AGENT SKILLBY NOWORK-STUDIO
Canonical NotFair workflow
AT A GLANCE
Canonical NotFair workflow is an agent skill for paid ads. Diagnose wasted paid-ad spend, pacing, and allocation, then propose safe evidence-backed optimizations. The nowork-studio/notfair-plugin repository on GitHub, maintained by nowork-studio, has 3.4K stars and the MIT license. To add it, run npx skills add nowork-studio/notfair-plugin --skill paid-ads-optimize
- SOURCE REPOSITORY
- nowork-studio/notfair-plugin (opens GitHub in a new tab)
- REPO STARS
- SOURCE REPOSITORY
- LICENSE
- MIT
- CATEGORIES
- SKILL.MD SIZE
- 2.1 KB28 LINES
QUICK ANSWER
- WHAT IT IS01
- Diagnose wasted paid-ad spend, pacing, and allocation, then propose safe evidence-backed optimizations. Use for waste, negatives, budgets, bid changes, poor CPA or ROAS, underpacing, overspend, or scaling decisions. The value is a focused slice of paid media judgment, useful when several similar skills cover the same ground.
- INPUTS02
- Context for paid media: your goals, audience, constraints, and any source material the skill asks for.
- OUTPUT03
- A ready-to-use result for paid media: the analysis, copy, or recommendations the agent produces.
INSTALL
One command adds this skill from its source repository.
npx skills add nowork-studio/notfair-plugin --skill paid-ads-optimizeREAD THE SKILL.MD BELOW BEFORE YOU RUN IT. SKILLS INSTRUCT YOUR AGENT.
SKILL.MD
The full file, as published in the source repository. Copy it, download it or open it on GitHub.
ON THIS PAGE · 6
Canonical NotFair workflow#
Read ../../paid-ads/paid-ads-optimize/SKILL.md (opens in a new tab) 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.
FAQ
07 QUESTIONS
01What is the Canonical NotFair workflow agent skill?
Diagnose wasted paid-ad spend, pacing, and allocation, then propose safe evidence-backed optimizations. Use for waste, negatives, budgets, bid changes, poor CPA or ROAS, underpacing, overspend, or scaling decisions. It is listed under paid ads in the 100xLab agent skills directory.
02How do I install Canonical NotFair workflow in Cursor, Claude Code, or Codex?
Run npx skills add nowork-studio/notfair-plugin --skill paid-ads-optimize in the project where you want it, then ask your agent for the skill by name. The --skill flag installs only Canonical NotFair workflow, not every skill in the repository.
03What does the Canonical NotFair workflow SKILL.md cover?
The SKILL.md includes sections on Supporting file: paid-ads/paid-ads-optimize/SKILL.md, Diagnose before cutting, Rank reversible moves and Approval and follow-up. Read the full text on this page or in plain markdown at /resources/agent-skills/nowork-studio-notfair-plugin--paid-ads-optimize--xefuav/raw.
04Which agents can use Canonical NotFair workflow?
Canonical NotFair workflow installs with the cross-agent skills installer (npx skills add), which sets it up for agents such as Claude Code, Cursor and Codex. Each agent loads the SKILL.md when you ask for the skill by name.
05What license is Canonical NotFair workflow under?
The nowork-studio/notfair-plugin repository declares the MIT license. Read the license file in the repository before you copy or redistribute the skill.
06Is Canonical NotFair workflow free to use?
Reading, copying and installing Canonical NotFair workflow costs nothing: it is published in a public GitHub repository under the MIT license. Reuse and redistribution follow that license.
07Where is the source of Canonical NotFair workflow?
Canonical NotFair workflow comes from https://github.com/nowork-studio/notfair-plugin, which has 3.4K GitHub stars. The SKILL.md is at https://github.com/nowork-studio/notfair-plugin/blob/HEAD/skills/paid-ads-optimize/SKILL.md. The author is nowork-studio.
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ATTRIBUTION
Canonical NotFair workflow was written by its original authors and is published in nowork-studio/notfair-plugin (opens GitHub in a new tab) under the MIT license. All rights stay with the original authors.
100xLab did not write, maintain or endorse this skill. This page lists it for discovery. Repo stars belong to the source repository, not to the skill. Review any skill before you install it.