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>".
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Low-risk findings worth noting
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 clones and reads arbitrary target repositories from GitHub (`org/repo` slugs or full URLs), ingesting repository code, comments, docstrings, and commit messages into the agent session.
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