Découvrez les skills DevOps, comparez leurs dépôts et consultez leurs instructions.
End-to-end frontend testing with Playwright. Page Object Model, selectors, fixtures, accessibility audits. Limited E2E coverage currently - area for improvement. Keywords: Playwright, E2E, Page Object Model, PO...
Provides expertise for analyzing DWARF debug files and understanding the DWARF debug format/standard (v3-v5). Triggers when understanding DWARF information, interacting with DWARF files, answering DWARF-related...
Fuzzing dictionaries guide fuzzers with domain-specific tokens. Use when fuzzing parsers, protocols, or format-specific code.
Wycheproof provides test vectors for validating cryptographic implementations. Use when testing crypto code for known attacks and edge cases.
Comprehensive token integration and implementation analyzer based on Trail of Bits' token integration checklist. Analyzes token implementations for ERC20/ERC721 conformity, checks for 20+ weird token patterns,...
Scans Algorand smart contracts for 11 common vulnerabilities including rekeying attacks, unchecked transaction fees, missing field validations, and access control issues. Use when auditing Algorand projects (TE...
Semgrep is a fast static analysis tool for finding bugs and enforcing code standards. Use when scanning code for security issues or integrating into CI/CD pipelines.
Create custom Semgrep rules for detecting bug patterns and security vulnerabilities. This skill should be used when the user explicitly asks to "create a Semgrep rule", "write a Semgrep rule", "make a Semgrep r...
cargo-fuzz is the de facto fuzzing tool for Rust projects using Cargo. Use for fuzzing Rust code with libFuzzer backend.
AddressSanitizer detects memory errors during fuzzing. Use when fuzzing C/C++ code to find buffer overflows and use-after-free bugs.
Scans TON (The Open Network) smart contracts for 3 critical vulnerabilities including integer-as-boolean misuse, fake Jetton contracts, and forward TON without gas checks. Use when auditing FunC contracts. (pro...
Provides guidance for property-based testing across multiple languages and smart contracts. Use when writing tests, reviewing code with serialization/validation/parsing patterns, designing features, or when pro...
Scans Solana programs for 6 critical vulnerabilities including arbitrary CPI, improper PDA validation, missing signer/ownership checks, and sysvar spoofing. Use when auditing Solana/Anchor programs. (project, g...
Ruzzy is a coverage-guided Ruby fuzzer by Trail of Bits. Use for fuzzing pure Ruby code and Ruby C extensions.
Coverage-guided fuzzer built into LLVM for C/C++ projects. Use for fuzzing C/C++ code that can be compiled with Clang.
Searches and explores Burp Suite project files (.burp) from the command line. Use when searching response headers or bodies with regex patterns, extracting security audit findings, dumping proxy history or site...
Techniques for writing effective fuzzing harnesses across languages. Use when creating new fuzz targets or improving existing harness code.
Clarify requirements before implementing. Use when serious doubts araise.
Atheris is a coverage-guided Python fuzzer based on libFuzzer. Use for fuzzing pure Python code and Python C extensions.
Comprehensive smart contract development advisor based on Trail of Bits' best practices. Analyzes codebase to generate documentation/specifications, review architecture, check upgradeability patterns, assess im...
Techniques for patching code to overcome fuzzing obstacles. Use when checksums, global state, or other barriers block fuzzer progress.
CodeQL is a static analysis framework that queries code as a database. Use when you need interprocedural analysis or complex data flow tracking.
Systematic code maturity assessment using Trail of Bits' 9-category framework. Analyzes codebase for arithmetic safety, auditing practices, access controls, complexity, decentralization, documentation, MEV risk...
AFL++ is a fork of AFL with better fuzzing performance and advanced features. Use for multi-core fuzzing of C/C++ projects.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.