Open your phone and refresh the ASP management screen. Yesterday's confirmed reward was 500 yen again... Anyone who has tried high-ticket affiliate marketing knows this small sigh of frustration. Even if you write 10 articles, the numbers don't move unless the deals are approved. No matter how much you write, your own working hours become the ceiling. Before you know it, you're alone at 5:00 AM opening the WordPress editor.
Let me be clear upfront. This article does not contain a single line about "earning 500k with one button." That's because generating a stable 500k yen monthly with high-ticket affiliates isn't magic that anyone can achieve in a month.
However, imagine this: In the morning, before brewing coffee, you open your terminal, and Claude Code has already finished a draft of an article from the night before. X (Twitter) posts are scheduled. A report of articles that dropped in ranking, along with the reasons and rewrite suggestions, is already lined up. All you do is decide the direction and give a final check.
This isn't a dream; it's a typical morning scene after handing over tasks to Claude Code (an agent-type AI developed by Anthropic that runs in the terminal). This article only describes the steps to create that environment for yourself. We will reverse-engineer a system to eliminate over 90% of high-ticket affiliate work using Claude Code and generate 500k yen monthly stably for 12 months. I have listed about 40 copy-pasteable prompts in order, assuming you are a beginner. Even if you've never touched Claude Code, you can set up the entire system by following from the top.
Why "stable 500k" instead of "1 million"? The reason is simple. Continuing to earn 500k for 12 months changes your life more surely than hitting a 1 million peak once. 500k x 12 months = 6 million yen. If you get this in a year, the freedom in your life planning changes significantly when combined with your main job. Conversely, hitting 1 million once and having sales drop to zero the moment that deal ends is typical for those who fail in high-ticket marketing. "Stability" is actually harder. That's why we hand it over to Claude Code.
There's another reason I'm writing this so specifically. There are too many posts talking about "AI side hustles" and "automation" that stop at abstract theories. They say "Claude Code is amazing" or "you can automate with AI," but no one writes what to do, in what order, and with which prompts. That's why people can't start, or even if they do, they don't continue.
I will honestly reveal the structure. The content of high-ticket affiliate marketing is incredibly simple.
Consistently close 10 to 25 deals per month with a unit price of 20,000 to 50,000 yen. Put that on autopilot and grow it into a portfolio that can withstand deal changes. That's it.
In other words, if you build these two things diligently, those who can replicate it will. Claude Code is the partner that takes over almost all of that assembly.
We will cover 10 chapters: Deal selection, persona and offer design, mass production of articles, SNS funnels, sales copy AB testing, prompt details, expanding capabilities with MCP, autopilot with Routines, avoiding pitfalls, and habits to discard.
By the time you finish reading, you will have both the "blueprint for stabilizing 500k for 12 months" and the "implementation steps that work with copy-paste." All you have to do is rewrite the deal names or genres and run them on the spot.
Let's get started.
Chapter 1: Deal Selection: 80% of the 500k is decided here before writing
The first chapter is about "what to sell." It's cruel, but if you make a mistake here, no matter how hard you work in the later chapters, you won't be able to stabilize 500k. Even if you perfectly automate a 1,000 yen product deal with Claude Code, you need 500 sales every month for 500k. With a 30,000 yen high-ticket deal, the same sales come from 17 deals. The work is almost the same. The only difference is what you chose first.
90% of people choose based on intuition. That's why you can enter the top 10% just by choosing based on numbers.
- Tier-S Niche Sniper: Sifting through genres on 3 axes
Genres that survive in high-ticket marketing are mostly determined by three axes: unit price, approval rate, and search demand. If you vaguely choose "side hustle" or "beauty," you'll hit a red ocean, low prices, or a landmine with a 3% approval rate. Let Claude Code score these and keep only the top ones.
Prompt:
``
You are a market analyst for high-ticket affiliate marketing.
