-anonymizer-specifier)<a href="https://agentmods.dev/skills/codebygarv/ai-skills/data-anonymizer-specifier"><img src="https://agentmods.dev/badge/skills/codebygarv/ai-skills/data-anonymizer-specifier.svg" alt="Measured on agentmods" 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 | $0.00029 | $0.00385 |
| Opus 5 | $0.00015 | $0.00192 |
| Sonnet 5 | $0.00006 | $0.00077 |
| Haiku 4.5 | $0.00003 | $0.00038 |
Grade A, and why
data-anonymizer-specifier 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 5d 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.
What it actually says
Purpose
Define automated data anonymization, pseudonymization, and redaction rules to prevent Personally Identifiable Information (PII), credit card numbers, passwords, and API keys from leaking into application logs, error monitoring tools, and staging databases.
When to Use
- Sanitizing production database snapshots for local development or QA environments.
- Configuring log scrubber rules (Datadog, Pino, Winston, Logstash).
- Complying with GDPR, HIPAA, or PCI-DSS redaction mandates.
What to Analyze
- PII Cataloging: Email addresses, phone numbers, SSNs, IP addresses, full names, physical addresses.
- Secret Identification: Passwords, bearer tokens, JWTs, API keys, credit card PANs / CVVs.
- Transformation Technique:
- Masking (e.g.
4111-XXXX-XXXX-1234). - Hashing with Salt (pseudonymization preserving relational consistency across tables).
- Synthetic Replacement (replacing with realistic Faker data).
- Zeroing / Nullification.
- Masking (e.g.
- Log Scrubber Interceptor: Middleware sanitizing HTTP request headers and bodies before logging.
Output Format
- Data Classification Matrix: Field Name, PII Category, Masking Method.
- SQL / Pipeline Anonymization Script: Anonymization script for staging database copies.
- Logger Masking Middleware: Regex/path-based scrubber for server logs.
Avoid
- Reversible weak encryption instead of one-way salted hashes for identifiers.
- Leaving sensitive URL query parameters (e.g.
?token=xyz) un-sanitized in access logs.
What ships with it
2 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.
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.
- 5d ago First seen · 37 lines · 29 tokens per session scan A 8e2370b8c974
data-anonymizer-specifier is a skill published in the GitHub repository codebygarv/Ai-skills (25 stars, last pushed 19d ago), licensed MIT. It adds 29 tokens to every session and 385 once invoked, about $0.0001 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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