Systematic code maturity assessment using Trail of Bits' 9-category framework. Analyzes codebase for arithmetic safety, auditing practices, access controls, complexity, decentralization, documentation, MEV risks, low-level code, and testing, then produces a scorecard with evidence-based ratings and a priority-ordered roadmap. Use when assessing or scoring the maturity of a smart contract or blockchain codebase, producing a maturity scorecard or evaluation, or judging how mature, well-tested, or well-documented such a project is against a rubric.
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Systematically assesses codebase maturity using Trail of Bits' 9-category framework. Provides evidence-based ratings and actionable recommendations.
Framework: Building Secure Contracts - Code Maturity Evaluation v0.1.0
Explores the codebase to understand:
For each of 9 categories, I'll:
Generates:
Rating Logic:
I assess 9 comprehensive categories covering all aspects of code maturity. For detailed criteria, analysis approaches, and rating thresholds, see ASSESSMENT_CRITERIA.md.
1. ARITHMETIC
2. AUDITING
3. AUTHENTICATION / ACCESS CONTROLS
4. COMPLEXITY MANAGEMENT
5. DECENTRALIZATION
6. DOCUMENTATION
7. TRANSACTION ORDERING RISKS
8. LOW-LEVEL MANIPULATION
9. TESTING & VERIFICATION
For complete assessment criteria including what I'll analyze, what I'll ask you, and detailed rating thresholds (WEAK/MODERATE/SATISFACTORY/STRONG), see ASSESSMENT_CRITERIA.md.
When the assessment is complete, you'll receive a comprehensive maturity report including:
For a complete example assessment report, see EXAMPLE_REPORT.md.
When invoked, I will:
Explore codebase
Analyze each category
Interactive assessment
Generate report
| Rationalization | Why It's Wrong | Required Action |
|---|---|---|
| "Found some findings, assessment complete" | Assessment requires evaluating ALL 9 categories | Complete assessment of all 9 categories with evidence for each |
| "I see events, auditing category looks good" | Events alone don't equal auditing maturity | Check logging comprehensiveness, testing, incident response processes |
| "Code looks simple, complexity is low" | Visual simplicity masks composition complexity | Analyze cyclomatic complexity, dependency depth, state machine transitions |
| "Not a DeFi protocol, MEV category doesn't apply" | MEV extends beyond DeFi (governance, NFTs, games) | Verify with transaction ordering analysis before declaring N/A |
| "No assembly found, low-level category is N/A" | Low-level risks include external calls, delegatecall, inline assembly | Search for all low-level patterns before skipping category |
| "This is taking too long" | Thorough assessment requires time per category | Complete all 9 categories, ask clarifying questions about off-chain processes |
| "I can rate this without evidence" | Ratings without file:line references = unsubstantiated claims | Collect concrete code evidence for every category assessment |
| "User will know what to improve" | Vague guidance = no action | Provide priority-ordered roadmap with specific improvements and effort estimates |
For detailed report structure and templates, see REPORT_FORMAT.md.
Executive Summary
Maturity Scorecard
Detailed Analysis
Improvement Roadmap
Estimated Time: 30-40 minutes
I'll need:
Let's assess this codebase!
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概要と使いどころ
Builds and runs code under AddressSanitizer to catch buffer overflows, use-after-free, and other memory errors during fuzzing or tests. Covers -fsanitize=address builds, ASAN_OPTIONS, reading the crash report, LeakSanitizer, and the overhead and platform trade-offs. Use when fuzzing C/C++ or Rust that has unsafe blocks or FFI, when debugging a memory corruption crash, or when reading an ASan stack trace.
日本語の概要は準備中です。原文の説明を表示しています。
Sets up and runs AFL++ for multi-core fuzzing of C/C++ projects built with afl-clang-fast or afl-gcc-fast. Covers instrumentation modes, parallel main and secondary campaigns, persistent mode, corpus minimization, and crash triage. Use when scaling fuzzing across cores, fuzzing a mature C/C++ codebase, reading the afl-fuzz status screen, or moving on after libFuzzer has plateaued.
日本語の概要は準備中です。原文の説明を表示しています。
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary patterns, direct expression injection, dangerous sandbox configurations, and wildcard user allowlists. Use when reviewing workflow files that invoke AI coding agents, auditing CI/CD pipeline security for prompt injection risks, or evaluating agentic action configurations.
日本語の概要は準備中です。原文の説明を表示しています。
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 (TEAL/PyTeal).
日本語の概要は準備中です。原文の説明を表示しています。
Sets up and runs Atheris, the coverage-guided Python fuzzer built on libFuzzer. Covers TestOneInput harnesses, FuzzedDataProvider, instrumenting both pure Python and native C extensions, and running under AddressSanitizer. Use when fuzzing a Python package, hunting memory corruption in a Python C extension, or choosing between Atheris and Hypothesis for a Python target.
日本語の概要は準備中です。原文の説明を表示しています。
Augments Trailmark code graphs with external audit findings from SARIF static analysis results, weAudit annotation files, and version-gated Trailmark 0.4.x binary-analysis graph exports. Maps findings to graph nodes by file and line overlap, creates severity-based subgraphs, and enables cross-referencing findings with pre-analysis data (blast radius, taint, etc.). Use when projecting SARIF results onto a code graph, overlaying weAudit annotations, importing binary graph findings, cross-referencing Semgrep, CodeQL, or binary-analysis findings with call graph data, or visualizing audit findings in the context of code structure.
日本語の概要は準備中です。原文の説明を表示しています。