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m(ctf)p – A semi-automated environment for solving CTF challenges

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m(ctf)p – A semi-automated environment for solving CTF challenges

Hi folks! I built this over the past few weeks for a cross-company CTF I was participating in. It was mostly an experiment to learn about CTFs, MCP servers, Kali Linux, Claude Code, and really just how far LLMs can go in a given domain. It's basically just an MCP server (for integrating with the CTF server APIs, providing notes, and a few other niceties) paired with a Kali Linux-based Docker image that has Claude Code installed, plus a custom slash command [1] to tie it all together. It performed admirably during the CTF I tried it on, it was able to zero-shot solve maybe 10 or so of the simpler challenges, and provided substantial assistance on another 5 or 6 before getting stuck. It didn't stand a chance against the hardest challenges. This was the first CTF I've participated in, and it was an absolute blast. I can imagine some people feeling that LLMs take the fun out of CTFs, but I think the "centaur" [2] aspect of human-LLM interactions is both powerful and effective, given the right infrastructure and UX. Happy to answer any questions people have about the project! [1] https://docs.anthropic.com/en/docs/claude-code/slash-command... [2] https://en.wikipedia.org/wiki/Advanced_chess

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: claude, mcp, dock · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
35%35% 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
20%20% 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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