choosing-a-data-vendorlisted
Install: claude install-skill howard-lynn-ye/fin-skills
# Choosing a data vendor
Most of the effort in a data decision goes to the wrong axis. Price, coverage breadth and which
Python client is nicest are all tiebreaks. Three flags decide it, and two of them are decided by a
vendor's **terms**, not by its API — so no amount of code changes the answer.
| Flag | The question it answers | What it costs you to get it wrong |
|---|---|---|
| `includes_delisted` | Are the companies that DIED in this data? | Every result is an upper bound, silently |
| `point_in_time` | Can it say what was known at *t*? | You trade on numbers published years later |
| `redistribution` | May you pass on what you fetched? | A licence breach you discover at publication |
Run the filters **before** picking a library. A source that lacks the dead names, cannot answer
"what was known at *t*", or may not be passed on is not fixable downstream.
## 1. Ask, don't guess
```bash
python -m fin_skills.data advise --market US --frequency 1d --history-years 10
python -m fin_skills.data advise --market US --delisted # exits 3: nothing serves it
python -m fin_skills.data advise --asset-class crypto --market CRYPTO --no-key
python -m fin_skills.data advise --coverage # what each source reaches
```
It reads the same `Declaration` table `python -m fin_skills.data adapters` prints, applies your
constraints as **hard filters**, and either ranks what survives — each with what it costs you,
what it *cannot* do, and the licence consequence of your