Please score the following genre candidates on a 10-point scale across 3 axes:
1. Average unit price (10,000 yen or more = high score)
2. Approval rate (50% or more = high score)
3. Balance between monthly search volume and competitor density
Add a one-line rationale for each axis and list them in descending order of total score.
Finally, leave only genres with a total of 24 points or more as "entry candidates."
Genre candidates: [ ]
``
- ASP Cross-Crawl: Comparing the same product across multiple ASPs
Even for the same product, the unit price and approval rate can vary by more than double depending on the ASP. Checking this manually every time is a waste of time, so let Claude Code create a cross-reference list. You'll find deals where the reward doubles for the same effort.
Prompt:
``
For the following product names, cross-search major ASPs like A8/Moshimo/afb/Access Trade/ValueCommerce/Cats and present them in a table.
Columns: "ASP Name / Unit Price / Approval Conditions / Estimated Approval Rate / Availability of Special Price Negotiation."
Write "Unknown" for items where the source cannot be confirmed; do not fill with guesses.
Product name: [ ]
``
- LTV Reverse Calculator: Back-calculating from "maintaining for 12 months"
You fail if you start with dreams; you continue if you start with numbers. Instead of "I want to earn 500k," break it down to "how much unit price, how many deals per month, and how much traffic." Furthermore, since we are building on the premise of "maintaining for 12 months," we also look at low dependency on a single deal. This is the moment vague aspirations turn into today's tasks.
Prompt:
``
Based on the premise of "stably maintaining 500,000 yen in monthly sales for 12 months,"
provide 5 realistic combination patterns.
For each pattern, create a table for the following:
- Assumed unit price
- Required number of conversions (monthly)
- Required traffic (at 3 levels of conversion rate: 1%/3%/5%)
- Assumed number of articles (with average monthly PV per article at 500/1500/3000)
- Impact amount (%) if the most dependent deal ends
Finally, rank them in order of low dependency on a single deal and ease of continuing for 12 months.
``
- Sustainable Demand Index: Quantifying sustained demand
Even if demand is large, a trendy genre that disappears in 3 months won't be "stable." To continue 500k for a year, you need to choose a genre where demand is constant, seasonal fluctuations are small, and the increase in competitor density after entry is moderate. Choose a place where you can run with a steady wind, not a momentary gust.
Prompt:
``
Score the following keyword groups on 3 axes:
1. Demand Score (0-10: Monthly search volume)
2. Saturation Score (0-10: Strength of competition)
3. Sustainability Score (0-10: High score if Google Trends shows a flat or upward trend over the last 3 years)
Calculate the index as "Demand ÷ Saturation × Sustainability" and list them in descending order.
For the top 5, write 3 lines each for:
- "Rationale for being able to maintain a top position stably for 12 months"
- "Countermeasures for expected competitor movements"
Keyword groups: [ ]
``
The common point of this chapter is to stop choosing by intuition and start choosing by numbers. More than half the difference between those who stabilize 500k for 12 months and those who disappear after a peak is decided by this selection before writing. Spending 30 minutes here determines whether the hundreds of hours that follow will be rewarded or wasted.
Chapter 2: Persona and Offer Design: Deeply targeting just one person
"Selling to everyone" doesn't sell. "Stabbing one person deeply" ends up reaching many. This chapter uses Claude Code to concretize that "one person" and build an offer that resonates only with them.
- N=1 Persona Generator: Generating one realistic person
We thoroughly create one person, including name, age, occupation, annual income, layer of worry, catchphrases, search times, and even the moment they hesitate to pay. "A male office worker in his 30s" moves no one's heart.
Prompt:
``
Generate one typical customer who would buy in the following genre, with a resolution that makes them seem real.
- Name/Age/Gender/Location
- Occupation/Annual Income/Family Structure/Hobbies
- 5 recent worries from the last 3 months
- 3 keywords searched immediately before purchase
- 3 reasons for hesitating to pay
- 3 triggers that push the payment
Generalities are prohibited; write with specific names.
