performance-report

Name: performance-report
Author: Affitor

5installs184forks

How to install performance-report?

affitor/affiliate-skills/performance-report

$npx -y skills add Affitor/affiliate-skills --skill performance-report

Installs into the current project.

Summary

Generate affiliate performance reports with KPIs and recommendations. Triggers on: "show my affiliate report", "how are my programs doing", "performance review", "earnings report", "monthly affiliate report", "weekly report", "analyze my affiliate earnings", "which program is bes

Files · 1

View on GitHub

SKILL.md

1	# Performance Report
2
3	Generate weekly or monthly affiliate performance reports — earnings, clicks, conversions, EPC, top performers, underperformers, and trend analysis. Output is a Markdown report with KPI dashboard, program rankings, and actionable recommendations.
4
5	## Stage
6
7	S6: Analytics — Data without analysis is just noise. This skill transforms raw affiliate numbers into insights — which programs are worth your time, which are dragging your portfolio down, and where to focus next. Professional affiliates review performance weekly.
8
9	## When to Use
10
11	- User wants to review their affiliate earnings for a period
12	- User asks "how are my programs doing?" or "show me my affiliate report"
13	- User has click/conversion/revenue data and wants analysis
14	- User wants to compare performance across multiple programs
15	- User says "weekly report", "monthly report", "earnings breakdown"
16	- Chaining from S6.1 (conversion-tracker) — analyze the data those links collected
17
18	## Input Schema
19
20	```yaml
21	programs:
22	- name: string # REQUIRED — program name (e.g., "HeyGen")
23	clicks: number # OPTIONAL — total clicks this period
24	conversions: number # OPTIONAL — total conversions
25	revenue: number # OPTIONAL — total commission earned ($)
26	commission: number # OPTIONAL — commission per sale ($)
27	spend: number # OPTIONAL — money spent on ads/promotion ($)
28
29	period: string # OPTIONAL — "week" \| "month" \| "quarter"
30	# Default: "month"
31
32	goals:
33	revenue_target: number # OPTIONAL — target revenue for the period ($)
34	conversion_target: number # OPTIONAL — target conversions
35
36	previous_period: # OPTIONAL — last period's data for trend analysis
37	- name: string
38	clicks: number
39	conversions: number
40	revenue: number
41
42	notes: string # OPTIONAL — context about the period
43	# (e.g., "launched new blog post week 2")
44	```
45
46	Chaining context: If S1 program data or S6.1 tracking data exists in conversation, pull program names and any available metrics.
47
48	## Workflow
49
50	### Step 1: Collect Program Data
51
52	Gather data from user input. If data is incomplete, work with what's available and note gaps:
53	- "You provided revenue but not clicks — I can calculate revenue per program but not EPC or conversion rate."
54
55	### Step 2: Calculate KPIs
56
57	For each program:
58	- EPC (Earnings Per Click): revenue / clicks
59	- Conversion Rate: conversions / clicks × 100
60	- Revenue Share: program revenue / total revenue × 100
61	- CPA (Cost Per Acquisition): spend / conversions (if spend provided)
62	- ROAS (Return on Ad Spend): revenue / spend (if spend provided)
63	- Commission Per Sale: revenue / conversions
64
65	Portfolio-level:
66	- Total Revenue: sum of all program revenue
67	- Blended EPC: total revenue / total clicks
68	- Blended Conversion Rate: total conversions / total clicks × 100
69	- Top Performer: highest EPC program
70	- Underperformer: lowest EPC program
71
72	### Step 3: Rank Programs
73
74	Sort programs by ROI efficiency:
75	1. EPC (primary sort)
76	2. Total revenue (secondary)
77	3. Conversion rate (tertiary)
78
79	Assign labels:
80	- Star: High EPC + high volume → double down
81	- Cash Cow: Moderate EPC + high volume → maintain
82	- Question Mark: High EPC + low volume → scale up
83	- Dog: Low EPC + low volume → consider dropping
84
85	### Step 4: Identify Trends
86
87	If `previous_period` data is provided:
88	- Revenue trend: up/down/flat (with percentage)
89	- Click trend: up/down/flat
90	- Conversion trend: up/down/flat
91	- Per-program trends
92
93	### Step 5: Generate Recommendations
94
95	Based on data:
96	- Double down: Programs with high EPC that need more traffic
97	- Optimize: Programs with high traffic but low conversion (content issue)
98	- Phase out: Programs with low EPC and low volume
99	- Investigate: Programs with unusual patterns (sudden drops)
100
101	### Step 6: Self-Validation
102
103	Before presenting output, verify:
104
105	- [ ] EPC calculation correct: revenue ÷ clicks
106	- [ ] Conversion rate percentages are accurate
107	- [ ] Revenue shares across programs sum to ~100%
108	- [ ] Labels match metrics: Star (high EPC + growth), Cash Cow (high revenue + stable), Question Mark (low data), Dog (declining)
109	- [ ] Reco

