Auto-document your analytics setup (npx package)

Hacker News

Hey HN, sharing an npx package that I’ve been working on to help teams automatically document their analytics setups. It crawls any JS/TS codebase and generates a YAML schema that catalogs all the events, properties, and triggers. Currently supporting GA, Amplitude, Mixpanel, Amplitude, Rudderstack, mParticle, PostHog, Pendo, Heap, Snowplow. Let me know if there’s any more I should add to the list! Came out of a personal pain where I was struggling to keep tabs on all the analytics events we had implemented. The “tracking plan” spreadsheets just weren’t cutting it, and I wanted something that would automatically update as the code changed. Hoping it’ll be helpful to other folks as well. Also open to suggestions for things I can build on top of this! Perhaps a code check tool to detect breaking changes or some UI to view this info when you’re querying your analytics data? Would love your thoughts and feedback!

Visit product View on Hacker NewsSep 11, 2024

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

89%89% 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 · Missing: supports, reddit linkedin, podcasting

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

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

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

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

39%39% 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

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

14%14% 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.

Incorrect prediction on native model

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