Update to No BS PPC by Fiera

{
  "description": "Review a Google Ads change history export to find the changes that explain a performance shift and the risky ones, such as target changes inside the calibration window, large budget swings, bid strategy changes, auto-applied recommendations and edits to conversion goals, networks or locations. Use when the user shares change history or asks what changed, who changed it, or whether a change caused a drop.",
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  "name": "change-history-review",
  "skill_md_contents": "---\nname: change-history-review\ndescription: Review a Google Ads change history export to find the changes that explain a performance shift and the risky ones, such as target changes inside the calibration window, large budget swings, bid strategy changes, auto-applied recommendations and edits to conversion goals, networks or locations. Use when the user shares change history or asks what changed, who changed it, or whether a change caused a drop.\n---\n\n# Change history review\n\nThe user's explicit instructions take priority over this guidance. Reply in the user's language.\n\n## Inputs\n\nChange history export (Tools, Change history, Download) for the period around the performance change, ideally 4 weeks before it to 2 weeks after.\n\n## Steps\n\n1. Run `python scripts/ppc.py inspect --data <file>` to confirm the date range, then `python scripts/ppc.py changes --data <file> [--big-change 0.2] [--learning-days 14] --out changes`.\n2. If the user has not shared performance data, ask for the campaign report by Day for the same period (see `performance-triage`). Without it, report the risky changes but do not name a cause.\n3. Read `net_changes` (where a setting ended after several steps), then `flags` from high to medium:\n   - Targets changed again before the bidder calibrated: results in between cannot be read.\n   - Steps above 20% in targets or budgets.\n   - Bid strategy changes.\n   - Auto-applied recommendations.\n   - Conversion goal, network and location changes.\n4. Put the flagged changes on the performance timeline. A change is a candidate cause only if it precedes the shift and fits its direction (a lower tCPA cuts volume, a broader match type lowers conversion rate).\n5. Look for typos in values: a target or budget off by a factor of 10 or 100 for a short time is common and costly.\n\n## Output\n\n- The timeline of material changes (targets, budgets, strategy, status), one line each.\n- Flags with severity and why they matter.\n- Which change most likely explains the performance shift, with the evidence, or \"none of these\" if nothing lines up.\n- Process advice when the pattern is frequent edits: batch changes, wait 1 to 2 conversion cycles, keep a change log.\n\n## Rules\n\n- Change history can miss some changes (some shared library edits). Absence is not proof.\n- Do not blame a person. Describe the change and its effect.\n\n## Scripts and references\n\n- `scripts/ppc.py changes`; `references/principles.md`, `references/bidding-budgets.md`.\n\n## Running the script\n\n- Run it with the Python that has pandas installed (`python` in ChatGPT's code environment). Write outputs to the working folder (for example `--out /mnt/data/result`) and share the files with the user.\n- Put ad text and any value with `$` in single quotes, or pass a CSV with `--data`: the shell expands `\"$12\"`.\n"
}