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Skipper AI – Turn Slack conversations into detailed Jira tickets

Hacker News

Skipper AI – Turn Slack conversations into detailed Jira tickets

Skipper turns Slack conversations into Jira tickets. It uses AI to organize the text, analyze images, and create detailed tickets in Jira — like an assistant. This reduces a bunch of tedious, repetitive Jira work. I initially built Skipper as an internal tool for my team, and it has worked well in saving us time and context-switching. What it does uniquely: - Task type: Auto-detects based on context. - Epics: Auto-assigns the most relevant epic it finds, or you can specify which one to use. - Sprints: Detects the next sprint automatically and places the ticket there. - Assignee: You can assign to someone through your command, or leave it unassigned by default. Just @Skipper in a thread, or use /ticket anywhere. A ticket will be created from context & posted inline for everyone to see. Try Skipper: https://tryskipper.ai Give it a spin! I'd love your feedback and questions.

Visit product View on Hacker NewsOct 24, 2024

Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, context · Missing: mac, agents, macos

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

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

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

15%15% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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