Genre: [ ]
``
- Pain-Stack Mapping: Stacking worries in 5 layers
People don't move based on surface-level worries. The true motivation lies three layers down. Deconstruct into five layers: surface, middle, deep, fear, and ideal, and decide where to strike. Copy that misses this will not resonate, no matter how well-formatted.
Prompt:
``
Structure the worries of this persona into the following 5 layers:
1. Surface (Worries the person usually voices)
2. Middle (Dissatisfaction not yet verbalized)
3. Deep (Root cause)
4. Fear (Worst-case scenario if left unaddressed)
5. Ideal (What they truly want to become)
Write 3 for each layer, and finally, propose which layer should be targeted in the copy, with rationale.
``
- Offer-Market Fit Test: Scoring the alignment between offer and market
Even if you find a deal, selling it as-is won't work. How well do the persona's deep worries and the deal's benefits align? Let Claude Code score it; if it doesn't fit, redesign the offer.
Prompt:
``
Read the following persona information and deal information, and score the alignment between the two on 10 items.
Score each item out of 10 and give a total.
- Under 70 points -> "Pivot recommended"
- 70-85 points -> "Offer redesign"
- 85 points or more -> "Execute immediately"
Write the reasons for the judgment in bullet points.
Persona: [ ]
Deal: [ ]
``
- Counter-Offer Generator: Creating the opposite of existing LPs to differentiate
You can't win as a latecomer with the same appeal as rivals. Let the AI read the claims of existing LPs and generate "what would be the appeal from the exact opposite angle." When everyone says right, you have a justifiable left. This is the entry point for latecomers to strike.
Prompt:
``
Read the following rival LP and extract 3 main appeal points.
Next, for all 3, generate an "appeal that can be justified from the exact opposite angle."
For each proposal, also list:
- What kind of reader segment it will resonate with
- Precautions for implementation
Rival LP: [URL or text]
``
Once the persona and offer are decided, writing articles or SNS posts becomes just a means. Only those who have solidified who they are selling to, what they are selling, and why can rotate the mass production from here on without hesitation.
Chapter 3: Article Mass Production: One draft a day accumulates automatically
From here, it's about "quantity." Even with high-ticket items, you eventually need a certain amount of traffic. If you increase 30k PV to 60k PV, revenue grows at a similar rate. It's impossible for humans to keep producing this volume, so we hand it to Claude Code.
Once you cross this, the daily weight of "having to write" disappears.
- SERP Reverse Engineering: Deconstructing and reconstructing top articles
Analyze why the top articles are at the top and extract the structure. Then, generate a framework that fills in the questions and loopholes no one else has written about.
Prompt:
``
Analyze the titles and heading structures of the top 10 search results for the following keyword.
1. Common heading structure
2. Top 5 points often mentioned
3. 5 questions/loopholes written nowhere
Based on this information, provide a heading proposal with a unique structure to beat the top results (H2x6, H3x3 each).
Keyword: [ ]
``
- Skeleton-Then-Fill: Creating skeletons in parallel and fleshing them out
If you let the AI write from the top, it will definitely lose steam in the second half. List the heading skeleton first, and flesh out each heading as an independent task. Since Claude Code can use Subagents (parallel workers), if you run each section as a separate task simultaneously, both quality and speed will stabilize.
Prompt:
``
For the following theme, list all heading skeletons (H2x5, H3x3 for each H2) first.
Next, treat each H2 as an independent task (Subagent) and flesh out each section with 800 characters.
Finally, integrate the whole and present a completed version with logical overlaps and contradictions removed.
Theme: [ ]
``
- E-E-A-T Injection: Inserting experience and expertise later
Articles written by AI may have a high average score but lack "experience." Articles evaluated by Google always contain details of real experiences. Let the AI insert these after the fact.
Prompt:
``
Evaluate the following draft article from the perspective of E-E-A-T.
1. Experience
2. Expertise
3. Authoritativeness
4. Trustworthiness
List 3 missing points for each perspective, generate specific sentences (numbers, proper nouns, real experience episodes) to insert there, and indicate the position with "Insert here."
