Cleaner, faster wordpress-theming with Skellie

Hacker News

I've created a 5.3-based skeleton/micro-view-framework/freak-show for wordpress called Skellie. It allows for cleaner templates and reusable partials and layouts. You can use it to start a theme from scratch: https://github.com/thanosp/Skellie Or to bc-refactor an existing theme: http://goo.gl/CF0km It does not force upon you another templating syntax. It's still wordpress but with proper, structured partials and should look familiar to ZF users. It only takes over from Wordpress before rendering, so it should be compatible with almost all plugins. I would like your feedback on it. Try it on a template or two. See how much code you can reuse. PS: Enjoy the lack of functions in functions.php :)

Visit product View on Hacker NewsMar 3, 2012

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Actual performance

3points

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, compatible · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, code · Missing: mac, agents, macos

40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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