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nice-try-gptlisted

Analyzes authorized CTF challenges by reproducing intended solves, identifying cheap LLM shortcuts, applying minimal human-friendly transformations, and verifying results end-to-end. Appropriate when reviewing or adapting CTFs to reduce pattern-matching shortcuts without materially increasing human difficulty.
aleff-github/NiceTryGPT · ★ 1 · AI & Automation · score 67
Install: claude install-skill aleff-github/NiceTryGPT
# NiceTryGPT Less pattern matching. More actual hacking. Use this skill only for CTFs, training labs, and systems the user is authorized to test. ## Core contract The goal is not to make the challenge harder in general. The goal is to reduce one cheap LLM shortcut while keeping the challenge fair and recognizable to a human player. Preserve these invariants: 1. Same intended vulnerability class. 2. Same learning objective. 3. Same prerequisite knowledge. 4. Same flag or success semantics. 5. Roughly the same human difficulty band. Default to **one** resistance change. Use a second only when the first is insufficient and the Human Cost Gate still passes. It is valid to make no change. Use these final statuses exactly: - `BASELINE FAILED` - `NO CHANGE NEEDED` - `TRANSFORMED PASS` - `TRANSFORMED FAILED` ## 1. Understand Read only what is needed to understand and run the challenge: - player-facing description; - entry point; - relevant source/configuration; - run or build instructions; - flag format or equivalent success condition; - intended learning objective. Before editing, record: - vulnerability class; - intended solve path; - expected player knowledge; - approximate difficulty band; - clean-start procedure. Do not modify files yet. ## 2. Baseline solve Start from a clean state and solve the original challenge end-to-end through the player-facing surface. A valid baseline must obtain the runtime flag or equivalent success condition through the intended vu