Choose how to handle repository initialization, code review, and security review requests. Start with Claude’s built-in commands for ordinary tasks, and route requests for multiple opinions, adversarial analysis, or specific providers to Octopus.
Use when choosing native or multi-LLM handling for init, review, or security requests
Review the instructions the agent will receive before you install.
Host: Codex CLI — This skill was designed for Claude Code and adapted for Codex. Cross-reference commands use installed skill names in Codex rather than
/octo:*slash commands. Use the active Codex shell and subagent tools. Do not claim a provider, model, or host subagent is available until the current session exposes it. For host tool equivalents, seeskills/blocks/codex-host-adapter.md.
Use this skill when the user asks for repository initialization, code review, or security review and it is not yet clear whether Claude-native behavior is sufficient or whether Octopus escalation is warranted.
Claude-native first:
/init/review/security-reviewOctopus for escalation:
/octo:review/octo:security/octo:debate/octo:multiPrefer Claude-native behavior when all of the following are true:
Examples:
Escalate when the user asks for or clearly benefits from:
Examples:
/octo:review or /octo:security, treat that as an escalation request.Use wording like:
Claude-native first, Octopus for escalation. Use Claude-native
/reviewor/security-reviewfor ordinary requests. Use Octopus when you want multiple model opinions, adversarial review, or stricter multi-LLM workflows.
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Interpret the saved results of a completed AI council review. Count verdicts, check whether enough participants responded and whether models are independent, surface substantive dissent, and explain what action the outcome supports.
Turn a technical choice into a structured debate between different AI model families. Assign explicit positions, launch the discussion, and summarize agreement, disagreement, and a recommendation supported by the strongest argument that survives the debate.
Clarify a feature’s requirements with multiple AI providers, combining technical constraints with user and business needs. Produce a scoped definition covering priorities, edge cases, exclusions, and a requirements checklist before implementation begins.
Review code or documents with available AI providers before delivery. Combine findings into a scored validation report that checks prior requirements and decisions, separates critical issues from warnings, and recommends next steps.
Develop complex features or business documents by gathering approaches from available AI providers. Combine their perspectives with prior project decisions and quality checks to produce a proposed plan, then proceed with implementation after user confirmation.
Research technical, market, or academic topics through multiple available AI providers working in parallel. Combine their findings into a report that preserves disagreements, identifies gaps, attributes sources, and recommends next steps with priorities.