Draft: [ ]
``
- Internal Link Web Designer: Designing internal link networks structurally
As articles increase, internal link design stalls. Provide the sitemap and URL list and let the AI build it with a topic cluster structure.
Prompt:
``
Read the following URL list and title list for each article, and design an internal link map with a topic cluster structure.
Classify into "Pillar Articles," "Cluster Articles," and "Satellite Articles," and provide a table of up to 5 internal links to be placed from each article. Also include anchor text proposals.
URL list: [ ]
``
- Comparative Review Stack: Mass-producing comparative reviews structurally
High-ticket affiliates and comparative reviews are a perfect match. Mass-produce articles comparing 3 to 5 products in the same genre with a fixed structure.
Prompt:
``
Generate 3 comparative review articles with the same structure for the following 3 products.
Change the main product for each article, and have the other 2 appear as "comparison targets."
Article structure is fixed:
- Introduction (150 characters)
- General review of the main product
- Comparison table on 3 axes (Price, Effect, Support)
- People the main product resonates with / doesn't resonate with
- Conclusion
Product list: [ ]
``
By this point, you'll be close to a state where "one draft is produced every day." If you proceed to the Routines in Chapter 8, you can run this completely unattended.
Chapter 4: SNS Funnel: Automatically connecting the X -> note -> LINE baton
To stabilize 500k, relying only on search traffic is unstable. Generate traffic on X, warm them up on note, and close on LINE. Automatically connect the batons of this three-stage relay. The feeling of the list accumulating is the very feeling of revenue stabilizing.
- Hook Library Builder: Extracting appeal patterns from past viral posts
Extract only the posts that grew and have the AI extract common "hook types." This becomes your own dedicated collection of appeal templates.
Prompt:
``
Analyze the following list of X posts (engagement rate 3% or higher) and extract 10 types of common "hook syntax."
For each syntax, provide a table of:
- Situations where it's used
- Expected reaction
- General template (fill-in-the-blank format)
Post list: [ ]
``
- Thread-to-Article Bridge: Automatically generating CTAs from X threads to note
How to naturally send readers interested on X to note. 80% of the inflow rate is decided here. Let the AI generate 3 patterns of transition text at the end of the thread.
Prompt:
``
Read the content of the following X thread and generate 3 patterns of natural CTAs to a note article.
1. "Curiosity-driven" type
2. "Verbalizing reader's worry" type
3. "Benefit-explicit" type
Each within 140 characters, no hard sell, and written on the premise of "placing a link to the note article" in the last sentence.
Thread: [ ]
``
- Lead Magnet Generator: Mass-producing LINE registration benefits
The lifeline of high-ticket affiliates is the LINE list. Mass-produce "benefits" that serve as motivation for registration for each persona. Just by changing the benefit, the registration rate can change several times over.
Prompt:
``
Generate 10 lead magnet ideas to encourage LINE registration for the following persona.
For each idea, list:
- Benefit name (within 20 characters)
- Summary of content
- Assumed registration rate
- Time required for production
Finally, rank them by production cost-effectiveness.
Persona: [ ]
``
- Follow-up DM Script: Automatically generating follow-up LINEs after registration
Half of the drop-offs are decided in the first 3 days after registration. Generate a scenario of 3 to 5 messages tailored to the persona and deal.
Prompt:
``
Generate 5 follow-up LINE messages after registration for the following persona.
1st message = Immediate thanks + expectation setting
2nd message = Empathy story
3rd message = Comparison with other companies/self-study
4th message = Social proof
5th message = Offer presentation
Structure the beginning of each message so it can branch based on sticker reactions.
Persona: [ ]
Deal: [ ]
``
Chapter 5: Automated AB Testing of Sales Copy: Measuring with 5 instead of betting on 1
Polishing one LP is important, but the perfect form doesn't come out from the start. Claude Code is good at "creating multiple variants with the same appeal." So run 5 and keep only the winner. Change the task from trying to hit the mark to a task of drawing the winning ticket.
