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prospector-dev
prospector
Python2013–2017
prospector-dev / prospector

prospector

Inspects Python source files and provides information about type and location of classes, methods etc

Developer ToolsScience & Engineering
Explore on GitHubRead Hacker News discussions
5Distinct people
6Mentions
0Explicit recommenders
2.1KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Inspects Python source files and provides information about type and location of classes, methods etc

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year6 dated mentions

Observed mentions: 2013-11-05 09:05:13 UTC to 2017-01-10 14:53:24 UTC.

0122013-01-01 00:00:00 UTC to 2014-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments20132014-01-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20152016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2017

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20132013-01-01 00:00:00 UTC2014-01-01 00:00:00 UTC211
20142014-01-01 00:00:00 UTC2015-01-01 00:00:00 UTC101
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC202
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC000
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC101

What Hacker News says

6 sources
underyx·Jan 10, 2017
This seems especially useful when you don't want to think about how to set up linting, and what rules you want to use. There's a similar tool for Python, called prospector: https://github.com/landscapeio/prospector With coala you need to configure which linters run on which files, and we recognize this as a thing we can improve upon. We're soon going to ship a feature which will allow configuration to just specify where the files are (e.g. 'src/*.py') and what aspects to check (e.g. 'syntax,smells,duplication,security'). Then coala will decide which tools are appropriate, get them from pip/npm/wherever, and run them.
In PMD source code analyzer
jquast·Apr 27, 2015
This looks like a poor clone of http://landscape.io/ - viewing what they have to offer on github, I'm not very impressed at all, the internal tool of landscape.io, https://github.com/landscapeio/prospector does a whole lot more. I think calling it "code review" as opposed to "health" or just "lint checker" is marketing bullshit aimed at managers who think all work of engineers could be magically boiled down to automation. That said, I make heavy use of prospector, along with or wrapping: dodgy, frosted, mccabe, pep257, pep8, pylint, pyroma, restructuredtext_lint, doc8, and sphinx-build "warnings as errors" as part of my builds. Result: Code that becomes a public source reference used by other projects. Contributions from people who are able to quickly dive in and find where to place the change they wish to propose. Through enforced stylin
Read the rest
g, the entire codebase reads as though it has a single authorship, "same throughout", this can make ~5K LOC as easy to adapt to as another of 1/10th its size. I took a ~25K LOC open source project, ran it through landscape.io, worked (along with closing many other bugs) from "50% health" towards "97% health", and the readability and maintainability of the project increased drastically. It does work. Why are these checkers useful? Because I want people who review code, especially in business, to spend their time on the purpose of the code, and not spend a single minute of their time considering whether this suites the "style" of their engineering department. I have seen too many people use code review for pedantic styling. If I can have the tools reject the code before a person, it lowers the time/cost of a purposeful review. It also hurts less ego's to get only 1 or 2 open issues as opposed to 15-20. Crude napkin estimates of current and past employers gauge the value of the software at roughly $10-$25 per line of code. It should be treated as such.
In Automated code review for Python, Django, etc.
jquast·Feb 26, 2015
I work very hard to have tools reject pull requests as much as possible. For python I am making good use of https://github.com/landscapeio/prospector to enforce pep257 and pep8 compliance. It also catches a lot of things (through pylint and frosted/flake8) that humans often do not, such as unused variables. The advantage is that tools reject the pull request, and not a person. It doesn't harm ego to be rejected by a tool.
In Why Code Snobs Are Invaluable

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