self-improver

Name: self-improver
Author: Affitor

3installs184forks

How to install self-improver?

affitor/affiliate-skills/self-improver

$npx -y skills add Affitor/affiliate-skills --skill self-improver

Installs into the current project.

Summary

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"

Files · 1

View on GitHub

SKILL.md

1	# Self-Improver
2
3	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.
4
5	## Stage
6
7	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.
8
9	## When to Use
10
11	- User has run a campaign and wants to understand results
12	- User's affiliate content isn't converting and wants to diagnose why
13	- User wants to compare actual vs expected results
14	- User says "what went wrong?", "why no conversions?", "how to improve?"
15	- User wants a structured retrospective on their affiliate efforts
16	- Chaining from S6.3 (performance-report) — analyze the data and plan improvements
17
18	## Input Schema
19
20	```yaml
21	campaign:
22	description: string # REQUIRED — what was done (e.g., "Published 3 blog reviews
23	# of AI video tools, shared on LinkedIn and Reddit")
24	duration: string # OPTIONAL — how long (e.g., "2 weeks", "1 month")
25	skills_used: string[] # OPTIONAL — which Affitor skills were used
26	channels: string[] # OPTIONAL — where content was distributed
27
28	results:
29	clicks: number # OPTIONAL — total clicks on affiliate links
30	conversions: number # OPTIONAL — total signups/purchases
31	revenue: number # OPTIONAL — total commission earned
32	traffic: number # OPTIONAL — total page views / impressions
33	feedback: string # OPTIONAL — qualitative feedback received
34
35	expectations:
36	expected_clicks: number # OPTIONAL — what was expected
37	expected_conversions: number # OPTIONAL
38	expected_revenue: number # OPTIONAL
39	benchmark: string # OPTIONAL — "industry average" or specific number
40
41	context:
42	niche: string # OPTIONAL — product category
43	experience: string # OPTIONAL — "first campaign" \| "experienced"
44	budget: string # OPTIONAL — money spent (if any)
45	```
46
47	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.
48
49	## Workflow
50
51	### Step 1: Establish Baseline
52
53	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."
54
55	### Step 2: Compare Results vs Expectations
56
57	Calculate gaps:
58	- Traffic gap: Expected vs actual impressions/visits
59	- Click gap: Expected vs actual CTR
60	- Conversion gap: Expected vs actual conversion rate
61	- Revenue gap: Expected vs actual earnings
62
63	Use industry benchmarks if user doesn't have expectations:
64	- Affiliate blog CTR: 2-5%
65	- Affiliate conversion rate: 1-3%
66	- Social post engagement: 1-3% of impressions
67	- Email click rate: 2-5%
68
69	### Step 3: Diagnose Root Causes
70
71	Apply affiliate-specific diagnostic frameworks:
72
73	Offer-Market Fit: Is the product right for the audience?
74	- Wrong audience for the product
75	- Product too expensive for the audience's budget
76	- Product solves a problem the audience doesn't have
77
78	Traffic-Content Match: Is the traffic source aligned with the content?
79	- Blog content promoted on TikTok (format mismatch)
80	- Reddit post that reads like an ad (platform mismatch)
81	- Cold traffic sent to a hard sell (temperature mismatch)
82
83	Funnel Leaks: Where do people drop off?
84	- High impressions but low clicks → weak headline/hook
85	- High clicks but low conversions → landing page or product issue
86	- High conversions but low revenue → wrong product (low commission)
87
88	### Step 4: Prioritize Improvements
89
90	Rank each improvement by:
91	- Impact: How much would this change move the needle? (1-5)
92	- Effort: How hard is it to implement? (1-5)
93	- Priority: Impact / Effort ratio
94
95	### Step 5: Create Iteration Plan
96
97	For each top improvement, specify:
98	- What to change
99	- Which Affitor skill to re-run
100	- Exact prompt modificatio

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self-improver

3installs184forks

View on GitHub

How to install self-improver?

affitor/affiliate-skills/self-improver

$npx -y skills add Affitor/affiliate-skills --skill self-improver

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Self-Improver
2
3	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.
4
5	## Stage
6
7	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.
8
9	## When to Use
10
11	- User has run a campaign and wants to understand results
12	- User's affiliate content isn't converting and wants to diagnose why
13	- User wants to compare actual vs expected results
14	- User says "what went wrong?", "why no conversions?", "how to improve?"
15	- User wants a structured retrospective on their affiliate efforts
16	- Chaining from S6.3 (performance-report) — analyze the data and plan improvements
17
18	## Input Schema
19
20	```yaml
21	campaign:
22	description: string # REQUIRED — what was done (e.g., "Published 3 blog reviews
23	# of AI video tools, shared on LinkedIn and Reddit")
24	duration: string # OPTIONAL — how long (e.g., "2 weeks", "1 month")
25	skills_used: string[] # OPTIONAL — which Affitor skills were used
26	channels: string[] # OPTIONAL — where content was distributed
27
28	results:
29	clicks: number # OPTIONAL — total clicks on affiliate links
30	conversions: number # OPTIONAL — total signups/purchases
31	revenue: number # OPTIONAL — total commission earned
32	traffic: number # OPTIONAL — total page views / impressions
33	feedback: string # OPTIONAL — qualitative feedback received
34
35	expectations:
36	expected_clicks: number # OPTIONAL — what was expected
37	expected_conversions: number # OPTIONAL
38	expected_revenue: number # OPTIONAL
39	benchmark: string # OPTIONAL — "industry average" or specific number
40
41	context:
42	niche: string # OPTIONAL — product category
43	experience: string # OPTIONAL — "first campaign" \| "experienced"
44	budget: string # OPTIONAL — money spent (if any)
45	```
46
47	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.
48
49	## Workflow
50
51	### Step 1: Establish Baseline
52
53	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."
54
55	### Step 2: Compare Results vs Expectations
56
57	Calculate gaps:
58	- Traffic gap: Expected vs actual impressions/visits
59	- Click gap: Expected vs actual CTR
60	- Conversion gap: Expected vs actual conversion rate
61	- Revenue gap: Expected vs actual earnings
62
63	Use industry benchmarks if user doesn't have expectations:
64	- Affiliate blog CTR: 2-5%
65	- Affiliate conversion rate: 1-3%
66	- Social post engagement: 1-3% of impressions
67	- Email click rate: 2-5%
68
69	### Step 3: Diagnose Root Causes
70
71	Apply affiliate-specific diagnostic frameworks:
72
73	Offer-Market Fit: Is the product right for the audience?
74	- Wrong audience for the product
75	- Product too expensive for the audience's budget
76	- Product solves a problem the audience doesn't have
77
78	Traffic-Content Match: Is the traffic source aligned with the content?
79	- Blog content promoted on TikTok (format mismatch)
80	- Reddit post that reads like an ad (platform mismatch)
81	- Cold traffic sent to a hard sell (temperature mismatch)
82
83	Funnel Leaks: Where do people drop off?
84	- High impressions but low clicks → weak headline/hook
85	- High clicks but low conversions → landing page or product issue
86	- High conversions but low revenue → wrong product (low commission)
87
88	### Step 4: Prioritize Improvements
89
90	Rank each improvement by:
91	- Impact: How much would this change move the needle? (1-5)
92	- Effort: How hard is it to implement? (1-5)
93	- Priority: Impact / Effort ratio
94
95	### Step 5: Create Iteration Plan
96
97	For each top improvement, specify:
98	- What to change
99	- Which Affitor skill to re-run
100	- Exact prompt modificatio

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6 picks

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