/self-improver

name: self-improver
description: >
  Review affiliate campaign results and improve strategy. Triggers on:
  "review my results", "what went wrong", "how to improve conversions",
  "analyze my campaign", "affiliate retrospective", "why am I not converting",
  "improve my strategy", "what should I change", "campaign review",
  "optimize my approach", "learn from my results", "post-mortem on my campaign".
license: MIT
version: "1.0.0"
tags: ["affiliate-marketing", "meta", "planning", "compliance", "improvement", "feedback"]
compatibility: "Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"
metadata:
  author: affitor
  version: "1.0"
  stage: S8-Meta

Self-Improver

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.

Stage

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.

When to Use

• User has run a campaign and wants to understand results
• User's affiliate content isn't converting and wants to diagnose why
• User wants to compare actual vs expected results
• User says "what went wrong?", "why no conversions?", "how to improve?"
• User wants a structured retrospective on their affiliate efforts
• Chaining from S6.3 (performance-report) — analyze the data and plan improvements

Input Schema

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

context:
  niche: string                # OPTIONAL — product category
  experience: string           # OPTIONAL — "first campaign" | "experienced"
  budget: string               # OPTIONAL — money spent (if any)

**Chaining context**: If S6.3 (performance-report) was run in the same conversation, pull KPIs directly. If S1-S5 outputs exist in context, reference them for gap analysis.

Workflow

Step 1: Establish Baseline

Collect campaign description and results. If numbers are missing, work with whatever is available. State assumptions clearly: "You didn't share click data, so I'll focus on qualitative analysis."

Step 2: Compare Results vs Expectations

Calculate gaps:

• **Traffic gap**: Expected vs actual impressions/visits
• **Click gap**: Expected vs actual CTR
• **Conversion gap**: Expected vs actual conversion rate
• **Revenue gap**: Expected vs actual earnings

Use industry benchmarks if user doesn't have expectations:

• Affiliate blog CTR: 2-5%
• Affiliate conversion rate: 1-3%
• Social post engagement: 1-3% of impressions
• Email click rate: 2-5%

Step 3: Diagnose Root Causes

Apply affiliate-specific diagnostic frameworks:

**Offer-Market Fit**: Is the product right for the audience?

• Wrong audience for the product
• Product too expensive for the audience's budget
• Product solves a problem the audience doesn't have

**Traffic-Content Match**: Is the traffic source aligned with the content?

• Blog content promoted on TikTok (format mismatch)
• Reddit post that reads like an ad (platform mismatch)
• Cold traffic sent to a hard sell (temperature mismatch)

**Funnel Leaks**: Where do people drop off?

• High impressions but low clicks → weak headline/hook
• High clicks but low conversions → landing page or product issue
• High conversions but low revenue → wrong product (low commission)

Step 4: Prioritize Improvements

Rank each improvement by:

• **Impact**: How much would this change move the needle? (1-5)
• **Effort**: How hard is it to implement? (1-5)
• **Priority**: Impact / Effort ratio

Step 5: Create Iteration Plan

For each top improvement, specify:

• What to change
• Which Affitor skill to re-run
• Exact prompt modification for better results
• Expected improvement (realistic estimate)

Step 6: Self-Validation

Before presenting output, verify:

• [ ] Gap calculations accurate: expected minus actual
• [ ] Root causes are evidence-based, not speculation
• [ ] Impact (1-5) and effort (1-5) scores are justified with reasoning
• [ ] Next steps reference specific Affitor skills by name
• [ ] Iteration plan has concrete timeline and measurable success metric

If any check fails, fix the output before delivering. Do not flag the checklist to the user — just ensure the output passes.

Output Schema

output_schema_version: "1.0.0"  # Semver — bump major on breaking changes
retrospective:
  campaign: string
  period: string
  overall_assessment: string   # "strong" | "average" | "needs_work" | "failing"

gaps:
  - metric: string             # e.g., "conversion_rate"
    expected: string
    actual: string
    gap: string                # e.g., "-2.5%"

diagnosis:
  root_causes:
    - cause: string