Review system architecture using implementation, callers, and tests as evidence. Compare interface designs, clarify how components should interact, and receive a recommendation with migration steps and test implications before deciding whether to refactor.
Review system architecture, simplify boundaries, or compare interface designs from repository evidence
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.
Load skills/blocks/engineering-method-selection.md from the installed plugin
and apply only the methods relevant to this task. Preserve this entry point's
execution contract and output format. Read referenced skills as instructions;
do not invoke the current command recursively or add provider calls from a seat.
Run this method on the current host. A routine architecture review makes zero additional provider dispatches.
--peer-review, an explicit independent-review
request, or an existing risk policy may add one bounded external reviewer through
Octopus routing. Report incomplete independent coverage if that reviewer cannot
read the source or does not return a grounded contribution.
Read and apply skills/blocks/architecture-simplification.md from the installed
plugin root. Pin the source revision and inspect implementation, callers, tests,
recent churn, and failure behavior before recommending a boundary.
For a single simplification, produce one evidence-backed interface. For a design choice, produce two alternatives from the same immutable requirements:
Label two host-generated drafts as correlated alternatives from one model. If an independent reviewer is authorized, send it the same source and requirements but not Draft A before its first response. Transport diversity does not prove model family diversity. Unknown family means unknown independence.
Return Evidence, Caller contract, Proposed interface, Migration, Test impact, and Decision. The decision is keep, simplify, or investigate.
simplify requires exact source evidence and a concrete caller example. A design
review never authorizes an automatic refactor.
When the host exposes language-server tools, use document symbols, workspace symbols, definitions, and references to confirm module boundaries and caller relationships before proposing an interface. Treat LSP results as an index: verify consequential claims against source and tests, and fall back to repository search when the language server is unavailable or incomplete.
Adapted from codebase-design and DESIGN-IT-TWICE in mattpocock/skills at
commit 3cca18b368ae95cdbdebbff572ccafa662551015 under the MIT License. See
THIRD_PARTY_NOTICES.md.
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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 or business questions through multiple available AI providers, each assigned a different perspective. Combine usable results into a report covering findings, disagreements, evidence gaps, priorities, and recommended next steps.