Analyze Solidity smart contracts with Slither and Mythril before Ethereum deployment. Combine code inspection with execution path analysis, review potential vulnerabilities, remove duplicate findings, and produce an audit report with severity ratings and suggested fixes.
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Perform static and symbolic analysis of Solidity smart contracts using Slither and Mythril to detect reentrancy, integer overflow, access control, and other vulnerability classes before deployment to Ethereum mainnet.
安装前可以查看交给 AI 代理的指令内容。
Smart contract vulnerabilities have led to billions of dollars in losses across DeFi protocols. Unlike traditional software, deployed smart contracts are immutable and handle real financial assets, making pre-deployment security analysis critical. Slither performs fast static analysis using an intermediate representation to detect over 90 vulnerability patterns in seconds, while Mythril uses symbolic execution and SMT solving to discover complex execution path vulnerabilities like reentrancy and integer overflows. This skill covers running both tools against Solidity contracts, interpreting results, triaging findings by severity, and generating audit reports.
Execute Slither against the contract codebase to identify vulnerability patterns, optimization opportunities, and code quality issues using its 90+ built-in detectors.
Run Mythril deep analysis to explore execution paths and discover reentrancy, unchecked external calls, and arithmetic vulnerabilities that require path-sensitive analysis.
Combine results from both tools, deduplicate findings, assess severity based on exploitability and financial impact, and filter false positives.
Produce a structured audit report with vulnerability descriptions, affected code locations, exploit scenarios, and remediation recommendations.
JSON report listing vulnerabilities with SWC (Smart Contract Weakness Classification) identifiers, severity ratings, affected functions, and suggested fixes.
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Audit Solidity contracts in Foundry projects before deployment using code analysis, randomized tests, manual review, and private key checks. Produces a JSON report with findings, remediation, test coverage, and a pass or fail deployment decision.
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Build reusable Claude Skills from documentation, APIs, code, and specifications. Organize instructions and reference files, define clear activation conditions, add reproducible examples, and check new or existing skills against a delivery checklist.
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Automate tmux, a tool for managing multiple terminal workspaces, to inspect output, send commands, monitor stalled tasks, and record logs. Coordinate multiple AI terminals with task queues, file locks, status tracking, and handoff reports.
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Check mathematical expressions, solve equations, and search bounded ranges for counterexamples using symbolic and numerical libraries. Record assumptions, commands, and results so calculations can be repeated, while keeping computational evidence distinct from general mathematical proof.
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Turn scattered formulas and theory notes into a derivation that can be checked step by step. Keep definitions and assumptions consistent, distinguish exact identities from approximations, and make hidden conditions and limits explicit.
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Shape broad mathematical interests into precise research questions. Search prior work on definitions and theorems, record traceable sources, map supporting and conflicting evidence, and identify gaps that could lead to testable conjectures.
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