For a new assistant professor in CS, how to build/manage a publication pipeline I am a new assistant professor in a research institute, where the number of top-tier publications matter a lot. In short, they simply count the number of tier-one papers. 

I am highly advised to work out a good publication pipeline, in terms of how many papers are in progress (e.g., implementation), how many papers are under review, and also how many are in the preliminary stage to investigate the feasibility. 

So besides "working hard", what are some tips, comments and advices on start to setting up a "publication pipeline"? To concretize a bit, what I can come up with are:


has one or two main research lines such that you can constantly publish your major research output towards some really prestigious conferences in your field, in CS it's like SIGGRAH, OSDI, POPL and so on. But of course, usually preparing such a work takes a very long time; my personally experience is about 1~1.5 year, when I was a Ph.D. student.
Simultaneously, become versatile in terms of skillset and therefore can quickly hunt for some (low-hanging) fruit. This can be much shorter, say 3~5 months, can still target on tier-one conference, but maybe not that "prestigious" ones. I am not going to name such conferences to avoid some arguments here... 
What else? 


All I can find is a thread here (https://www.chronicle.com/forums/index.php?topic=38427.0), but the message is a bit unclear to me. Any suggestion or advice would be appreciated a lot. Thanks.
Here two pieces of advice:


As a mid/long term perspective you should build a large network with
bright people. In the beginning talk to as many as possible. Tell
them about your ideas and ask them about theirs. This will lead to a lot of collaborative papers. Don't waste too much time with people who are reluctant. Most people will be very open (especially the younger ones).
Do this also with people who are not working in exactly your field. There might be a lot of low hanging fruits to collect i.e. something that is easy for you to do but not for them or vice versa. There is hardly any type of research field that would not like to get some input from CS (buzzword "data science"). e.g. Experimental biologists here or people working in business or geography etc. This will not lead to first-authorships but possibly get your name on many papers by putting only few days of work in.