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Envgrd – Detect environment variable drift using AST analysis

Hacker News

Envgrd – Detect environment variable drift using AST analysis

I built a small CLI to solve a problem that repeatedly caused production issues on my teams: environment variable drift. Things like: Code starts using a new env var but configs aren’t updated Old variables sit in .env or docker-compose long after they’ve been removed Onboarding fails because required env vars aren’t documented anywhere CI/CD passes locally but fails remotely because variables were exported only on one machine Regex-based scanners always produced tons of false positives and couldn’t handle dynamic patterns. So I built envgrd, a fast, AST-based scanner that uses Tree-Sitter to parse code (JS/TS, Go, Python, Rust, Java) and compare it against env sources: .env files, direnv, docker-compose, Kubernetes ConfigMaps/Secrets, systemd units, and shell exports. It reports: Missing env vars (used in code but not in configs) Unused env vars (in configs but never referenced in code) Dynamic patterns like process.env["prefix_" + var] or os.Getenv(key + "_suffix") It runs in parallel, supports JSON output, and can be hooked into post-merge or CI jobs. Repo: https://github.com/njenia/envgrd Would love feedback, ideas, or any edge cases you think it should handle!

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, dock, new · Missing: agents, macos, agent
85%85% 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: supports, para · Missing: reddit linkedin, podcasting, created
67%67% 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
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para, scanner · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, 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
29%29% 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
18%18% 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.

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