Use this skill when you see `/omo`. Multi-agent orchestration for "code analysis / bug investigation / fix planning / implementation". Choose the minimal agent set and order based on task type + risk; recipes below show common patterns.
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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.
At runtime this skill’s required workflow can invoke the `librarian` agent (SKILL.md:21-23, 36) which is explicitly designed to “lookup external library docs or OSS examples” and can read web/GitHub content, then passes those outputs in the agent “Context Pack” into the LLM context—i.e., outsider-authored free text from public web/GitHub is ingested indirectly.
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 Librarian agent specification instructs runtime cloning/fetching of arbitrary GitHub repositories and inclusion of their code as evidence (e.g., "gh repo clone owner/repo ${TMPDIR:-/tmp}/repo-name -- --depth 1" and "https://github.com/owner/repo/blob/<sha>/path/to/file#L10-L20"), which brings external code into the model context and can directly control prompts, so this is a runtime external dependency risk.
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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.