- Variant Spawner: Generating 5 patterns for the same LP
Just by changing one word in the first view, CVR can double or triple. Writing them manually is a waste of time, so let the AI output them at once.
Prompt:
``
Read the following LP text and generate 5 variants changing only the "Catchphrase + Sub-copy + CTA button text" of the first view.
Clearly change the appeal axis for each variant.
Example: Fear appeal / Gain appeal / Authority appeal / Empathy appeal / Urgency appeal
For each proposal, also list the reader segment it will resonate with.
LP text: [ ]
``
- Objection Handler: Automatically multiplying the rebuttal part
Drop-offs happen in specific places: "It's expensive," "I'm worried if I can do it," "Not right now." Let the AI write the handling for these three major objections in the persona's words.
Prompt:
``
For the following deal, list 10 assumed objections before purchase and generate rebuttal text for each.
Structure it to first accept the reader's anxiety without being pushy, then resolve it with data or examples.
Each rebuttal should be within 200 characters.
Deal: [ ]
``
- Urgency Calibrator: Adjusting urgency and scarcity with numbers
Too much hype is counterproductive; too little doesn't move people. Specify the intensity as low, medium, or high, and find the sweet spot that works for your readers.
Prompt:
``
For the following offer, write the presentation of urgency and scarcity at 3 levels: low, medium, and high.
Generate 3 patterns of text around the CTA for each level, and also list the expected reaction (increase/decrease in conversion rate / presence of aversion).
Offer: [ ]
``
- Story Arc Injector: Building story-type LPs structurally
The higher the price, the less logic alone sells. You need a story that moves emotions. Generate an arc to insert at the beginning of the LP using a template structure.
Prompt:
``
Generate a story arc that fits the following persona and deal.
Structure is 6 stages: Daily life -> Incident -> Conflict -> Encounter -> Transformation -> Success
200 characters per stage, one character, clearly indicate emotional ups and downs.
Finally, propose where in the LP this story should be inserted.
Persona: [ ]
Deal: [ ]
``
Chapter 6: Polishing the Prompt Content: Stabilizing output quality every time
By now, "what to make" is decided. This chapter covers 5 patterns to stabilize the quality of that work every time. All work just by how you write the prompt. Mastering this changes Claude Code's output from "occasionally good" to "always usable."
- Output-First Specification: Fixing the template of the final form first
If you just say "write an article," the output fluctuates. If you build the template of the final form first and have it fill in the blanks, the fluctuation disappears.
Prompt:
``
Fill in the following template perfectly.
Title: [Within 40 characters, including numbers]
Introduction: [3 reader worries, 1 sentence each]
Body: [H2x3 + 300 characters each]
Conclusion: [1 action proposal]
CTA: [Within 15 characters]
Theme: [ ]
``
- Negative Constraints: List of NGs to remove the AI smell
"In natural writing" is vague and not followed. If you provide a specific list of NGs, the AI smell almost disappears.
Prompt:
``
Create the following. Strictly observe prohibitions.
1. Prohibit "About..." and "It is important to..."
2. Prohibit consecutive use of 3-character kanji compounds
3. Prohibit opening greetings
4. Prohibit escaping with bulleted lists
5. Prohibit using the same sentence ending 3 times in a row
If violated, rewrite the entire text.
Target: [ ]
``
- XML Structured Tagging: Separating information with tags
If you pass goals, background, constraints, examples, and output format in one lump, the AI loses track of priorities. Just by partitioning with tags, Claude Code's comprehension accuracy goes up a notch.
Prompt:
``
I will instruct with the following structure. Please answer according to the content of each tag.
<goal>Goal to achieve</goal>
<context>Background information</context>
<constraints>Prohibitions</constraints>
<examples>Reference examples</examples>
<output_format>Output format frame</output_format>
``
- Self-Refine: Running generation, criticism, and revision in one go
If you review your own writing, you won't see the flaws. Let Claude Code play 3 roles and complete generation, harsh scoring, and revision in one response.
