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ml-paper-writing

Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.

71

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Critical

Do not install without reviewing

The canonical home for this skill is ml-paper-writing in OpenLAIR/dr-claw

SKILL.md
Quality
Evals
Security

Security

1 critical severity finding. Installing this skill is not recommended: please review these findings carefully if you do intend to do so.

Critical

E005: Suspicious download URL detected in skill instructions.

What this means

Detected a suspicious URL in the skill instructions that could lead the agent to download and execute malicious scripts or binaries. This includes links to executables from untrusted sources, typosquatting of official packages, URL shorteners that obscure the destination, and personal file hosting services.

Why it was flagged

The skill instructs installation of a remote MCP via npx pointing to https://mcp.exa.ai/mcp (fetch-and-run of third‑party code), which is an unverified remote installer and therefore a high-risk download/execute vector.

Report incorrect finding

Low

Low-risk findings.

1 low severity finding. Worth noting, but not necessarily harmful.

Low

W011: Third-party content exposure detected (indirect prompt injection risk).

What this means

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.

Why it was flagged

Although the skill itself mostly uses bundled/local reference text (SKILL.md and references/*), its required runtime workflow explicitly performs web/API literature search (semantic scholar/CrossRef/arXiv/Exa MCP) and ingests returned paper metadata/abstracts as readable text into the agent context, enabling outsider-authored content and potential indirect prompt injection via fetched results.

Repository
synthetic-sciences/openscience
Audited
Security analysis
Snyk

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