Async multi-person collaboration skill for Claude Code

Hacker News

Whilst working with a colleague researching a data model we wanted to build into a website we were struggling sharing the output of our AI sessions, not just the findings but the thought process, the failures as well as success, and any files written. So I built a Claude skill that enables asynchronous collaboration between multiple human / agent pairs. Its write once markdown files in a shared location, git, dropbox, network, that all users can view realtime. User driven summaries can be created to save context and summarise status for new joiners to the workspace. If wanted these can be posted to git discussions for wider collaboration or sharing. On git there are no branches, merges or PRs so nothing to slow you down. Files are uniquely named so no clashes. It's all markdown files with links between summaries and summary commands that extract themes into a patterns.md file. No database, no embeddings or vector DBs. If you want to put Obsidian over the top you can, it's just not built in. I'd love feedback on the concept and implementation. Raw data kept forever for review, with the compressed summaries available for faster review by humans and to save tokens for agents.

Visit product View on Hacker NewsApr 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

98%98% 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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% 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 · Strong signals: users · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

20%20% 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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