Prompt:
``
For the following topic, do all 3 steps in one response.
1. Write the first draft
2. As a harsh editor, score on 5 perspectives: persuasiveness, uniqueness, logic, readability, and omissions
3. Write a revised version based on the scoring
Topic: [ ]
``
- Calibrated Confidence Prompting: Making it explicit about confidence levels
In high-ticket marketing, one factual error loses trust. Having it list the confidence level for each claim makes judging reliability much easier.
Prompt:
``
When answering the following question, always include a "confidence level 0-100%" for each claim.
- Under 50% -> "Speculation"
- 70% or more -> "Fact"
Label them and provide a one-line rationale for each confidence level.
Question: [ ]
``
Chapter 7: Adding Hands and Feet with MCP: 4 to include
MCP (Model Context Protocol) is a standard for connecting AI to external services. Including this changes Claude Code from a "coding tool" to an "agent that moves reality." I've strictly selected 4 necessary for high-ticket affiliates. Everything up to here was preparation; from here, Claude Code really starts moving.
- Firecrawl MCP: Turning rival LPs and sites into Markdown entirely
Letting Claude Code read rival LPs directly makes analysis much faster.
Prompt:
``
Turn the following URL into Markdown with Firecrawl, apply Technique 8 (Counter-Offer Generator) from Chapter 2, and provide 3 LP structure proposals appealing from the exact opposite angle.
URL: [ ]
``
- Supadata MCP: Extracting appeal elements from videos in one shot
This is an MCP that passes video transcripts to Claude Code. Deconstruct the structure of growing videos and divert them to your own LPs or threads.
Prompt:
``
Extract the transcript from the following video URL and extract 5 "hook syntaxes" and "drop-off avoidance points" that are generating audience retention.
URL: [ ]
``
- Memory MCP: Giving Claude Code permanent memory
Normally, memory disappears when a session ends. With Memory MCP, personas, deals, and verification results are made permanent, eliminating the task of "re-pasting premises every time." There's also a mechanism where placing CLAUDE.md in the project root automatically loads it, so combining both makes it even stronger.
Prompt:
``
Register the following information in permanent memory.
1. Genre being handled [ ]
2. Main persona [ ]
3. Active deals and unit prices [ ]
4. NG appeal axes [ ]
In future sessions, always refer to this first before working.
``
- Notion/Sheets MCP: Unifying deal management and revenue data
Let Claude Code directly interact with Notion or spreadsheets for deal unit prices, monthly occurrences, approval rates, and confirmed amounts. Everything from data updates to analysis and the next move is connected.
Prompt:
``
From the Notion DB "Deal Management," retrieve the number of occurrences and confirmed cases for the current month, and aggregate by ASP/deal.
Extract deals whose approval rate has dropped in the last 3 months and formulate 3 causal hypotheses for each.
``
Reading, remembering, moving. The moment these three are aligned, Claude Code starts moving on its own with hands and feet.
Chapter 8: Fully Automated Operation with Claude Code Routines: Once made, it moves while you sleep
Claude Code has a /schedule command (Routines). It's a schedule-execution type automation that runs on Anthropic's cloud side. Even if your PC is off, Claude Code keeps running your side hustle.
Combining the 5 introduced here starts everything moving on its own: daily drafts, daily SNS posts, weekly ranking monitoring, monthly revenue reports, and automatic detection of underperforming articles. This is where everything in this article connects into one.
- Daily Article Drafter: A draft is placed on your desk every morning
Create the following Routine with Claude Code's /schedule.
[Schedule] Every day at 6:00 AM
[Prompt]
- Retrieve 1 keyword for today from
./keyword-queue/ - Analyze the heading structure of the top 10 SERP results for that keyword (Chapter 3, Tech 9)
- Generate a 4,000-character draft based on the structure
- Save as
./drafts/YYYY-MM-DD.md - Notify completion via Slack MCP
- Daily SNS Cannon: Automatic generation and scheduling of X posts
Create the following Routine with Claude Code's /schedule.
