# Canonical NotFair workflow Human Guide

## What This Is For
Diagnose wasted paid-ad spend, pacing, and allocation, then propose safe evidence-backed optimizations. 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-optimize/SKILL.md, Diagnose before cutting, Rank reversible moves, Approval and follow-up.

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-optimize`.

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

---

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

# Paid Ads Optimization

Read `../shared/operating-contract.md` and `../shared/measurement-framework.md`. Review before changing anything.

## Diagnose before cutting

Verify the conversion signal, period completeness, spend volume, attribution model, and recent account changes. Spend with no recorded conversion can indicate broken tracking or immature data; treat it as a hypothesis until the signal and volume support an intervention. Check landing-page or operational failures before blaming targeting.

Classify the bottleneck as query/audience quality, creative fatigue, delivery/rank, budget constraint, landing-page mismatch, tracking, or economics. Use the specialized Google, Meta, X, or LinkedIn skill for live diagnosis. For other platforms, analyze only the supplied or verified data.

## Rank reversible moves

Prefer this order: exclude an irrelevant query, placement, or audience; pause the narrowest losing unit; adjust budget or bid in a measured step; then consider structural change. For a reallocation, show the current and proposed allocations, the same total budget unless the user approves an increase, and the observable hypothesis.

Do not declare a loser from a few clicks. Set a threshold appropriate to the named target CPA, conversion lag, and channel role. Preserve upper-funnel and assisted-conversion context rather than judging all campaigns on last-click CPA alone.

## Approval and follow-up

Present each exact mutation with scope, current value, proposed value, currency exposure, rationale, and review date. After approval, execute only through the verified platform skill or connector, read back the result, and record the intervention's expected effect and guardrail. Revisit after the declared observation window instead of promising a generic ongoing watch.