# I Built a Free AI Pain Point Miner

> Source: <https://github.com/joatsaint/pain-point-miner>
> Published: 2026-07-29 01:45:40+00:00

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)