Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nospicyplease/amazon-ppc-advanced-skills --skill amazon-ads-performance-drop-diagnosisgit clone --depth 1 https://github.com/nospicyplease/amazon-ppc-advanced-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nospicyplease/amazon-ppc-advanced-skills/amazon-ads-performance-drop-diagnosis)<a href="https://agentmods.dev/skills/nospicyplease/amazon-ppc-advanced-skills/amazon-ads-performance-drop-diagnosis"><img src="https://agentmods.dev/badge/skills/nospicyplease/amazon-ppc-advanced-skills/amazon-ads-performance-drop-diagnosis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nospicyplease/amazon-ppc-advanced-skills/amazon-ads-performance-drop-diagnosis"><img src="https://agentmods.dev/badge/skills/nospicyplease/amazon-ppc-advanced-skills/amazon-ads-performance-drop-diagnosis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00102 | $0.04189 |
| Opus 5 | $0.00051 | $0.02094 |
| Sonnet 5 | $0.00020 | $0.00838 |
| Haiku 4.5 | $0.00010 | $0.00419 |
Grade A, and why
amazon-ads-performance-drop-diagnosis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Ads Performance Drop Diagnosis
Objective
Diagnose the cause of a decline. Do not produce a generic performance recap. Trace when the break started, quantify the impact, isolate the entities causing the loss, connect ads movement to BSR/rank movement, and recommend only the actions supported by evidence gates. Protect sales velocity and organic momentum; do not optimize only for lower ACOS or lower spend.
Public-Safe And Vendor-Neutral Guidance
This skill is intended for reusable public guidance. Do not include client-sensitive, account-sensitive, competitor-sensitive, or private examples in the skill, its references, or generated reusable templates. Use synthetic examples only when examples are necessary.
Do not depend on or name any specific third-party retail-data vendor. Refer generically to retail intelligence data, rank history, offer snapshot, cached retail data, or external retail data source.
Repeatable Drop Analysis Flow
Use this flow in order for every account, ASIN, campaign, marketplace, or product performance-drop diagnosis unless the user explicitly narrows the scope. If Rocketcart tools are available, use Rocketcart trusted data/API paths and canonical optimization endpoints. Screenshots and stale exports are validation inputs only and must not drive optimization recommendations.
Default Periods
- Freshness check: latest trusted reporting date; anchor recent analytics on T-1.
- Drop window: T-7 through T-1 unless the user supplies a dated incident window.
- Baseline window: T-14 through T-8 unless the user supplies a better matched baseline.
- Control-change audit: at least 14 days before the break through the drop window; expand to 30 days when changes are sparse, delayed, or disputed.
- Retail, rank, and competitor context: 30-90 days when dated history is available, plus current-state snapshots for offer/readiness checks.
For any explicit audit, print the exact baseline and drop dates used. Do not compare partial current-day data against completed historical days.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 206 lines · 102 tokens per session scan A 86c5ac6b21b1
amazon-ads-performance-drop-diagnosis is a skill published in the GitHub repository nospicyplease/amazon-ppc-advanced-skills (14 stars, last pushed 3mo ago), licensed MIT. It adds 102 tokens to every session and 4,189 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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