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Diagnose and fix Claude in Chrome MCP extension connectivity issues. Use when mcp__claude-in-chrome__* tools fail, return "Browser extension is not connected", or behave erratically.

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Builds and queries multi-language source and binary code graphs for security analysis. Includes pre-analysis passes for blast radius, taint propagation, privilege boundaries, entry point enumeration, proxy/unresolved-call tracking, type/reference queries, structural traversal, graph diffs, audit augmentation, declared cross-language/FFI/external links via `.trailmark/links.toml`, and SQL schema graphs. Use when analyzing call paths, mapping attack surface, finding complexity hotspots, enumerating entry points, tracing taint propagation, measuring blast radius, importing SARIF/weAudit/binary findings, linking source graphs across language or RPC boundaries, or building a code graph for audit prioritization. Feature-gate version-specific Trailmark APIs before using them; prefer `trailmark.parse.detect_languages()` or `--language auto` when the target language is unknown or polyglot.

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Designs and improves fuzzing harnesses for C/C++ and Rust. Covers mapping raw bytes onto a target API, generating structured inputs, avoiding non-determinism and false crashes, and deciding what to fuzz together. Use when writing a first LLVMFuzzerTestOneInput or fuzz_target! harness, when a campaign finds nothing or reports crashes that will not reproduce, or when the target API needs structured rather than raw input.

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Measures and interprets what a fuzzing campaign actually reaches, using llvm-cov, lcov, or a fuzzer's own coverage output. Covers baselining a new campaign, reading coverage reports, and turning uncovered regions into harness, seed, or dictionary work. Use when a fuzzer plateaus, when judging whether a harness is effective, after changing a harness, or when asking why some code is never reached.

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Sets up and runs libFuzzer, the coverage-guided fuzzer built into LLVM, on C/C++ code that compiles with Clang. Covers harness structure, -fsanitize=fuzzer builds, corpus and dictionary management, sanitizer integration, and campaign triage. Use when writing or debugging an LLVMFuzzerTestOneInput harness, starting fuzzing on a C/C++ library, choosing between libFuzzer and AFL++, or working out why a libFuzzer run finds nothing.

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Generates Mermaid diagrams from Trailmark code graphs. Produces call graphs, class hierarchies, module dependency maps, containment diagrams, complexity heatmaps, and attack surface data flow visualizations. Use when visualizing code architecture, drawing call graphs, generating class diagrams, creating dependency maps, producing complexity heatmaps, or visualizing data flow and attack surface paths as Mermaid diagrams.

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Writes and reviews structured Lean 4 proofs and designs Lean libraries following Mathlib conventions. Use when proving theorems in Lean, formalizing mathematics or specifications in Lean 4, defining new types or definitions in a Lean library, reviewing Lean proofs for readability and maintainability, refactoring long tactic proofs into lemmas, filling in sorry placeholders in a Lean development, setting up CI or linters for a Lean project, diagnosing slow proofs or maxHeartbeats timeouts, or writing custom tactics, macros, or linters.

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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.

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Runs a Trailmark structural review gate over a branch, pull request, fix commit, release diff, or git ref range to detect new entrypoints, new tainted paths, removed validation or authorization calls, privilege-boundary drift, blast-radius growth, complexity growth, and newly reachable sensitive sinks. Use when reviewing a PR, branch, remediation commit, or release diff where graph-level security regressions should be checked before merge.

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Compares Trailmark code graphs at two source code snapshots (git commits, tags, or directories) to surface security-relevant structural changes. Detects new attack paths, complexity shifts, blast radius growth, taint propagation changes, and privilege boundary modifications that text diffs miss. Use when comparing code between commits or tags, analyzing structural evolution, detecting attack surface growth, reviewing what changed between audit snapshots, or finding security-relevant changes that text diffs miss.

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Expands one confirmed or suspected vulnerability into a Trailmark graph neighborhood of variant candidates by finding sibling functions, shared callers and callees, common sensitive sinks, common entrypoint paths, interface implementations, override relationships, type/reference neighbors, and structurally similar nodes. Use after one issue is found to seed variant-analysis, semgrep-rule-creator, static-analysis, or manual review with graph-derived candidate locations.

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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.

