Clawned.io Crowdsource public security scanner for OpenClaw skills

Hacker News

Hey HN, I built Clawned because I got mass downloading OpenClaw skills without reading a single SKILL.md. That felt stupid. Turns out a lot of them are stupid and unsafe too having reverse shells, credential theft, prompt injection buried in configs people(and myself) blindly trust. Clawned scans any skill before it touches your machine. 60+ threat patterns. Sub-2s. No signup. Paste a name or URL and go. Already scanned 6,500+ skills. ~20% flagged as CAUTION or THREAT. That number honestly surprised me Please give it a go and let me know how I can improve it

Visit product View on Hacker NewsFeb 26, 2026

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Launch Intel predictions

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

69%69% 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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: scanner · Missing: mobile apps, ios, personal

52%52% 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

37%37% 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

28%28% 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

24%24% 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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