Implement Exa reference architecture for search pipelines, RAG, and content discovery. Use when designing new Exa integrations, reviewing project structure, or establishing architecture standards for neural search applications. Trigger with phrases like "exa architecture", "exa project structure", "exa RAG pipeline", "exa reference design", "exa search pipeline".
80
77%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/exa-pack/skills/exa-reference-architecture/SKILL.mdSecurity
2 findings — 2 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: 1.00). The skill explicitly calls exa.searchAndContents and exa.findSimilarAndContents to fetch text/highlights/summaries from public sites (see ragSearch where results.results[i].text and highlights are injected into LLM context, and SEARCH_PROFILES listing github.com, stackoverflow.com, arxiv.org), so untrusted third-party content is ingested and can materially influence agent behavior.
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: 1.00). The skill calls the external Exa API (api.exa.ai) at runtime (via exa.searchAndContents / exa.findSimilarAndContents) and injects the fetched content directly into LLM prompts/context, so the external content can control agent instructions.
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