Generate conference presentation slides (beamer LaTeX → PDF + editable PPTX) from a compiled paper, with speaker notes and full talk script. Use when user says "做PPT", "做幻灯片", "make slides", "conference talk", "presentation slides", "生成slides", "写演讲稿", or wants beamer slides for a conference talk.
71
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Does it follow best practices?
Impact
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No eval scenarios have been run
Advisory
Suggest reviewing before use
Security
3 findings — 3 medium severity. This skill can be installed but you should review these findings before use.
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Third-party content exposure detected (high risk: 0.75). At runtime, the skill reads the outsider-authored paper content from `paper/sections/*.tex` (and associated `paper/figures/*`) to extract slide text and speaker notes, which then gets fed into the agent/LLM context during slide drafting and script generation.
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
Potentially malicious external URL detected (high risk: 0.80). The skill will (when the user supplies --style-ref) invoke the local helper via python3 "$STYLE_HELPER" --source "<source>" where <source> may be an http(s) URL, and the fetched style_profile.md from that remote http(s) source is used at runtime to steer slide structure (i.e., it directly controls generation prompts), so remote http(s) sources passed to --style-ref are a runtime external dependency.
Detected hidden or invisible Unicode characters (Format/Cf or Control/Cc categories) in the component’s content. These characters are invisible when rendered but are still processed by AI models, and attackers use them to smuggle instructions past human review — for example, zero-width spaces, bidirectional overrides, invisible formatters, or Unicode Tag characters (U+E0000–U+E007F) that encode an entire hidden message. Severity escalates to high when three or more distinct hidden character types are present, or when a hidden tag-encoded message is successfully decoded, as these strongly indicate intentional obfuscation.
Hidden Unicode characters detected (1 type(s) found)
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