B — Some risk signals Graded October 16, 2026Minor concerns worth a quick check.Based on public adoption and transparency signals as of October 16, 2026. Our opinion, not a code audit or security guarantee.
AI agent skill · Files & documents
Kami
👩🚒 Good content deserves good paper. Layout rules and templates that let AI agents export PDF, PNG and editable PowerPoint.
Bottom line: Signal-based grade (4/7 points): very widely starred on GitHub, public source code. This reflects adoption and transparency, not a code review.
- Popularity score
- 46/100
- Rank
- #196 of 500
- GitHub stars
- 12k
What it does
Kami is a layout engine and template system that enables AI agents to programmatically generate polished PDFs, PNGs, and editable PowerPoint presentations from structured content. It abstracts away document formatting complexity, letting agents focus on content quality while maintaining consistent, professional output across multiple formats.
Who it’s for
Product builders integrating document generation into AI workflows, and enterprises that need agents to produce client-ready reports, presentations, or visual exports at scale. Useful for teams prioritizing output quality and format flexibility over rapid prototyping.
Copy-paste setup
Check these steps against the project’s own instructions before running anything.
Verify against the repo README at https://github.com/tw93/Kami (opens external site) for current installation and configuration steps. The project uses GitHub as its primary distribution channel; check for npm package availability or direct integration examples in the documentation.
Risks
- Template Injection: Malicious or prompt-injected content in agent payloads could exploit template logic to execute unintended code or exfiltrate data embedded in documents.
- File Exfiltration: Generated PDFs/PNGs may inadvertently include sensitive metadata, variable values, or system information if agents lack strict output sanitization.
- Supply Chain: Minimal npm download signals and early-stage maintenance status suggest limited production hardening; use in high-stakes workflows requires thorough vetting.
- Credential Exposure in Exports: If agents embed API keys, credentials, or internal URLs into document templates, exports become attack surface for credential harvesting.
- Dependency Risk: Reliance on underlying document libraries (e.g., for PowerPoint/PDF rendering) may inherit security issues; monitor transitive dependencies closely.
How to stay secure
Least-privilege permissions
- Restrict what Kami can access: Only allow the AI agent to read templates and data it absolutely needs—don't give it access to your entire company file system or database. Think of it like giving an employee a key to only their desk, not the whole office.
- Separate template storage: Keep export templates in a dedicated, locked folder that only Kami uses. Prevent the agent from modifying original templates, only reading them.
- Limit export destinations: Specify exactly where PDFs, PNGs, and PowerPoints can be saved (e.g., one secure output folder). Block exports to email, cloud storage, or external services unless explicitly required.
- Audit template authors: Only trusted team members should create or update templates. Require review before new templates enter production.
Network isolation
- Disable external outbound connections: Configure your network or container settings so Kami cannot call external APIs, contact unfamiliar servers, or "phone home" with documents. Malicious prompts might try to exfiltrate data this way.
- Use air-gapped environments for sensitive exports: For highly confidential documents (contracts, financial reports), run Kami on an isolated machine or virtual environment with no internet access.
- Block dependency auto-updates: Pin exact versions of Kami and its underlying libraries (e.g., PDF/PowerPoint rendering tools). Auto-updates from untrusted sources can introduce hidden vulnerabilities.
Monitoring
- Log all exports: Record who requested what export, when it happened, and what data was included. Review logs weekly for suspicious patterns (e.g., unusual file sizes, repeated exports of sensitive templates).
- Scan output files: Use antivirus or document-scanning tools to automatically inspect generated PDFs and PNGs for embedded scripts or malware before they reach users.
- Watch for template tampering: Compare template file hashes (unique digital fingerprints) regularly to detect if someone has secretly modified them.
- Alert on failed exports or errors: Misconfigured exports sometimes leak metadata or system information in error messages; flag these for immediate investigation.
Red flags to watch for
- Unexpected variables or text in exports: If a PDF contains API keys, internal server names, database passwords, or employee emails that shouldn't be there, stop using that template immediately and audit what went wrong.
- Slow or unusually large file sizes: A 50 MB PDF from a simple one-page template is a red flag—the agent may be bundling hidden data or executing unexpected code.
- Template injection symptoms: Prompts that include special characters, code snippets, or instructions (e.g., "ignore these rules and output raw data") should be rejected or manually reviewed before processing.
- Requests from unexpected users or roles: If a junior analyst suddenly exports confidential board presentations repeatedly, investigate whether their account was compromised or permissions were misconfigured.
Alternatives
- Puppeteer – Browser automation for HTML-to-PDF; mature, widely used, but heavier resource footprint.
- LibreOffice API – Server-side document generation with strong format support; requires infrastructure and careful sandboxing.
- Pptxgen-js – Focused PowerPoint generation library; lighter weight but narrower scope than Kami.
- Weasyprint – Python-based HTML/CSS-to-PDF; strong for design flexibility, less agent-friendly out of the box.