{"slug": "i-built-a-pdf-report-generator-for-nvidia-skillspector-because-ai-skills-need", "title": "I Built a PDF Report Generator for NVIDIA SkillSpector Because AI Skills Need Better Security Reviews", "summary": "A developer built SkillSpector Report, an open-source Python CLI that converts NVIDIA SkillSpector security scan output into PDF reports for easier sharing and review. The tool addresses the need for structured, readable reports that can be attached to tickets or sent to managers, complementing the terminal and JSON output of the original scanner.", "body_md": "A few weeks ago, I started looking more seriously at AI “skills.”\n\nNot skills in the human sense.\n\nI mean the installable skill packages used by AI coding agents and assistants, the kind of thing you might add to Claude Code, Codex-style workflows, Gemini CLI workflows, or other local agent setups.\n\nAt first, they look harmless.\n\nA `SKILL.md`\n\nfile.\n\nMaybe a helper script.\n\nMaybe a few references.\n\nMaybe some instructions telling the assistant how to behave.\n\nBut the more I looked at them, the more they started to feel less like “just prompts” and more like small software packages.\n\nAnd software packages need security review.\n\nThe risk in an AI skill is not always obvious.\n\nIt might not look like malware.\n\nIt might not have a scary filename.\n\nIt might not even contain a traditional executable payload.\n\nThe risk can hide in places that are easy to overlook:\n\nTraditional security review often asks:\n\nWhat code will run?\n\nAI skill review also needs to ask:\n\nWhat behavior is this skill trying to influence?\n\nThose are related, but they are not the same question.\n\nOWASP already lists risks such as prompt injection, sensitive information disclosure, and supply-chain vulnerabilities in its Top 10 for LLM applications.\n\nNVIDIA’s SkillSpector project also highlights why this matters. Its README cites research showing that **26.1% of skills contained vulnerabilities** and **5.2% showed likely malicious intent**.\n\nThat number made me stop and think.\n\nIf AI skills are easy to install, easy to share, and sometimes risky, then “just install it and see what happens” is not a review process.\n\nNVIDIA SkillSpector is a security scanner for AI agent skills.\n\nIt helps answer a simple but important question:\n\nIs this skill safe to install?\n\nIt can scan folders, files, zip archives, and repositories. It looks for risky patterns before installation, including suspicious instructions, hidden behavior, risky scripts, and other security concerns.\n\nThat is exactly the kind of tool this ecosystem needs.\n\nBut when I started using it, I noticed a practical gap.\n\nThe scan result was useful, but I wanted something easier to share.\n\nNot everyone wants terminal output.\n\nNot everyone wants raw JSON.\n\nNot every review record should be a screenshot from a shell window.\n\nSometimes you need a report.\n\nSomething structured.\n\nSomething readable.\n\nSomething you can attach to a ticket, send to a reviewer, or keep as review evidence.\n\nSo I built one.\n\nI built **SkillSpector Report**, a small open source Python CLI that turns NVIDIA SkillSpector scan output into a local PDF report.\n\nProject: [SkillSpector Report on GitHub](https://github.com/mbahubaishi/skillspector-report)\n\nIt does not replace SkillSpector.\n\nIt does not perform independent security analysis.\n\nIt simply runs the installed local `skillspector`\n\nCLI, reads SkillSpector JSON output, and turns the result into a polished PDF report.\n\nThe generated report includes:\n\nThe point is not to make scanner output “pretty.”\n\nThe point is to make scanner output easier to review, explain, share, and archive.\n\nDevelopers often prefer JSON, Markdown, or terminal output.\n\nI understand that.\n\nBut security review is not always only for developers.\n\nSometimes the result needs to go to a manager.\n\nSometimes it needs to be attached to a governance ticket.\n\nSometimes it needs to be reviewed by someone who understands risk but does not want to inspect raw JSON.\n\nSometimes you just want a clean record of what was scanned and what was found.\n\nThat is where a PDF report is useful.\n\nA PDF does not make a scanner more accurate.\n\nBut it can make the result easier to understand and easier to act on.\n\nAnd in security, communication matters.\n\nA finding that nobody understands is a finding that may not get fixed.\n\nI wanted the tool to handle two very different outcomes clearly.\n\nA clean report does **not** mean “guaranteed safe.”\n\nIt means SkillSpector did not report significant issues in that scan.