New user "un-boxing" experience

# Use case: install vrs-python, run vcf_annotation

## It would be useful to have a command line oriented "quick start":

* goal: user should be quickly able to do a pip install, download known VCF examples, run vrs-python with remote seqrepo
* setup: documented values for `--seqrepo_dp_type` `--seqrepo_base_url`. I was stumped by this until I read through python notebooks to find values.
* data: known vcf files a set of vcf files would be useful. I would up downloading the [test fixture](https://raw.githubusercontent.com/ga4gh/vrs-python/main/tests/extras/data/test_vcf_input.vcf) to do a sanity check.
* Question: What is the best population of variants in the known vcf files? Perhaps some that should produce hits in metakb &/or clingen?

It would be useful to have a command line oriented "production ready":
* goal: user should be quickly install local seqrepo and run vrs-python at "scale"
* setup: [seqrepo](https://github.com/biocommons/biocommons.seqrepo#quick-start) installs to `/usr/local/share/seqrepo` by default. This may not be possible in environments where user lacks privileges. Perhaps feature `--seqrepo_root_dir` more prominently in documentation?

## Usage:
* Should the seqrepo installation oriented options [seqrepo_dp_type, seqrepo_base_url, seqrepo_root_dir] have environmental variable equivalents? This simplifies downstream scripting.
* `--vrs_pickle_out` I might be missing something obvious, but I needed to do an eval() in order to use the results.

```
from pprint import pprint
import pickle
import ast
import requests

# load pickled dict
with open('vrs_objects.pkl', 'rb') as f:
vrs_objects = pickle.load(f)
for k, v in vrs_objects.items():
vrs_objects[k] = ast.literal_eval(v)

# view details
pprint(vrs_objects)

```

## Application

Once vrs identifiers are calculated, the question is `What can I do with them?`. It would be useful if there was a known endpoint using the same schema versions. See attached notebook example.