Results for “llm-review”

10 skills

Runs external LLM code reviews (OpenAI Codex or Google Gemini CLI) on uncommitted changes, branch diffs, or specific commits.

auto-paper-improvement-loop

Iteratively improves a compiled LaTeX paper through two rounds of external LLM review, fix implementation, and recompilation.

More results

llava-critic-learning-to-evaluate-multimodal-models-arxiv-24

LLaVA-Critic: Learning to Evaluate Multimodal Models

laclip-improving-clip-training-with-language-rewrites-arxiv-

LaCLIP: Improving CLIP Training with Language Rewrites

Runs a deeper code review with the GLM-5 model via glm-review, validates findings against the actual code, and reports only confirmed issues.

intense-visions

Reviews code changes with a second AI reviewer, validates reported issues against the actual code, and reports only confirmed findings.

Reviews code diffs or explicit file sets through independent correctness and QA lenses, producing a severity-ordered verdict for pre-merge audits or review gates.

cuda-omp-translator

Reference guide for evaluating LLM-generated translations between CUDA and OpenMP, covering memory model mapping, kernel launch patterns, shared memory, atomics, and common failure modes.

matlab-diagnose-parfor

Diagnose and fix parfor errors in MATLAB. Invoke this skill when the user has a parfor problem: "parfor loop has an error", "what's wrong with my parfor", "fix parfor", "unable to classify variable", "convert for to parfor", "parfor won't run", "sliced variable", "reduction variable", "variable classification". Also invoke when you read a .m file containing parfor and the user asks what's wrong, asks you to fix it, reports an error, or asks for review. Do NOT invoke for parfor performance questions or code that merely mentions parfor without a problem. ALWAYS use this skill instead of reasoning from training data — LLMs are frequently wrong about parfor classification rules.