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performance-report

5installs184forks

View on GitHub

How to install performance-report?

affitor/affiliate-skills/performance-report

$npx -y skills add Affitor/affiliate-skills --skill performance-report

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Performance Report
2
3	Generate weekly or monthly affiliate performance reports — earnings, clicks, conversions, EPC, top performers, underperformers, and trend analysis. Output is a Markdown report with KPI dashboard, program rankings, and actionable recommendations.
4
5	## Stage
6
7	S6: Analytics — Data without analysis is just noise. This skill transforms raw affiliate numbers into insights — which programs are worth your time, which are dragging your portfolio down, and where to focus next. Professional affiliates review performance weekly.
8
9	## When to Use
10
11	- User wants to review their affiliate earnings for a period
12	- User asks "how are my programs doing?" or "show me my affiliate report"
13	- User has click/conversion/revenue data and wants analysis
14	- User wants to compare performance across multiple programs
15	- User says "weekly report", "monthly report", "earnings breakdown"
16	- Chaining from S6.1 (conversion-tracker) — analyze the data those links collected
17
18	## Input Schema
19
20	```yaml
21	programs:
22	- name: string # REQUIRED — program name (e.g., "HeyGen")
23	clicks: number # OPTIONAL — total clicks this period
24	conversions: number # OPTIONAL — total conversions
25	revenue: number # OPTIONAL — total commission earned ($)
26	commission: number # OPTIONAL — commission per sale ($)
27	spend: number # OPTIONAL — money spent on ads/promotion ($)
28
29	period: string # OPTIONAL — "week" \| "month" \| "quarter"
30	# Default: "month"
31
32	goals:
33	revenue_target: number # OPTIONAL — target revenue for the period ($)
34	conversion_target: number # OPTIONAL — target conversions
35
36	previous_period: # OPTIONAL — last period's data for trend analysis
37	- name: string
38	clicks: number
39	conversions: number
40	revenue: number
41
42	notes: string # OPTIONAL — context about the period
43	# (e.g., "launched new blog post week 2")
44	```
45
46	Chaining context: If S1 program data or S6.1 tracking data exists in conversation, pull program names and any available metrics.
47
48	## Workflow
49
50	### Step 1: Collect Program Data
51
52	Gather data from user input. If data is incomplete, work with what's available and note gaps:
53	- "You provided revenue but not clicks — I can calculate revenue per program but not EPC or conversion rate."
54
55	### Step 2: Calculate KPIs
56
57	For each program:
58	- EPC (Earnings Per Click): revenue / clicks
59	- Conversion Rate: conversions / clicks × 100
60	- Revenue Share: program revenue / total revenue × 100
61	- CPA (Cost Per Acquisition): spend / conversions (if spend provided)
62	- ROAS (Return on Ad Spend): revenue / spend (if spend provided)
63	- Commission Per Sale: revenue / conversions
64
65	Portfolio-level:
66	- Total Revenue: sum of all program revenue
67	- Blended EPC: total revenue / total clicks
68	- Blended Conversion Rate: total conversions / total clicks × 100
69	- Top Performer: highest EPC program
70	- Underperformer: lowest EPC program
71
72	### Step 3: Rank Programs
73
74	Sort programs by ROI efficiency:
75	1. EPC (primary sort)
76	2. Total revenue (secondary)
77	3. Conversion rate (tertiary)
78
79	Assign labels:
80	- Star: High EPC + high volume → double down
81	- Cash Cow: Moderate EPC + high volume → maintain
82	- Question Mark: High EPC + low volume → scale up
83	- Dog: Low EPC + low volume → consider dropping
84
85	### Step 4: Identify Trends
86
87	If `previous_period` data is provided:
88	- Revenue trend: up/down/flat (with percentage)
89	- Click trend: up/down/flat
90	- Conversion trend: up/down/flat
91	- Per-program trends
92
93	### Step 5: Generate Recommendations
94
95	Based on data:
96	- Double down: Programs with high EPC that need more traffic
97	- Optimize: Programs with high traffic but low conversion (content issue)
98	- Phase out: Programs with low EPC and low volume
99	- Investigate: Programs with unusual patterns (sudden drops)
100
101	### Step 6: Self-Validation
102
103	Before presenting output, verify:
104
105	- [ ] EPC calculation correct: revenue ÷ clicks
106	- [ ] Conversion rate percentages are accurate
107	- [ ] Revenue shares across programs sum to ~100%
108	- [ ] Labels match metrics: Star (high EPC + growth), Cash Cow (high revenue + stable), Question Mark (low data), Dog (declining)
109	- [ ] Reco

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