home / skills / yoanbernabeu / producthunt-skills / ph-algorithm-guide

ph-algorithm-guide skill

/skills/compliance/ph-algorithm-guide

This skill helps you optimize Product Hunt launches by applying known ranking factors to craft timing, engagement, and quality-focused strategies.

npx playbooks add skill yoanbernabeu/producthunt-skills --skill ph-algorithm-guide

Review the files below or copy the command above to add this skill to your agents.

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---
name: ph-algorithm-guide
description: Understand how the Product Hunt ranking algorithm works. Use this skill to optimize your launch strategy based on known algorithm factors and behaviors.
---

# Product Hunt Algorithm Guide

This skill explains how Product Hunt's ranking algorithm works, helping you optimize your launch strategy based on publicly known factors.

## When to Use This Skill

- Planning your launch strategy
- Understanding why rankings change
- Optimizing for algorithm factors
- Diagnosing ranking issues
- Setting realistic expectations

## Algorithm Fundamentals

### Key Insight
**Upvotes ≠ Points**

Product Hunt CTO Mike Kerzhner confirmed: "There is not a 1:1 correspondence between upvotes and points."

### What This Means
- Not all votes count equally
- Account quality matters
- Engagement quality matters
- Timing patterns matter

## Known Ranking Factors

### Factor 1: Vote Weight

**Higher Weight Votes:**
- Older accounts (months/years old)
- Active accounts (regular engagement)
- Diverse activity (not just voting)
- Organic voting pattern

**Lower Weight Votes:**
- New accounts (recently created)
- Inactive accounts (created but unused)
- Single-purpose accounts
- Suspicious patterns

**Potentially Discounted:**
- Brand new accounts
- Accounts created same day
- Bulk votes from same source
- Coordinated voting patterns

---

### Factor 2: Engagement Depth

**Positive Signals:**
- Thoughtful comments
- Discussion threads
- Maker responses
- Question-answer exchanges

**Why It Matters:**
- Comments indicate genuine interest
- Discussions show community value
- Engagement harder to fake than votes

---

### Factor 3: Velocity Pattern

**What Algorithm Watches:**
- Rate of upvote accumulation
- Time distribution of votes
- Spikes vs steady growth
- Natural vs artificial patterns

**Healthy Pattern:**
```
Hour 1: [████████░░] 40 votes
Hour 2: [██████░░░░] 35 votes
Hour 3: [███████░░░] 38 votes
Hour 4: [█████████░] 45 votes
```

**Suspicious Pattern:**
```
Hour 1: [██████████] 150 votes (spike!)
Hour 2: [█░░░░░░░░░] 5 votes
Hour 3: [█░░░░░░░░░] 3 votes
Hour 4: [█░░░░░░░░░] 2 votes
```

---

### Factor 4: First 4 Hours

**Special Period:**
- Rankings randomized initially
- Vote counts hidden publicly
- Algorithm observing patterns
- Critical for initial position

**After 4 Hours:**
- Rankings become vote-based
- Position reflects accumulated strength
- Top positions attract organic traffic
- Momentum becomes visible

---

### Factor 5: Account Relationships

**Flagged Patterns:**
- Votes from connected accounts
- Same IP address votes
- Same device votes
- Employee/team votes (weighted less)

**Clean Patterns:**
- Diverse geographic sources
- Independent account histories
- Organic discovery paths

## How Rankings Are Determined

### The Daily Cycle

```
12:01 AM PST → Day begins
    ↓
Hours 0-4: Randomized ranking
    ↓
Hour 4+: Algorithm-sorted ranking
    ↓
Throughout day: Continuous re-ranking
    ↓
11:59 PM PST → Final rankings locked
    ↓
Awards: POTD, Top 5, etc.
```

### Ranking Formula (Approximate)

```
Score = (Weighted Votes × Quality Multiplier)
      + (Engagement Depth Bonus)
      - (Spam/Manipulation Penalty)
```

Where:
- Weighted Votes = Sum of all votes adjusted by account quality
- Quality Multiplier = Based on product profile completeness
- Engagement Depth = Comments, discussions, maker activity
- Penalty = Deductions for suspicious patterns

## Optimizing for the Algorithm

### Do: Quality Over Quantity

**Instead of:**
Getting 200 votes from low-quality accounts

**Aim for:**
Getting 100 votes from active, established accounts

### Do: Stagger Engagement

**Instead of:**
All supporters voting at 12:01 AM

**Aim for:**
Supporters spread across 5-6 waves over 24 hours

### Do: Encourage Real Comments

**Instead of:**
"Please upvote!"

**Aim for:**
"Would love your honest thoughts in the comments!"

### Do: Respond to Everything

**Why:**
- Shows you're present
- Creates discussion threads
- Signals genuine launch
- Builds engagement depth

## Algorithm Behaviors

### What Triggers Scrutiny

1. **Vote Velocity Spikes**
   - Sudden burst of votes
   - Then dramatic dropoff
   - Unnatural acceleration

2. **Account Patterns**
   - Multiple new accounts
   - Same creation timeframe
   - Similar activity patterns

3. **Geographic Clustering**
   - All votes from one location
   - No geographic diversity
   - Pattern doesn't match product