[Schedule] Every day at 7:00 AM
[Prompt]
- Refer to 10 viral posts (engagement rate 3% or higher) in
./content-bank/ - Choose 3 syntax patterns and generate 3 X posts for today's theme
- Insert a natural CTA to a note article in each post
- Send API to a scheduled posting tool
- Notify the completion list via Slack
- Weekly SERP Watcher: Weekly automatic monitoring of ranking fluctuations
Create the following Routine with Claude Code's /schedule.
[Schedule] Every Monday at 9:00 AM
[Prompt]
- Retrieve rankings for all active keywords (
./keywords.csv) - Extract keywords that dropped by 5 or more positions compared to the previous week
- Analyze differences with top articles (heading structure/word count/E-E-A-T elements)
- Report as rewrite candidates in order of priority
- Notify via Slack
- Monthly P&L Reporter: Automatic generation of monthly revenue reports
Create the following Routine with Claude Code's /schedule.
[Schedule] 1st of every month at 8:00 AM
[Prompt]
- Retrieve all data for the previous month from the "Deal Management" DB via Notion MCP
- Aggregate occurrence amount/confirmed amount/approval rate/contribution by deal
- Generate graphs for month-over-month/year-over-year comparison
- Extract 3 highlights, 3 issues, and 3 priority actions for next month
- Save as
./reports/YYYY-MM.mdand notify via Slack
- Failure Detection Loop: Automatically detecting and proposing fixes for poorly performing articles
Create the following Routine with Claude Code's /schedule.
[Schedule] Every Friday at 10:00 AM
[Prompt]
- Retrieve PV/CV data for the last 30 days from access analysis
- Extract articles whose PV dropped by 30% or more compared to the last 90 days
- Summarize causal hypotheses (SEO/trend/competitor), 3 directions for revision, and man-hour estimates for each article
- Notify via Slack in order of priority
When these 5 start rotating, it's true automated operation. What remains for you is deciding strategy, giving the final polish to drafts, and deciding the next move by looking at monthly reports. Just these three. You move from the side being chased by work to the side watching the work.
Chapter 9: Pitfalls Unique to High-Ticket Affiliates: 4 to crush before automating
Automation is powerful, but automating with a weak design only mass-produces mistakes at high speed. We'll crush 4 pitfalls that frequently occur in high-ticket marketing beforehand. Those who skip this will have everything stop in six months.
- Compliance Guard: Automating checks for Pharmaceutical Affairs Law and Premiums and Representations Act
High-ticket genres like beauty, health, finance, and career change have strict expression regulations. Violations can lead not only to deal suspension but also administrative guidance. Always have them checked before publishing. Using Claude Code's Hooks feature, you can also run this automatically upon saving.
Prompt:
``
Read the following article and extract all risky expressions from the 3 perspectives of the Pharmaceutical Affairs Law, Premiums and Representations Act, and Specified Commercial Transactions Act.
For each expression, provide a table of:
- Applicable law
- Violation level (High/Medium/Low)
- Alternative expression proposal
Point out gray zones without hesitation.
Article: [ ]
``
- Approval-Rate Optimizer: Article structure that doesn't lower approval rates
In high-ticket marketing, ASPs judge approval or denial. If the approval rate drops, even if apparent sales look good, the confirmed amount can be half. To "stabilize" 500k, you must defend this.
Prompt:
``
Evaluate the following article on 5 perspectives for risks that lower the approval rate.
1. Presence of induced misclicks
2. Presence of excessive exaggeration
3. Exposure design to non-targets
4. Inappropriate implementation of AB tests
5. Unclear drop-off paths before application
Score each item out of 10; if the total is under 30, judge as "Fix before publishing."
Article: [ ]
``
- ASP Diversification: Design to break away from 1-deal dependency
The biggest enemy to "stabilizing 500k for 12 months" is dependency on a single deal. If 80% of sales depend on one deal, sales drop to zero the moment that deal ends. Build a design that diversifies into 3 to 5 deals as a portfolio.
