Search across company knowledge bases (Confluence, Jira, internal docs) to find and explain internal concepts, processes, and technical details. When an agent needs to: (1) Find or search for information about systems, terminology, processes, deployment, authentication, infrastructure, architecture, or technical concepts, (2) Search internal documentation, knowledge base, company docs, or our docs, (3) Explain what something is, how it works, or look up information, or (4) Synthesize information from multiple sources. Searches in parallel and provides cited answers.
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Low
Low-risk findings worth noting
Low
Low-risk findings.
2 low severity findings. Worth noting, but not necessarily harmful.
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.
The required workflow explicitly searches and then fetches full Confluence pages (`search` → `getConfluencePage` returning Markdown) and Jira issue details (`search` → `getJiraIssue` including description/comments), which are outsider-authored free text in the sense of third-party team contributions; these contents become LLM-readable context for synthesis.
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.
The skill explicitly fetches Confluence and Jira content at runtime (e.g., https://yoursite.atlassian.net/wiki/spaces/SPACE/pages/123456 and https://yoursite.atlassian.net/browse/PROJ-123) via getConfluencePage/getJiraIssue to inject external documents into the model context, so external URLs directly influence agent prompts and outputs.
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