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NeverC
NeverC contains 6 collected skills from NeverSight, with repository-level occupation coverage and site-owned skill detail pages.
Skills in this repository
Expertise in compiler development using LLVM infrastructure including frontend design, IR generation, optimization passes, and code generation. Use this skill when building custom programming languages, implementing DSL compilers, or working on compiler internals.
Expertise in LLVM-based dynamic binary instrumentation, runtime tracing, and program monitoring. Use this skill when implementing runtime analysis tools, code coverage systems, profilers, or dynamic security monitors.
Use when a CI/CD pipeline (GitHub Actions or similar) surfaces a runtime bug that is platform-specific ("fails on linux-x64 but passes on arm64/macos/windows"), intermittent/flaky (different tests fail each run, segfaults, "Segmentation fault (core dumped)", non-deterministic), or does not reproduce locally ("works on my machine"). Covers reproducing in a CI-matching environment (Docker + qemu for cross-arch hosts), reusing prebuilt CI artifacts instead of rebuilding, distinguishing a tool/compiler crash from a built-program crash, determinism / ASLR / data-race triage, pushing a minimal workflow_dispatch debug job to a real runner, and capturing core-dump backtraces. Trigger phrases: "CI fails but works locally", "only fails on x64/arm64", "flaky test", "random segfault in CI", "core dumped", "can't reproduce the CI crash".
Expertise in LLVM optimization passes, performance tuning, and code transformation techniques. Use this skill when implementing custom optimizations, analyzing pass behavior, improving generated code quality, or understanding LLVM's optimization pipeline.
Expertise in MLIR (Multi-Level Intermediate Representation) and CIR (Clang IR) development for domain-specific compilation and high-level optimizations. Use this skill when building ML compilers, domain-specific languages, or working with multi-level compilation pipelines.
Self-referential completion loop for AI CLI tools. Re-runs the agent on the same task across turns with fresh context each iteration, until the completion promise is detected or max iterations is reached.