intent-score

Score a social post 0–100 for buying intent with transparent, editable regex rules. There's also an optional LLM prompt template for posts the rules can't call.

$ intent-score "Is there a tool that finds people asking for a CRM on forums? Need one for my startup asap."
 75 high   Is there a tool that finds people asking for a CRM on forums? Need one for my startup asap.
           +25 asks whether a tool exists; +25 states a need; +10 business context; +10 urgency; +5 asks a question

• Pure Python, no dependencies, no network calls.
• Every point in a score comes with a reason, so you can see why a post scored the way it did.
• Ships with a static, in-browser demo (space/) that uses the exact same rules.
• The example posts in examples/ are made up. No real user posts are included.

Install

pip install intent-score     # once published
# or from a checkout:
pip install .

Usage

# Score text directly
intent-score "Can anyone recommend a simple invoicing app?"

# Score a JSON-lines file, keep only strong signals, emit JSON
intent-score --jsonl examples/sample_posts.jsonl --min 60 --json

# Plain text on stdin, one post per line
cat posts.txt | intent-score

# Combine with a watcher, e.g. hn-keyword-watch or bsky-keyword-watch
hn-keyword-watch "alternative to" --json | intent-score --jsonl - --field title,text --min 25

In Python:

from intent_score import score, IntentScorer, load_rules

r = score("Looking for an alternative to our help desk, budget is flexible")
print(r.score, r.label, r.reasons)   # 85 high ['+30 asks for a solution', ...]

custom = IntentScorer(load_rules("my_rules.json"))

How scoring works

src/intent_score/rules.json holds a list of {id, pattern, weight, reason} rules. Each rule that matches adds its weight once. Negative weights cover self-promotion, job posts, promo codes and "not looking for...". The total is clamped to 0–100 and labelled high (≥60), medium (≥25) or low. Copy the file and tune it for your niche with --rules my_rules.json.

Optional LLM step

intent-score never calls an LLM. If you want a second opinion on borderline posts, build a prompt and send it to whichever model you already use:

from intent_score import build_prompt, parse_llm_response

prompt = build_prompt(post_text, product="a social listening tool for agencies")
reply = my_llm(prompt)                 # your own client
print(parse_llm_response(reply))       # {'score': 82, 'label': 'high', 'reason': '...'}

Or print prompts from the CLI with intent-score --prompt "a CRM for agencies" "post text".

Static demo (Hugging Face Space ready)

space/ is a plain HTML/JS page (index.html, scorer.js, rules.js) with Space front-matter in space/README.md (sdk: static). Open space/index.html locally or upload the folder as a static Space. After you edit rules.json, run python scripts/build_space.py to regenerate space/rules.js. A test checks that the JS and Python scorers agree.

Limits

• It matches English keywords. It won't catch sarcasm or context, and it doesn't know about other languages. Treat the score as a first filter, not a verdict.
• The default weights were tuned on a small set of made-up examples. Check them against your own data.
• When you score posts from a public platform, follow that platform's API terms and rate limits, and don't contact people in bulk based on a score.

If you'd rather not run this yourself, Prospy does social listening and AI lead generation across X/Twitter, Reddit, Bluesky and Hacker News, surfacing buying-intent posts.

Development

PYTHONPATH=src python -m unittest discover -s tests -v

License

MIT