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Shikhu – Understand the code your agents write

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Shikhu – Understand the code your agents write

Agents write code really well, but it can get hard to understand what they've written and why. Shikhu is a CLI tool and Agent Skill that facilitates learning your code through self-quizzing, transcript analysis, and validation flows. I'm Arjun, I'm currently a developer advocate at Pinecone, and I use agents to code a lot! I enjoy using agents to code, but I've been feeling like I've lost some conceptual learning and understanding that comes with writing the code yourself. I wanted to make a tool that would make it easy to re-build habits around learning, while complementing workflows that use agents. I found inspiration for the tool from reading a research paper on skill formation from Anthropic, and Shikhu is the result of my attempt to build a tool around that: https://arxiv.org/abs/2601.20245 Shikhu focuses on helping develop conceptual understanding via knowledge coverage on a file-level. Shikhu makes multiple choice quizzes on a file-level for you to take. You can reject a malformed question, answer it, and eventually create validated "golden" question sets for your code. Your ability (or, anyone who you collaborate with) to answer golden questions about your code corresponds to your knowledge coverage. Using the skill, you can learn specific files with your own coding agents, and use those conversations to inform quiz development, to help reinforce learnings. Repo is here, it's free to use, and requires an Inception API Key for question generation and summarization: https://github.com/arjunpatel7/shikhu Install Shikhu with uv: uv tool install shikhu Install the agent skill afterwards: shikhu init shikhu install-skill As of writing, the free tier of Inception's pricing should cover typical use. My article where I explain more about Shikhu is here: https://www.arjunkirtipatel.com/blog/introducing-shikhu It's definitely rough around the edges, probably will change a lot and maybe has a few bugs, but I found it useful enough to share. Thanks for reading! -Arjun

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, using · Missing: mac, macos, cursor
86%86% 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
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
41%41% 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
33%33% 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
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: collaborate · 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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