# Canonical NotFair workflow Human Guide

## What This Is For
Plan and review Amazon Ads with margin-aware ACoS, product, and search-term guardrails. It gives the agent a clearer input/output frame for paid media: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Canonical NotFair workflow agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for canonical NotFair workflow.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Canonical NotFair workflow guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- Frame the work around canonical NotFair workflow and ask for the context needed to do it well.
- Turn vague preferences into explicit choices before drafting the final output.
- Ask for a concrete deliverable, not just general advice.

## Decision Points And Nuance
The original skill emphasizes: Supporting file: paid-ads/paid-ads-amazon/SKILL.md, Start with unit economics, Review and handoff.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- Starting without a clear audience or goal.
- Asking for a final artifact before sharing examples or constraints.
- Accepting a generic first draft without checking it against the intended use.

## Copy-And-Paste Prompt
```text
Use the Canonical NotFair workflow human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the nowork-studio/notfair-plugin skill entry for `paid-ads-amazon`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# Canonical NotFair workflow

Read [`../../paid-ads/paid-ads-amazon/SKILL.md`](../../paid-ads/paid-ads-amazon/SKILL.md) completely, then follow it as the active workflow. Resolve every relative reference from that file against `../../paid-ads/paid-ads-amazon/`.

---

## Supporting file: paid-ads/paid-ads-amazon/SKILL.md

# Amazon Ads Planning

Read `../shared/operating-contract.md` and `../shared/measurement-framework.md`. This plugin does not declare a first-party NotFair Amazon Ads MCP mutation surface; use a verified connector or supplied report and deliver a reviewable operator brief.

## Start with unit economics

Record the product/ASIN, marketplace, currency, contribution margin, price, inventory constraint, and target ACoS. ACoS is spend divided by attributed ad revenue; it is only good or bad relative to margin and the user's strategic goal. Separate the advertised product from any measured cross-sell before judging performance.

Propose the narrowest learning plan: product scope, campaign intent, automatic discovery or manual term/ASIN hypothesis, budget, negative/exclusion rule, and review window. Treat search-term findings as evidence for targeted negatives or promotion into controlled targeting, not as a reason to remove broad discovery prematurely.

## Review and handoff

Report spend, attributed sales, ACoS, ROAS if useful, orders, conversion rate, search-term quality, and inventory risk for a complete comparable window. State reporting lag and attribution source. Mark changes `ready_for_review` until a verified connector or authorized Amazon Ads operator confirms the exact result.