4. **Timing Uniformity**
   - Votes in exact intervals
   - Automated-looking patterns
   - Unnatural consistency

### What the Algorithm Rewards

1. **Organic Growth**
   - Steady accumulation
   - Natural peaks and valleys
   - Timezone-appropriate waves

2. **Diverse Sources**
   - Various account ages
   - Different activity levels
   - Geographic spread

3. **Deep Engagement**
   - Multiple comments
   - Discussion threads
   - Question-answer pairs

4. **Maker Presence**
   - Quick responses
   - Genuine conversation
   - Helpful attitude

## Featured vs Unfeatured

### Getting Featured

**Requirements (Unofficial):**
- Product is clearly explained
- Meets category standards
- No obvious manipulation
- Complete profile

**Helps Your Chances:**
- Quality visuals
- Clear value proposition
- Active maker engagement
- Previous PH presence

### Getting Unfeatured

**Common Causes:**
- Vote manipulation detected
- Spam reports received
- Policy violations
- Low-quality product

**Recovery:**
- Usually not possible same day
- Contact support (respectfully)
- Learn for next time

## Realistic Expectations

### What You Can Control
- Quality of your product
- Quality of your assets
- Your community engagement
- Your response rate
- Your outreach authenticity

### What You Can't Control
- Competitor strength
- Algorithm behavior
- Vote weighting details
- Featuring decisions
- Final ranking

### Healthy Mindset
```
Focus on: Building something people love
Not on: Gaming the system

Focus on: Genuine community
Not on: Vote numbers

Focus on: Long-term reputation
Not on: One-day ranking
```

## Algorithm Myths Debunked

### Myth: "Having a famous hunter guarantees success"
**Reality:** 79% of featured products are self-hunted. Hunter followers help awareness but don't guarantee votes.

### Myth: "More votes always means higher rank"
**Reality:** Vote quality matters more than quantity. 50 high-weight votes can beat 100 low-weight votes.

### Myth: "The first hour determines everything"
**Reality:** First 4 hours matter, but the entire 24 hours count. Late momentum can overcome slow starts.

### Myth: "Weekend launches are easy wins"
**Reality:** Lower competition, but also lower traffic. Easier badge, fewer users.

### Myth: "The algorithm is random/unfair"
**Reality:** It's designed to surface genuinely interesting products. Work with it, not against it.

## Output Format

```
ALGORITHM OPTIMIZATION CHECK

VOTE QUALITY:
- Expected high-weight votes: [Number]
- Expected low-weight votes: [Number]
- Risk of discounted votes: [Low/Medium/High]

ENGAGEMENT PLAN:
- Comment depth strategy: [Description]
- Maker response plan: [Description]
- Discussion seeding: [Description]

VELOCITY PATTERN:
- Wave 1 timing: [Time]
- Wave 2 timing: [Time]
- Expected distribution: [Natural/Concerning]

RISK FACTORS:
- [Risk 1]: [Mitigation]
- [Risk 2]: [Mitigation]

REALISTIC TARGETS:
- Conservative estimate: [Rank range]
- Optimistic estimate: [Rank range]
```

Overview

This skill explains how the Product Hunt ranking algorithm works and how to shape a launch strategy around known, publicly observed factors. It translates signals like vote weight, engagement depth, and velocity into concrete tactics you can apply on launch day. Use it to set realistic targets and reduce risk of algorithm penalties.

How this skill works

The skill inspects publicly reported algorithm behaviors and synthesizes them into actionable checks: vote quality, engagement patterns, timing/velocity, and account relationships. It models the daily cycle (randomized first 4 hours, then algorithmic ranking) and produces optimization recommendations and risk flags. Outputs focus on what to encourage (diverse, thoughtful engagement) and what to avoid (spikes, coordinated votes).

When to use it

  • Planning a Product Hunt launch strategy
  • Diagnosing unexpected rank changes during or after launch
  • Designing outreach and supporter timing waves
  • Preparing maker response and comment plans
  • Setting realistic ranking and engagement targets

Best practices

  • Prioritize quality over raw vote count — target established, active accounts
  • Stagger supporter activity in multiple waves over 24 hours rather than all at once
  • Encourage genuine comments and conversations, then respond quickly to build depth
  • Diversify geographic and account-age sources to avoid clustering signals
  • Avoid coordinated bulk voting, same-IP/device patterns, and obvious automation

Example use cases

  • Create an engagement schedule with 4–6 supporter waves across the day
  • Audit supporter list to estimate high-weight vs low-weight votes before launch
  • Build a comment and maker-response plan to increase engagement depth
  • Run a pre-launch risk checklist to catch geographic or timing clustering
  • Estimate realistic rank ranges (conservative and optimistic) given expected vote quality

FAQ

Do upvotes equal ranking points?

No. Upvotes are weighted by account quality and other signals, so raw counts do not map 1:1 to ranking points.

How important are the first four hours?

They are critical for observation: rankings are randomized then and the algorithm watches patterns. But momentum across the full 24 hours still matters.

Can you recover from a manipulation penalty same day?

Usually not. If flagged, best course is respectful support contact and focusing on clean signals for future launches.