lardbit/nefarious

[Feature] Manunal search for specific episodes

Open

#28 opened on Dec 20, 2019

View on GitHub
 (8 comments) (0 reactions) (0 assignees)Python (84 forks)auto 404
enhancementhelp wanted

Repository metrics

Stars
 (1,274 stars)
PR merge metrics
 (PR metrics pending)

Description

My apology if it is already there and I just missed it.

Manual search is great feature but right now for series is bin unintuitive since you can only search whole seasons manually.

Another thing would be allow search from the status section (You see torrent was not found for episode / series, you will try your own luck right there)

Now if I am searching for episode where torrent was not found, I would like to know why without long search in logs. So last feature suggestion for this would be catch search data returned from Jackett (could be just for 24h or something). When applying search criteria on data again user can get grasp of why torrent wasn't found and also select one of the rejected torrents without new search.

The last part is bit of a stretch that would be nice for future but definitely low priority now.

Contributor guide

Research direction
Investigate the current search implementation in nefarious, focusing on how series episodes are searched and how Jackett results are processed. Suggest changes to allow manual search for specific episodes and to expose search data temporarily for user inspection.
Tech stack
python
Domain
backendfrontend
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonWeb development
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
60