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 h4vzz/awesome-ai-agent-skills --skill static-application-security-testinggit clone --depth 1 https://github.com/h4vzz/awesome-ai-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/h4vzz/awesome-ai-agent-skills/static-application-security-testing)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/static-application-security-testing"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/static-application-security-testing/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/h4vzz/awesome-ai-agent-skills/static-application-security-testing"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/static-application-security-testing.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 | $0.00027 | $0.02090 |
| Opus 5.5 | $0.00011 | $0.00836 |
| Sonnet 5.5 | $0.00005 | $0.00418 |
| Haiku 4.5 | $0.00003 | $0.00209 |
Grade A, and why
static-application-security-testing 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.
This is a copy
95% identical to static-application-security-testing — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Static Application Security Testing
This skill enables the agent to perform Static Application Security Testing (SAST) on source code repositories to detect security vulnerabilities without executing the application. The agent selects appropriate analysis tools based on the project's language, runs scans with relevant rule sets, triages findings to separate true positives from false positives, and integrates results into CI/CD pipelines. SAST catches issues such as SQL injection, cross-site scripting, hardcoded secrets, insecure deserialization, and cryptographic misuse early in the development lifecycle.
Workflow
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Detect Languages and Frameworks — Analyze the repository to determine primary languages (Python, JavaScript, Java, Go, C#, etc.) and frameworks in use. This determines which SAST tools and rule sets are applicable. Check for existing tool configurations like
.semgrep.yml,codeqlquery packs, or.banditconfig files. -
Select and Configure SAST Tools — Choose the appropriate tools for the detected stack. Use Semgrep for multi-language pattern matching, CodeQL for deep semantic analysis, Bandit for Python-specific checks, and ESLint security plugins for JavaScript/TypeScript. Load built-in security rule sets and any project-specific custom rules.
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Execute Static Analysis — Run the selected tools against the codebase. Capture all findings including the vulnerability type, affected file and line number, severity level, CWE identifier, and a description of the issue. For large codebases, parallelize scans across multiple tools simultaneously.
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Triage and Deduplicate Findings — Merge results from multiple tools, remove duplicate detections of the same issue, and classify findings as true positive, false positive, or needs-review. Use contextual analysis such as checking whether a flagged SQL string actually reaches a database driver to reduce noise.
-
Generate Report with Fix Suggestions — Produce a structured findings report grouped by severity and category. Include the vulnerable code snippet, an explanation of the risk, a suggested fix with corrected code, and references to relevant CWE entries and OWASP categories.
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 · 164 lines · 27 tokens per session scan A 2535cce10c57
static-application-security-testing is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (36 stars, last pushed 22d ago), licensed MIT. It adds 27 tokens to every session and 2,090 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to static-application-security-testing, differing in 9 lines, and is treated as a copy.
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