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Review affiliate campaign results and improve strategy.
Passed 18 of 22 test cases with the skill, and 10 of 22 without it.
and 11 more in the full report
10 fixed8 passed either way2 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
Over the last month, I published 4 detailed review articles about web hosting services on my niche blog. I received 3,500 total page views, 80 affiliate link clicks, and 0 signups ($0 revenue), whereas I expected a 2% conversion rate on clicks. Please analyze what happened. Provide an output structured as a campaign retrospective, a metric gap comparison, a root cause investigation looking into potential funnel drop-offs or offer mismatch, a ranked list of specific improvements with prompt templates and priority ratings, and an ordered next-steps execution plan.
| Checked | Without skill | With skill |
|---|---|---|
| The improvement actions rate impact and effort using a 1-5 numeric scale. | Missed | Passed |
| The next-steps plan provides prompt modification templates for content adjustments. | Passed | Passed |
| The gap analysis compares actual conversion rates against benchmark expectations. | Passed | Passed |
The instructions your model reads when the skill loads.
Review affiliate campaign results, diagnose what worked and what didn't, and generate a prioritized improvement plan. Uses affiliate-specific diagnostic frameworks (offer-market fit, traffic-content match, funnel leak analysis) to identify root causes and actionable fixes.
S8: Meta — Most affiliates repeat the same mistakes because they never do structured retrospectives. Self-Improver closes the feedback loop: it takes your results, compares them to expectations, diagnoses gaps using affiliate-specific frameworks, and produces concrete actions that feed back into S1-S7 for the next iteration.
campaign:
description: string # REQUIRED — what was done (e.g., "Published 3 blog reviews
# of AI video tools, shared on LinkedIn and Reddit")
duration: string # OPTIONAL — how long (e.g., "2 weeks", "1 month")
skills_used: string[] # OPTIONAL — which Affitor skills were used
channels: string[] # OPTIONAL — where content was distributed
results:
clicks: number # OPTIONAL — total clicks on affiliate links
conversions: number # OPTIONAL — total signups/purchases
revenue: number # OPTIONAL — total commission earned
traffic: number # OPTIONAL — total page views / impressions
feedback: string # OPTIONAL — qualitative feedback received
expectations:
expected_clicks: number # OPTIONAL — what was expected
expected_conversions: number # OPTIONAL
expected_revenue: number # OPTIONAL
benchmark: string # OPTIONAL — "industry average" or specific number
test cases passed, vs 10 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.