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Validates cryptographic implementations against Project Wycheproof's test vectors, which encode known attacks and edge cases across AES, RSA, ECDSA, ECDH, and more. Covers loading test vectors, mapping result flags onto pass and fail expectations, and reading a failure. Use when testing a crypto implementation against known attacks, checking a library against standard test vectors, or investigating why two implementations disagree on the same input.

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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.

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Translates Mermaid sequenceDiagrams describing cryptographic protocols into ProVerif formal verification models (.pv files). Use when generating a ProVerif model, formally verifying a protocol, converting a Mermaid diagram to ProVerif, verifying protocol security properties (secrecy, authentication, forward secrecy), checking for replay attacks, or producing a .pv file from a sequence diagram.

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Graph-informed mutation testing triage. Parses codebases with Trailmark, runs mutation testing and necessist, then uses survived mutants, unnecessary test statements, and call graph data to identify false positives, missing test coverage, and fuzzing targets. Use when triaging survived mutants, analyzing mutation testing results, identifying test gaps, finding fuzzing targets from weak tests, running mutation frameworks (including circomvent and cairo-mutants), or using necessist.

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Sets up and runs cargo-fuzz, the standard fuzzing tool for Cargo-based Rust projects. Covers cargo fuzz init, the nightly toolchain requirement, fuzz_target! harnesses, Arbitrary-derived structured inputs, sanitizer options, cargo fuzz coverage, and reproducing a crash artifact. Use when fuzzing a Rust crate, writing a fuzz_target!, exercising unsafe blocks or FFI in Rust, or triaging a cargo fuzz crash.

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Mutation-driven test vector generation. Finds implementations of a cryptographic algorithm or protocol, runs mutation testing to identify escaped mutants, then generates new test vectors that deliberately exercise the uncovered code paths. Compares before/after mutation kill rates to prove vector effectiveness. Use when generating cryptographic test vectors, measuring Wycheproof coverage gaps, finding escaped mutants via mutation testing, creating cross-implementation test suites, or improving test vector coverage for crypto primitives.

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Patches past the barriers that stop a fuzzer making progress — checksum and hash verification, magic-value validation, time-based seeds, and other non-deterministic global state. Covers locating the blocking check, neutering it behind a fuzzing build flag, and avoiding the false positives a patch can introduce. Use when a fuzzer is stuck at validation, when coverage shows large regions behind a checksum, or when valid inputs are impractical to generate.

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Guides authoring of high-quality YARA-X detection rules for malware identification. Use when writing, reviewing, or optimizing YARA rules. Covers naming conventions, string selection, performance optimization, migration from legacy YARA, and false positive reduction. Triggers on: YARA, YARA-X, malware detection, threat hunting, IOC, signature, crx module, dex module.

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Measures timing side channels in cryptographic implementations by running them, using dudect for statistical analysis and Timecop over Valgrind for dynamic tracing. Covers the formal, symbolic, dynamic, and statistical tool categories and how to read a result. Use when testing whether a running implementation is constant-time, measuring timing variance on a compiled binary, or investigating a suspected timing attack. Not for statically inspecting compiler output — the constant-time-analysis plugin covers that.

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Hunts for the other instances of a bug already found — the variants of one root cause across a codebase. Use immediately after a vulnerability, logic bug, or bad pattern turns up in a specific file and the question becomes where else it occurs, including the bare conversational form ("are there others like this?", "is this the same bug?"). Also for generalizing one known instance into a CodeQL or Semgrep query for its whole pattern family, and for triaging a set of look-alike candidates against a known root cause. Not for initial discovery with no bug in hand.

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Performs graph-assisted triage of a single security finding, SARIF result, weAudit annotation, suspicious function, or report excerpt using Trailmark reachability, entrypoint paths, taint, privilege-boundary, blast-radius, caller/callee, and neighborhood evidence. Use when deciding whether one candidate issue is reachable, prioritizing a finding before PoC work, preparing evidence for exploit validation, or checking whether a static-analysis result is actionable.

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Generates Claude Code skills from the Trail of Bits Testing Handbook (appsec.guide), analyzing handbook pages and emitting SKILL.md files with the structure each skill type requires. Use when creating or refreshing a skill from handbook content, or when the user names the testing handbook or appsec.guide. Not for answering security testing questions — the generated skills cover those.

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