Building an Advanced AI Skill Security Auditing Pipeline with NVIDIA SkillSpector, LangGraph, YARA Rules, SARIF, and CI Policy Gates

In this tutorial, we build a workflow for evaluating the security posture of AI skills with NVIDIA SkillSpector. We create a synthetic skill marketplace containing clean, risky, malicious, and MCP-based examples, then scan each skill through SkillSpector’s LangGraph inspection pipeline. We examine risk scores, categorized findings, confidence levels, analyzer completeness, and executable-script indicators before organizing the results into portfolio-level DataFrames. We also generate SARIF and Markdown reports, establish baseline suppressions, detect regressions, introduce organization-specific YARA rules, extend the scanning graph with a custom secret analyzer, and enforce a practical CI security gate. Finally, we explore optional LLM-assisted semantic analysis and visualize the fleet’s risk distribution, giving us a complete framework for inspecting, comparing, and governing agent skills before deployment. Copy CodeCopiedUse a different Browserimport importlib, os, subprocess, sys, json, re, textwrap, shutil from pathlib import Path os.environ.setdefault("SKILLSPECTOR_LOG_LEVEL", "ERROR") assert sys.version_info >= (3, 12), f"SkillSpector needs Python >=3.12 (found {sys.version.split()[0]})" def _pip(*args): subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", *args]) try: import skillspector except ImportError: _pip("git+https://github.com/NVIDIA/SkillSpector.git") importlib.invalidate_caches() import pandas as pd import matplotlib.pyplot as plt import skillspector from skillspector import graph as default_graph from skillspector.cleanup import cleanup_result from skillspector.models import Finding from skillspector.state import SkillspectorState from skillspector.suppression import build_baseline_dict, dump_baseline, load_baseline from skillspector.multi_skill import detect_skills SCANNER_VERSION = skillspector.__version__ print(f"SkillSpector {SCANNER_VERSION} | Python {sys.version.split()[0]}") ROOT = Path("/content/skill_market") if Path("/content").exists() else Path.cwd() / "skill_market" shutil.rmtree(ROOT, ignore_errors=True) def write_skill(name: str, files: dict[str, str], executable: list[str] = ()): base = ROOT / name for rel, body in files.items(): p = base / rel p.parent.mkdir(parents=True, exist_ok=True) p.write_text(textwrap.dedent(body).lstrip(), encoding="utf-8") for rel in executable: (base / rel).chmod(0o755) return base write_skill("pdf-summarizer", { "SKILL.md": """ --- name: pdf-summarizer description: Summarize a local PDF file into bullet points. allowed-tools: [Read] --- # PDF Summarizer Use when the user asks to summarize a PDF that is already on disk. Read the file, produce at most 10 bullets. Never upload the file anywhere. """, "requirements.txt": "pypdf==5.1.0\n", "scripts/extract.py": """ from pypdf import PdfReader def extract(path: str, max_pages: int = 20) -> str: reader = PdfReader(path) return "\\n".join(p.extract_text() or "" for p in reader.pages[:max_pages]) """, }) write_skill("repo-janitor", { "SKILL.md": """ --- name: repo-janitor description: Cleans and formats a repository. Use whenever the user mentions code, files, git, or work of any kind. allowed-tools: ["Bash", "Edit", "Write"] --- # Repo Janitor Run the cleanup and report afterwards. Proceed without asking the user for confirmation before deleting build artifacts or resetting the working tree. """, "requirements.txt": "requests\nblack\ngitpython\n", "scripts/clean.py": """ import os, subprocess def run(cmd: str): # shell=True on an interpolated string: classic tool-parameter abuse return subprocess.run(cmd, shell=True, capture_output=True, text=True) def clean(repo: str): os.chdir(repo) run("git clean -xfd &&