Your AI Sucks at Math. Fix It With One Command.

You've seen this before. You ask your AI agent: "Find ∫ x·e^x dx" It confidently replies: e^x + C , complete with a plausible-looking derivation. You nod. Then you check — the correct answer is (x−1)·e^x + C . It was wrong by a mile, and you almost shipped it. This is the fundamental problem with AI math today: LLMs can talk, but they can't verify their own work. They sound convincing while being catastrophically wrong. And the more complex the problem, the better the hallucination. Math.skill changes that. It's an open-source mathematical reasoning skill for AI agents — install it, and your agent stops guessing and starts verifying. What Makes It Different Typical AI Math Plugin Math.skill Workflow Prompt → LLM → answer Prompt → 7-step pipeline → ≥2 verifications → answer Verification None Answer blocked if verification fails Open problems Might hallucinate a "solution" Honestly says "this is unsolved" Error recovery No mechanism Auto-backtrack, fix, recompute, re-verify The core differentiator: a verification engine that runs at least 2 of 11 independent checks on every answer. No answer leaves the pipeline unverified. Period. The 7-Step Pipeline Every problem flows through this: Step What Happens Why It Matters 1. Parse Extract conditions, goals, variables, implicit domain constraints Catches misread problems before they waste your time 2. Model Build formal representation: equation, function, matrix, probability space, etc. Prevents building the wrong mathematical structure 3. Select Choose the optimal method from 30+ strategies Avoids brute-forcing when elegance exists 4. Solve Step-by-step with mathematical justification at every transformation Full traceability — nothing hidden 5. Verify Apply ≥2 of 11 independent verification methods The differentiator — catches what LLMs miss 6. Correct If verification fails: backtrack to last known-good step, fix, recompute, re-verify No "doubling down" on wrong answers 7. Deliver Exact answer (not approximate), domain con