Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/UnitOneAI/SecuritySkillsnpx agentmods add skills/unitoneai/securityskills/sast-configWrote 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/unitoneai/securityskills/sast-config)<a href="https://agentmods.dev/skills/unitoneai/securityskills/sast-config"><img src="https://agentmods.dev/badge/skills/unitoneai/securityskills/sast-config.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 | $0.00078 | $0.05451 |
| Opus 5 | $0.00039 | $0.02726 |
| Sonnet 5 | $0.00016 | $0.01090 |
| Haiku 4.5 | $0.00008 | $0.00545 |
Grade A, and why
sast-config 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 — 581 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SAST Tool Configuration and Tuning
A structured, repeatable process for reviewing and tuning Static Application Security Testing (SAST) tool configurations against OWASP ASVS 4.0.3 verification requirements and the CWE Top 25 Most Dangerous Software Weaknesses. This skill covers Semgrep rule authoring, CodeQL query patterns, severity tuning, false positive management, custom rule development, and CI integration. All findings map to ASVS controls and CWE identifiers.
When to Use
If a target is provided via arguments, focus the review on: $ARGUMENTS
- Initial SAST deployment to establish baseline rule configuration.
- Periodic SAST tuning reviews to reduce false positive rates.
- Custom rule development for organization-specific vulnerability patterns.
- CI/CD integration review for SAST gate enforcement.
- Post-incident rule gap analysis (a vulnerability was missed -- why?).
- ASVS compliance mapping to verify SAST coverage against verification requirements.
Context
SAST tools are only as effective as their configuration. Default rule sets produce high false positive rates that erode developer trust, while overly aggressive tuning creates dangerous blind spots. OWASP ASVS 4.0.3 provides 286 verification requirements across 14 chapters -- a subset of these are automatable via SAST. The CWE Top 25 (2024 edition) identifies the most prevalent and impactful weakness types. Effective SAST tuning maps rules to these frameworks, tunes severity to organizational risk context, and integrates into CI with clear pass/fail criteria that developers can act on.
Process
Step 1: Discovery -- Locate SAST Configurations
Use Glob and Grep to locate SAST tool configurations, custom rules, and CI integration.
Patterns to search:
# Semgrep
**/.semgrep.yml
**/.semgrep.yaml
**/.semgrep/
**/semgrep*
**/.semgrepignore
# CodeQL
**/.github/codeql/
**/codeql-config.yml
**/*.ql
**/*.qll
**/qlpack.yml
**/.github/workflows/*codeql*
# General SAST
**/sonar-project.properties
**/.sonarcloud.properties
**/checkmarx*
**/fortify*
**/.bandit
**/bandit.yaml
**/.flake8
**/pylintrc
**/.eslintrc*
# CI integration
**/.github/workflows/*.yml
**/.gitlab-ci.yml
**/Jenkinsfile*
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 · 581 lines · 78 tokens per session scan A 0babc2ef75f4
sast-config is a skill published in the GitHub repository UnitOneAI/SecuritySkills (60 stars, last pushed 2mo ago), licensed MIT. It adds 78 tokens to every session and 5,451 once invoked, about $0.0004 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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