I built a way to see if your SDK is AI-friendly

Hacker News

Have you ever wonder if your SDKs is friendly for Agentic AI like Claude Code or Codex? I built an opensource (Apache 2.0) CLI that answer that question for you. With it you can create a test suite either manually or with an Agent based on the source code and documentation. The CLI will dispatch Agents with their own sandboxed microVMs to solve each test. Results then get graded by another Judge Agent. Test-taker agents only have access to public information (guides, blogs, package metadata), while Judge agents have access to both public and private information (source code, internal documents) After the test result are generated you can make improvement to your SDK manually, or use an Agent to automate the process. Agents are sandboxed, this means: - Host machine secrets (API keys) are not exposed to the sandbox environment - Egress HTTP requests are monitored, Judge agents' egress are limited to trusted domains to ensure that proprietary IP are not exfiltrated Features: - CLI commands for the entire workflow of generating, eval, reporting on test suites - Agent skills for each command - Local Web UI if you want to inspect test result and edit test cases visually GitHub: https://github.com/PSPDFKit-labs/agentic-usability

Visit product View on Hacker NewsApr 28, 2026

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

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 · 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 · Strong signals: way · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, friendly · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, 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

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.

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.

Correct prediction on native model

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