Self Improver

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 2515ce9634da · 15 Oct 2026Report an issue or request a re-scan

• PublisherAffitor GitHub organizationOwner of the repository this skill is read from
• Source folderAffitor/affiliate-skills/skills/meta/self-improverThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict2515ce9634daA new audit runs when the source changes
• Repository statusNot archivedLast push 2 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its 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",…

---
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

```yaml
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

```yaml
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 

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-15
   ✓SAFE B 89 / 100

   first audit · source 2515ce9634da

Questions about Self Improver

• github.com/Affitor/affiliate-skills/tree/main/skills/meta/self-i — the audited source at commit 2515ce9634da and every finding aboveObserved · 15 Oct 2026

Spotted something wrong? Report a correction · Methodology