\n\nThe report still includes the full structure:\n\nThat matters because even a low-risk result should still be reviewed before trusting a skill.\n\nSecurity scanners can miss things. A clean result is useful evidence, not a certificate of safety.\n\nThis is where the report becomes more valuable.\n\nWhen SkillSpector finds issues, the PDF includes a detailed findings table showing:\n\nThat makes it easier to answer the questions reviewers actually care about:\n\nWhat was found?\n\nWhere was it found?\n\nHow serious is it?\n\nHow confident is the scanner?\n\nWhat should be reviewed next?\n\nThat is the difference between raw output and evidence that is easier to review.\n\nI wanted the reporting workflow to stay local.\n\nSkillSpector Report runs the installed local `skillspector`\n\ncommand and generates the PDF locally.\n\nThe report generator itself does not upload scan inputs or generated reports.\n\nBy default, it currently runs SkillSpector in static-only mode using `--no-llm`\n\n.\n\nThat means LLM semantic analysis is not enabled by default.\n\nThis was intentional. For a first review, I prefer a conservative local workflow before adding anything that may involve an external model provider.\n\nThere is one important nuance. Depending on the scanned content, dependencies, SkillSpector configuration, and network availability, SkillSpector itself may still perform package vulnerability lookups such as OSV.dev queries.\n\nSo the honest version is:\n\nThe report generator is local first, but users should still understand what the underlying scanner may do.\n\nAfter installing NVIDIA SkillSpector separately, SkillSpector Report can generate a local PDF from a skill folder, zip file, package, or repository path.\n\nThe full setup instructions and examples are in the GitHub README:\n\n[https://github.com/mbahubaishi/skillspector-report](https://github.com/mbahubaishi/skillspector-report)\n\nThe repository also includes example PDF reports for both clean and malicious scan outcomes.\n\nI think this part matters.\n\nSkillSpector Report is not a magic safety button.\n\nIt does not guarantee that a skill is safe.\n\nIt does not replace manual review.\n\nIt does not replace NVIDIA SkillSpector.\n\nIt does not perform independent security analysis.\n\nIt does not certify compliance.\n\nIt does not prove that malicious behavior is absent.\n\nIt is a reporting layer.\n\nA useful one, I hope, but still a reporting layer.\n\nSecurity scanners can produce false positives and false negatives. A low-risk report should not be treated as a guarantee, and a high-risk report should be reviewed carefully before using the scanned item.\n\nAI skills and agent tools are becoming more like software packages.\n\nThey can influence behavior.\n\nThey can include scripts.\n\nThey can reference external resources.\n\nThey can shape what an assistant does next.\n\nThat means teams will need better ways to review them.\n\nNot just scan them.\n\nReview them.\n\nExplain them.\n\nArchive the decision.\n\nShare the evidence.\n\nHelp non-technical people understand the risk.\n\nThat is the gap I wanted to explore with this project.\n\nNVIDIA SkillSpector does the security scanning.\n\nSkillSpector Report tries to make the result easier to read, share, and keep.\n\nSome ideas I’m considering:\n\nThe project is still early, but the direction is clear:\n\nAI skill security needs to become easier to review, not just easier to scan.\n\nThe project is open source under the MIT License:\n\n[https://github.com/mbahubaishi/skillspector-report](https://github.com/mbahubaishi/skillspector-report)\n\nFeedback, issues, and suggestions are welcome.", "url": "https://wpnews.pro/news/i-built-a-pdf-report-generator-for-nvidia-skillspector-because-ai-skills-need", "canonical_source": "https://dev.to/mbahubaishi/i-built-a-pdf-report-generator-for-nvidia-skillspector-because-ai-skills-need-better-security-8c9", "published_at": "2026-07-08 20:33:47+00:00", "updated_at": "2026-07-08 21:11:24.668772+00:00", "lang": "en", "topics": ["ai-safety", "developer-tools", "ai-agents"], "entities": ["NVIDIA", "SkillSpector", "SkillSpector Report", "OWASP", "Claude Code", "Gemini CLI"], "alternates": {"html": "https://wpnews.pro/news/i-built-a-pdf-report-generator-for-nvidia-skillspector-because-ai-skills-need", "markdown": "https://wpnews.pro/news/i-built-a-pdf-report-generator-for-nvidia-skillspector-because-ai-skills-need.md", "text": "https://wpnews.pro/news/i-built-a-pdf-report-generator-for-nvidia-skillspector-because-ai-skills-need.txt", "jsonld": "https://wpnews.pro/news/i-built-a-pdf-report-generator-for-nvidia-skillspector-because-ai-skills-need.jsonld"}}