Forum Monitor

Overview

Systematic workflow for monitoring online communities to discover recurring user pain points. Transforms unstructured forum discussions into ranked, evidence-backed opportunity reports suitable for automated app generation pipelines.

Core principle: Pain points with high frequency and high engagement are the strongest product signals. Monitor, classify, rank, report.

When to Use

• Discovering product opportunities from community feedback
• Running periodic (daily/weekly) market research scans
• Identifying trending complaints or feature requests
• Competitive intelligence gathering from user discussions
• Feeding the app-generation-workflow with validated pain points

Cron Integration

This skill is designed to run on a schedule via CronCreate:

CronCreate({
  name: 'weekly-forum-scan',
  schedule: '0 9 * * MON', // Every Monday at 9 AM
  prompt:
    'Run forum monitor scan for [target domain]. Invoke Skill({ skill: "forum-monitor" }). Save report to .claude/context/reports/backend/',
});

Workflow

Step 1: Configure Target Forums

Define the forums to monitor based on the target domain:

Forum Selection Matrix:

Forum	Best For	Query Pattern
Reddit	Consumer pain points, UX issues	site:reddit.com <topic> frustrating OR wish
Hacker News	Developer tools, B2B SaaS	site:news.ycombinator.com <topic>
ProductHunt	New product gaps, feature envy	site:producthunt.com <topic>
Indie Hackers	Solo dev pain points, pricing	site:indiehackers.com <topic>
Dev.to	Developer workflow friction	site:dev.to <topic> pain OR annoy

Query Templates:

"{topic} frustrating OR annoying OR wish OR need OR missing"
"{topic} alternative to OR better than OR looking for"
"{topic} feature request OR roadmap OR please add"

Step 2: Scrape and Collect

For each configured forum, execute searches and extract content:

// Search for pain points
WebSearch({ query: 'site:reddit.com {topic} frustrating OR wish OR need 2026' });

// Fetch specific threads with high engagement
WebFetch({
  url: '{thread-url}',
  prompt:
    'Extract all complaints, feature requests, and pain points. For each, note the exact quote, upvote count, and whether others agreed.',
});

Collection Requirements:

• Minimum 20 posts per forum per scan
• Include posts from the last 30 days (or configurable window)
• Capture: title, URL, community, engagement (upvotes + comments), date, key quotes

Step 3: Classify Pain Points

Categorize each collected item into one of these categories:

Category	Signal Words	Example
missing-feature	"wish", "need", "please add", "roadmap"	"I wish Notion had offline mode"
workflow-friction	"slow", "clunky", "takes forever"	"It takes 10 clicks to export a PDF"
bug-report	"broken", "crashes", "error"	"The app crashes on large files"
pricing	"expensive", "not worth", "free alt"	"Too expensive for a solo dev"
ux-confusion	"confusing", "can't find", "intuitive"	"I had no idea where settings were"
integration-gap	"connect to", "integrate with", "API"	"No Zapier integration available"

Step 4: Cluster and Deduplicate

Group similar pain points into clusters:

1. Exact duplicates: same complaint, different posts -> merge, sum engagement
2. Semantic duplicates: similar complaint, different wording -> cluster, note variants
3. Related but distinct: same domain, different problems -> keep separate

Step 5: Rank by Opportunity Score

For each cluster, compute:

Opportunity Score = (Frequency x 0.4) + (Engagement x 0.3) + (Recency x 0.2) + (Sentiment Intensity x 0.1)

Where:

• Frequency: Number of unique posts mentioning this pain point (normalized 0-10)
• Engagement: Total upvotes + comments across all posts (normalized 0-10)
• Recency: How recent the complaints are (last 7 days = 10, last 30 days = 5, older = 2)
• Sentiment Intensity: How strongly negative the language is (0-10)

Step 6: Generate Report

Write structured output to .claude/context/reports/backend/forum-monitor-report-{YYYY-MM-DD}.md:

<!-- Agent: forum-monitor-agent | Task: #{id} | Session: {date} -->

# Forum Monitor Report

**Scan Period**: {start-date} to {end-date}
**Forums Monitored**: {list}
**Total Posts Analyzed**: {count}
**Pain Point Clusters Found**: {count}

## Top Pain Points (Ranked by Opportunity Score)

| Rank | Pain Point | Category | Freq | Engagement | Score | Sources |
| ---- | ---------- | -------- | ---- | ---------- | ----- | ------- |
| 1    | [desc]     | [cat]    | [n]  | [n]        | [n.n] | [n]     |

## Detailed Findings

### 1. [Pain Point Title] (Score: X.X)

**Category**: [type]
**Frequency**: [n] mentions across [n] sources
**Engagement**: [total upvotes] upvotes, [total comments] comments
**Forums**: [list of forums where this appeared]

**Representative Quotes:**

1. "[exact quote]" - [source URL] ([n] upvotes)
2. "[exact quote]" - [source URL] ([n] upvotes)
3. "[exact quote]" - [source URL] ([n] upvotes)

**App Opportunity Assessment:**

- Buildable as standalone app: YES/NO
- Estimated complexity: LOW/MEDIUM/HIGH
- Existing solutions: [list or "none found"]
- Differentiation angle: [what would make a new solution win]

Iron Laws

1. ALWAYS cite source URLs for every finding -- unverifiable claims are worthless
2. NEVER fabricate engagement metrics -- counts must come from actual collected data
3. ALWAYS cross-reference at least 2 forums before declaring a trend
4. NEVER include content from private or gated forums -- public content only
5. ALWAYS include verbatim quotes -- user language is more valuable than agent summaries

Anti-Patterns

Anti-Pattern	Why It Fails	Correct Approach
Single-forum reports	One community is not representative	Cross-reference 2+ forums before trending
Subjective ranking	Personal opinion is not data	Use weighted opportunity formula
Missing source URLs	Downstream agents cannot validate findings	Every finding must have a clickable source
Stale data without date range	Trends from 2024 are not 2026 trends	Always specify scan period in report header
Over-counting duplicates	Same user posting in 3 threads is not 3 data points	Deduplicate by unique user + unique complaint

Related Skills

• browser-automation -- for deeper scraping when WebSearch/WebFetch are insufficient
• deep-research -- for comprehensive investigation of specific pain points
• feedback-analysis -- for structured sentiment and NPS analysis

Assigned Agents

Agent	Role
forum-monitor-agent	Primary -- executes the full workflow
researcher	Supporting -- deeper investigation
app-generator-agent	Consumer -- reads reports for app ideas

Memory Protocol (MANDATORY)

Before starting:

node .claude/lib/memory/memory-search.cjs "forum monitor pain points trends"

Read .claude/context/memory/learnings.md

After completing:

• New monitoring pattern -> .claude/context/memory/learnings.md
• Forum access issue -> .claude/context/memory/issues.md
• Scoring model decision -> .claude/context/memory/decisions.md

ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.