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Pain Point Finder

四阶段流程:按领域发现相关 subreddit,扫描帖子并按 painScore 排序,深入分析评论中的认同与失败方案信号,最后由模型综合成结构化机会提案。依赖 node 脚本调用 PullPush API,无需 API key。

yanji84
yanji84
热度 61.96
最近更新 2026-05-11 17:27:24
分类:搜索依赖:代码执行 / 读写文件 / 第三方 API / 内部调用大模型应用场景:研究 / 市场分析 / 报告撰写 / 推荐

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add pain-point-finder
Metadata
name
pain-point-finder
description
Discover pain points, frustrations, and unmet needs on Reddit using PullPush API. No API keys required. Use to find startup ideas backed by real user complaints.
metadata
{"clawdbot":{"emoji":"🔬","requires":{"bins":["node"]}}}

Pain Point Finder

Discover validated pain points on Reddit. Searches for frustrations, complaints, and unmet needs, then analyzes comment threads for agreement signals and failed solutions. Powered by PullPush API — no API keys needed.

Workflow

Follow these 4 phases in order. Each phase builds on the previous.

Phase 1: Discover Subreddits

Find the right subreddits for the user's domain.

node {baseDir}/scripts/pain-points.mjs discover --domain "<user's domain>" --limit 8

Example:

node {baseDir}/scripts/pain-points.mjs discover --domain "project management" --limit 8

Take the top 3-5 subreddits from the output for phase 2.

Phase 2: Scan for Pain Points

Broad search across discovered subreddits.

node {baseDir}/scripts/pain-points.mjs scan \
  --subreddits "<sub1>,<sub2>,<sub3>" \
  --domain "<domain>" \
  --days 90 \
  --limit 20

Example:

node {baseDir}/scripts/pain-points.mjs scan \
  --subreddits "projectmanagement,SaaS,smallbusiness" \
  --domain "project management" \
  --days 90 \
  --limit 20

Review the scored posts. Posts with high painScore and high num_comments are the best candidates for deep analysis.

Phase 3: Deep-Dive Analysis

Analyze comment threads of top posts for agreement and solution signals.

Single post:

node {baseDir}/scripts/pain-points.mjs deep-dive --post <post_id>

Top N from scan output:

node {baseDir}/scripts/pain-points.mjs deep-dive --from-scan <scan_output.json> --top 5

Look at the validationStrength field:

  • strong: widespread, validated pain (agreementRatio > 0.20, 10+ agreements)
  • moderate: notable pain with some validation
  • weak: some signal but limited agreement
  • anecdotal: one person's complaint, needs more evidence

Phase 4: Synthesis (you do this)

For each validated pain point, present a structured proposal:

  1. Problem: One-sentence description of the pain
  2. Evidence: Top quotes + agreement count + subreddit
  3. Who feels this: Type of person/business affected
  4. Current solutions & gaps: What people have tried (from solutionAttempts) and why it fails
  5. Competitive landscape: Tools mentioned (from mentionedTools)
  6. Opportunity: What's missing in current solutions
  7. Idea sketch: Brief product/service concept
  8. Validation: strong/moderate/weak + data backing it

Options Reference

discover

Flag Default Description
--domain required Domain to explore
--limit 10 Max subreddits to return

scan

Flag Default Description
--subreddits required Comma-separated subreddit list
--domain Domain for extra search queries
--days 365 How far back to search
--minScore 1 Min post score filter
--minComments 3 Min comment count filter
--limit 30 Max posts to return
--pages 2 Pages per query (more = deeper, slower)

deep-dive

Flag Default Description
--post Single post ID or Reddit URL
--from-scan Path to scan output JSON
--stdin Read scan JSON from stdin
--top 10 How many posts to analyze from scan
--maxComments 200 Max comments to fetch per post

Rate Limits

The script self-limits to 1 request/sec, 30/min, 300/run. If PullPush is slow or returns errors, it retries with exponential backoff. Progress is logged to stderr.

Tips

  • Start broad with --days 90 then narrow to --days 30 for recent trends
  • High num_comments + high score = validated pain (many people agree)
  • High painScore + low num_comments = niche pain (worth investigating)
  • The mentionedTools in deep-dive output maps the competitive landscape
  • Posts with validationStrength: "strong" are the best startup candidates