Prompt:
``
Evaluate the following current deal structure from the perspective of risk diversification.
1. Sales dependency of each deal
2. Dependency by ASP
3. Dependency by genre
Calculate the impact amount if the most dependent deal ends, and propose 5 similar deals as supplementary candidates.
Status: [ ]
``
- Tier Migration Plan: Gradual transition from low-ticket to high-ticket
It's hard to get results with high-ticket items from the start. Have the AI create a 3-stage blueprint: build a track record with low-ticket, stabilize with medium-ticket, and move to high-ticket.
Prompt:
``
For the following persona and genre, design a roadmap to transition deal unit price bands in 3 stages: "Low (~3,000 yen) / Medium (3,000-15,000 yen) / High (15,000 yen~)."
For each stage, provide a table of:
- Deal types to handle
- Required number of articles
- Required verification period
- Criteria for moving to the next stage
Persona: [ ]
Genre: [ ]
``
Chapter 10: 3 Habits You Should Quit: Growing by subtraction
The last chapter is about subtraction. The difference between those who grow by automating with Claude Code and those who don't lies not in the content of automation, but in the habits they can quit.
- Stop "thinking after writing 100 articles"
This was a classic rule, but it's different now. Before 100 articles, find a winning pattern with 5 articles and increase only that winning pattern. Thanks to Claude Code, we are in an era where we can quickly verify 5 articles. Always design the metrics to measure before you start writing.
- Stop "one-off automation"
Even if you automate only article generation, if SNS, analysis, and reporting are manual, the bottleneck just moves there. Automation works because you combine it to flow through the whole. Move at least 3 of the 5 in Chapter 8 as a set.
- Stop "skipping human final checks"
This is the most important. As automation progresses, you'll want to skip human eyes before publishing. Please absolutely stop this. Claude Code almost never makes average mistakes, but it makes fatal mistakes a few times a year. Compliance violations, incorrect numbers, typos in deal names. All can be prevented by a 30-second visual check before publishing. Humans hold the final gate. This is the decisive difference between those who earn for a long time and those who disappear in the short term.
Conclusion: 500k monthly is built by "design," not "magic"
I've introduced about 40 steps all at once. What I want to say one last time is the first line I wrote. Stabilizing 500k for 12 months is something built by design, not magic.
Choose a deal in Chapter 1, design persona and offer in Chapter 2, mass-produce articles in Chapter 3, build SNS funnels in Chapter 4, run AB tests on sales copy in Chapter 5, improve prompt quality in Chapter 6, plug in MCP in Chapter 7, hand over to Routines in Chapter 8, crush pitfalls in Chapter 9, and discard old habits in Chapter 10.
All 10 chapters share only one idea: minimize the work humans do and maximize the work Claude Code does.
While you are sleeping, drafts are being made, SNS posts are going out, rankings are being monitored, reports are being written, and poorly performing articles are being found. When you wake up, all you do is judge the direction and check the final gate. This is the form of work when stabilizing 500k for 12 months.
And if you start today, the first step is already decided. Just copy and paste the prompt for Technique 3, LTV Reverse Calculator in Chapter 1, and throw it at Claude Code or ChatGPT. In 15 minutes, you'll see how the content of 500k is broken down into unit price, number of conversions, and traffic. Once you see the combination you can realistically aim for, all you have to do is proceed through this article in order.
Thank you for reading to the end.
To you who have read this far
Those who have read this far are probably the top 3%. Those who can actually move from there are further 1% of that.
Honestly, at the point you could read such a long article to the end, I don't think you are just on the "side that collects information" anymore.
For such people, I have decided to distribute the "Complete Blueprint for Claude Code Automated Operation to Stabilize 500k for 12 Months," which I couldn't write in the main text, and a free lecture where I talk directly about that logic, via LINE.
From the lecture participants, I will also guide limited to 30 people to individual roadmap creation.
Register on LINE -> answer a survey that ends in 10 seconds, that's all.
