I Built a Free AI Pain Point Miner

A 25-year enterprise IT veteran released a free open-source AI tool called Pain Point Miner that scans YouTube channel transcripts and returns a ranked list of audience questions and pain points, using Anthropic's Claude Haiku model for extraction. The tool, which costs a few cents per run and requires users to provide their own transcript files, aggregates results into a report showing the most frequently mentioned questions and pain points across videos. The creator noted that one of three test transcripts failed due to a token budget issue with Haiku, a known limitation not yet fixed.

Point this at a YouTube channel's transcripts and it hands back a ranked list of what that audience actually asks and struggles with — no manual comment-scrolling, no guessing. I spent 25 years in enterprise IT before I ever touched a line of Claude Code. Reading a room full of frustrated people and finding the one real question under the noise isn't a new skill AI gave me — it's the same skill I used for two decades in ops, just pointed at a different kind of ticket queue. This tool automates the scanning part. It doesn't replace the judgment part. indexer.py scans a folder of transcript .md files and builds a lightweight index. pain point extractor.py reads that index, sends each transcript through Claude Haiku to pull out questions, pain points, and desired outcomes, then aggregates everything into one ranked report. Output looks like this: Top Questions Most Asked 1. How do I rebuild my processes around what AI makes possible...? — mentioned in 4 video s Top Pain Points Most Expressed 1. Existing business models built on inefficiencies... — mentioned in 6 video s It doesn't download transcripts for you, it doesn't touch Reddit or any other source, and it doesn't publish anything. Point it at .md transcript files you already have. Nothing else. pain-point-miner/ ├── indexer.py scans transcripts/, builds the index ├── pain point extractor.py two-pass Claude extraction + aggregation ├── README.md ├── build-it-yourself-prompts.md rebuild this with your own AI, 3-phase prompt ├── devto-announcement.md ├── transcripts/ your own .md files go here not included │ └── <group-name / │ └── <channel-name / │ └── .md └── knowledge base/ ├── index.json generated by indexer.py └── reports/ └── pain points <date <group .md generated by the extractor - Clone this folder. pip install anthropic - Get an Anthropic API key: https://console.anthropic.com/settings/keys https://console.anthropic.com/settings/keys - Set it as an environment variable, or drop a .env file in this folder: ANTHROPIC API KEY=your key here - Put your own transcripts in transcripts/<group-name /<channel-name / .md , matching the structure above. Each file needs a plain text header title, channel, date followed by the transcript body — no sample transcripts are included in this repo the ones used to test it were someone else's copyrighted video content, not fit to redistribute . python python -c "from indexer import build index; build index verbose=True " python -c "from pain point extractor import run extractor; run extractor group='<group-name ' " Cost: Haiku, a few cents per run for a handful of files. You're paying Anthropic directly for your own usage — nothing routes through me. Fed it three real AI/tech transcripts as a first test. Two extracted cleanly; one failed with a truncated-JSON error because Haiku ran out of the token budget mid-response. Known limitation, not fixed yet — if you hit it, either raise max tokens in pass1 extract or just accept the occasional skip; the aggregation step still runs fine on whatever succeeded. Free guide for IT professionals figuring out what AI can't replace: https://rskiles.com/the-riddle-of-steel https://rskiles.com/the-riddle-of-steel