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Plan a complete affiliate funnel from research to revenue.
Passed 19 of 25 test cases with the skill, and 12 of 25 without it.
and 6 more in the full report
9 fixed10 passed either way4 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am a total beginner in affiliate marketing focusing on sustainable fashion with 5 hours per week. Should I try to launch across blog, YouTube, podcast, TikTok, Instagram, and email newsletter all at once in month one? Give me a structured execution plan.
| Checked | Without skill | With skill |
|---|---|---|
| The plan recommends narrowing focus to a maximum of two channels rather than attempting all six channels immediately. | Passed | Passed |
| The initial step in the sequence assigns research with an S1 skill. | Missed | Passed |
The instructions your model reads when the skill loads.
Plan a complete affiliate funnel from research to revenue by chaining Affitor skills into a week-by-week execution roadmap. Output is a Markdown plan with skill sequence, time estimates, and exact invocation prompts for each step.
S8: Meta — Most affiliates fail because they skip steps or work out of order. Funnel Planner solves this by mapping the user's resources (time, channels, experience) to a personalized execution plan that chains S1-S7 skills in the right sequence. It's the onboarding wizard for affiliate marketing via AI agent.
niche: string # OPTIONAL — e.g., "AI tools", "fitness supplements"
# If not provided, S1 research will help identify one
product: string # OPTIONAL — specific product if already chosen
# e.g., "HeyGen" or "Semrush"
experience_level: string # OPTIONAL — "beginner" | "intermediate" | "advanced"
# Default: "beginner"
available_channels: # OPTIONAL — platforms the user can use
- string # e.g., ["blog", "twitter", "linkedin", "email"]
# Default: ["blog", "twitter"]
weekly_hours: number # OPTIONAL — hours per week available
# Default: 5
goal: string # OPTIONAL — "first_commission" | "scale_to_1k" | "scale_to_10k"
# Default: "first_commission"
test cases passed, vs 12 / 25 without
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