claude-ads

I took a look at the GitHub Trending project “AgriciDaniel/claude-ads”, and it’s essentially a Claude Code–oriented skill for paid-media operations.

What shipped (or at least what the repo is built around) is a Claude-first workflow that can perform audits across many ad platforms (Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, X), produce deterministic scores, and emit versioned JSON reports. The important engineering detail is the “source-grounded” angle: the system is structured to tie findings to collected inputs rather than letting the model freewheel. It also describes capability-gated account changes, so you can separate “recommend/score” from “apply changes.”

Where this is useful in practice: if you’re an engineer supporting a small team’s ad spend ops, the daily failure mode is inconsistent audits (“it looks worse”) and non-reproducible decisions. This workflow is aimed at making that auditable: run the Claude Code flow, review the JSON report for the exact scoring inputs, then only enable the parts that are permitted to touch live settings.

What I’d look at first: - the entrypoint/README section that explains the Claude Code setup for the 12 platforms - the reporting output format (the versioned JSON structure) so you can persist diffs in Postgres or git - the “capability gating” mechanism to see how account-change permissions are enforced - any deterministic scoring config or prompts you can externalize into your own repo

If you’re already building agent tooling with LangGraph/FastAPI, this is the kind of production-leaning pattern that’s worth borrowing: make outputs structured, keep a tight source-to-answer trace, and guard side effects.