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-account-growth-operating-systemgit 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-account-growth-operating-system)<a href="https://agentmods.dev/skills/nospicyplease/amazon-ppc-advanced-skills/amazon-account-growth-operating-system"><img src="https://agentmods.dev/badge/skills/nospicyplease/amazon-ppc-advanced-skills/amazon-account-growth-operating-system/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-account-growth-operating-system"><img src="https://agentmods.dev/badge/skills/nospicyplease/amazon-ppc-advanced-skills/amazon-account-growth-operating-system.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 | $0.00152 | $0.05627 |
| Opus 5 | $0.00076 | $0.02814 |
| Sonnet 5 | $0.00030 | $0.01125 |
| Haiku 4.5 | $0.00015 | $0.00563 |
Grade A, and why
amazon-account-growth-operating-system 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 13d 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 — 486 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Account Growth Operating System
Purpose
Act as the decision-making and orchestration layer for proactive Amazon account growth. Do not merely summarize metrics. Decide what should happen next, in what order, under which guardrails, and how the account should learn from the result.
Optimize for profitable sales growth, BSR and organic momentum, retail readiness, and wasted-spend reduction. Protect current revenue before scaling new growth.
Upstream Skills
Use this skill after, or alongside, these upstream analyses when available:
amazon-ads-performance-drop-diagnosis: Use as the downside input. Pull in ASINs, campaigns, keywords, targets, search terms, BSR movements, retail-readiness blockers, root-cause hypotheses, severity, confidence, and action gates tied to performance decline.amazon-growth-opportunity-finder: Use as the upside input. Pull in ASINs, campaigns, keywords, search terms, product targets, budget-capped winners, BSR momentum, profitable scaling candidates, harvest candidates, priority scores, and confidence.
Do not treat upstream outputs as generic summaries. Treat them as separate evidence layers with their own gates. Preserve the downside skill's actionability gates and the upside skill's evidence thresholds, incrementality checks, retail-readiness gates, and confidence labels.
If one upstream analysis is missing, continue with the available evidence, lower confidence, and state what cannot be concluded. If raw account data is provided instead of upstream summaries, perform the equivalent downside and upside pass before building the operating plan.
Upstream Execution And Conflict Resolution
When the user asks for an account operating plan and the upstream skills are available, run or apply them before finalizing this skill's output:
- Use
amazon-ads-performance-drop-diagnosisfirst when there is any decline, risk, performance break, BSR deterioration, conversion drop, TACoS increase, efficiency decline, inventory issue, or important campaign loss. Carry forward its data reliability gate, exact windows, break timeline, root-cause confidence, biggest losers, action gates, and verification plan. - Use
amazon-growth-opportunity-findernext for upside. Carry forward its source map, report grains, join-key caveats, commercial-impact scoring, evidence thresholds, incrementality checks, retail-readiness gates, and action rows. - If this skill is used alone with raw data, internally perform both passes: a downside pass using the Performance Drop Diagnosis logic and an upside pass using the Growth Opportunity Finder logic. State that the upstream skills were not separately run if that is true.
- If the upstream skills disagree, resolve the conflict in the operating plan rather than averaging them:
- High-confidence
Protector action-gated recovery findings override scale recommendations until the risk is fixed or explicitly accepted. - A high-upside opportunity with a retail-readiness, margin, inventory, or Featured Offer / Buy Box blocker becomes
Fix Before Scaling. - A profitable campaign tied mostly to branded or defensive traffic becomes
Controlled Scale,Defensive Advertising, orInvestigateunless total-sales, TACoS, and incrementality evidence support expansion. - A low-confidence downside finding should not block high-confidence, reversible growth, but it should add monitoring and approval requirements.
- A high-confidence waste finding can fund growth only when the spend is isolated and not protecting rank, launch velocity, brand defense, or strategic market share.
- High-confidence
- Transfer upstream action gates directly:
- Do not recommend bid, budget, negative, pause, relaunch, or structural execution if the Performance Drop Diagnosis action gate says the evidence is not action-safe.
- Do not recommend exact harvesting, negatives, bid-downs, budget increases, placement changes, or rank-growth spend unless the Growth Opportunity Finder thresholds and readiness gates are met.
- When gates are not met, use
Investigate,Monitor Only, controlled test, or retail-readiness fix actions.
What ships with it
1 file 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.
- 13d ago First seen · 486 lines · 152 tokens per session scan A 3d9fb244b374
amazon-account-growth-operating-system is a skill published in the GitHub repository nospicyplease/amazon-ppc-advanced-skills (14 stars, last pushed 3mo ago), licensed MIT. It adds 152 tokens to every session and 5,627 once invoked, about $0.0008 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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