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 RedHatProductSecurity/prodsec-skills --skill secrets-detection-patternsgit clone --depth 1 https://github.com/RedHatProductSecurity/prodsec-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/redhatproductsecurity/prodsec-skills/secrets-detection-patterns)<a href="https://agentmods.dev/skills/redhatproductsecurity/prodsec-skills/secrets-detection-patterns"><img src="https://agentmods.dev/badge/skills/redhatproductsecurity/prodsec-skills/secrets-detection-patterns.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Privilege Escalation · line 53 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00057 | $0.01404 |
| Opus 5 | $0.00028 | $0.00702 |
| Sonnet 5 | $0.00011 | $0.00281 |
| Haiku 4.5 | $0.00006 | $0.00140 |
Grade A, and why
secrets-detection-patterns 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Secrets Detection Patterns
Overview
Pattern-based secrets detection for source code. This skill provides regex patterns, false positive filtering criteria, and remediation guidance for detecting hardcoded secrets. It is self-contained — no external tools (trufflehog, gitleaks) are required, though they are recommended as complementary tooling.
Zero Tolerance Policy
Any confirmed secret in code results in a BLOCKED verdict. There is no passing threshold — secrets in code are a critical finding. A secret is either present or it is not.
Redaction Rule
Reports must NEVER include actual secret values. Report secret type, file path, and line number only. Pattern matches are redacted to show type and location: e.g., "AWS Access Key at src/config.js:42".
Scan Scope Options
- Staged — scan git staged files only (pre-commit gate)
- All — scan entire working directory
- History — scan git commit history (post-incident review)
Detection Patterns
Pattern 1: AWS Access Keys
Format: AKIA followed by 16 alphanumeric characters.
AKIA[0-9A-Z]{16}
Pattern 2: AWS Secret Access Keys
40-character base64-like strings after a known label.
(aws_secret_access_key|aws_secret_key)\s*[=:]\s*[A-Za-z0-9/+=]{40}
Pattern 3: GitHub Personal Access Tokens
Prefixes: ghp_, gho_, ghu_, ghs_, ghr_.
(ghp_|gho_|ghu_|ghs_|ghr_)[A-Za-z0-9_]{36}
Pattern 4: Private Key Material
RSA, EC, DSA, and OpenSSH private key headers.
-----BEGIN (RSA |EC |DSA |OPENSSH |PRIVATE )PRIVATE KEY-----
Pattern 5: Generic Passwords (Labeled)
High-confidence labeled password assignments with values >= 8 characters.
(password|passwd|pwd|secret|api_key|apikey|api_secret|client_secret|auth_token|access_token)\s*[=:]\s*['"][^'"]{8,}['"]
Pattern 6: Database Connection Strings
Connection strings with embedded credentials.
(mysql|postgresql|postgres|mongodb|redis|amqp|jdbc)://[^@\s]+:[^@\s]+@
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 · 166 lines · 57 tokens per session scan A 614c56390fe3
secrets-detection-patterns is a skill published in the GitHub repository RedHatProductSecurity/prodsec-skills (52 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 57 tokens to every session and 1,404 once invoked, about $0.0003 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 2026-08-30.
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