ab-testing
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use…
Use when Amazon Ads optimization analysis, recommendations, approval packets, demos, recordings, or public artifacts must preserve exact source KPIs while masking account/profile/project names, products, ASINs, SKUs, campaigns, ad groups, keywords, search terms, targets,
Claude Code
$ npx -y skills add nospicyplease/amazon-ppc-advanced-skills --skill case-camouflage-skill --agent claude-code
Codex
$ npx skills add nospicyplease/amazon-ppc-advanced-skills --skill case-camouflage-skill -a codex -y
Cursor
$ npx skills add nospicyplease/amazon-ppc-advanced-skills --skill case-camouflage-skill -a cursor -y
Gemini CLI
$ npx skills add nospicyplease/amazon-ppc-advanced-skills --skill case-camouflage-skill -a gemini-cli -y
GitHub Copilot
$ npx skills add nospicyplease/amazon-ppc-advanced-skills --skill case-camouflage-skill -a github-copilot -y
OpenCode
$ npx skills add nospicyplease/amazon-ppc-advanced-skills --skill case-camouflage-skill -a opencode -y
Windsurf
$ npx skills add nospicyplease/amazon-ppc-advanced-skills --skill case-camouflage-skill -a windsurf -y
name: case-camouflage-skill description: Use when Amazon Ads optimization analysis, recommendations, approval packets, demos, recordings, or public artifacts must preserve exact source KPIs while masking account/profile/project names, products, ASINs, SKUs, campaigns, ad groups, keywords, search terms, targets, placements, filenames, URLs, and source-derived identifiers. Produces masked approval packets only; it must not directly mutate Amazon Ads.
Use this skill when preparing Amazon Ads optimization output that may be shown to users, reviewers, public repos, demos, recordings, docs, or evals. The job is to camouflage the case context: keep optimization math real and exact while masking only user-facing labels and source-derived identifiers.
1. Confirm scope: tenant/profile, marketplace, date windows, available reports, requested output surface, and whether this is public/demo/recording output. 2. Load or configure a tenant-scoped masking registry. If text-only identifiers are present, require a per-tenant HMAC secret. See [registry](references/registry.md). 3. Run the optimization in the analytical plane using raw source IDs and exact metrics. See [metrics](references/metrics.md). 4. Resolve display handles only after analysis. See [masking](references/masking.md). 5. Build a masked approval packet for recommended actions. Keep private execution manifests in ignored private paths only. See [approvals](references/approvals.md). 6. Scan public artifacts, logs, stdout/stderr, rationales, readbacks, metadata, hidden sheets, and filenames before release. See [artifact/log safety](references/artifact-log-safety.md). 7. Report coverage with counts and statuses only, never mappings. See [coverage](references/coverage.md).
For user-facing results, include:
If a raw label or identifier appears in a draft, stop and remediate before showing it.
AI assistant workflows for Amazon PPC diagnosis, growth planning, search-term harvesting, product-aware Rocketcart MCP reviews, masked optimization output, and approval-ready action queues.
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use…
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