One-shot, PER-CALL-SITE audit of a repo's EXISTING LLM calls — every place code invokes a model (an SDK/API call, a sub-agent dispatch, an agent frontmatter definition), decomposed by INTENT into the distinct jobs it does, never a grep hit on a model name and never one candidate per file. A skill pinned by one static `model:` key still gets its steps split by intent when severable under Claude Code's per-turn model binding. DELEGATES each call to `model-right-sizer-dryrun` (never re-implements its scoring) and merges results into ONE schema-conformant JSON blueprint committed at the TARGET REPO'S ROOT via a PR — never a markdown table. Distinct from `model-right-sizer-dryrun` (invoke directly for one hypothetical task) and `model-right-sizer-install` (the standing before/after mandate). Use when someone says "audit this repo's model calls", "right-size the models in <repo> per call site", "find every LLM call and right-size it", or "commit a model right-sizing blueprint for <repo>".
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 audits local repositories or clones remote GitHub repositories and processes their source code, which can contain outsider-authored free text ingested during the code review and model discovery phase.
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.