xLLM is an inference engine, meaning software that runs trained AI models to produce outputs from inputs, for large language, vision-language, diffusion, and recommendation models on different AI accelerators. Organizations use it to deploy these models with high-throughput and low-latency inference. The catalogue entries provide skills and instructions for working with xLLM.
Borrowing it
Nothing to install: this file belongs to xLLM-AI/xllm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/xLLM-AI/xllm/main/.agents/skills/code-review/SKILL.mdgit clone --depth 1 https://github.com/xLLM-AI/xllmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xllm-ai/xllm/code-review)<a href="https://agentmods.dev/skills/xllm-ai/xllm/code-review"><img src="https://agentmods.dev/badge/skills/xllm-ai/xllm/code-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xllm-ai/xllm/code-review"><img src="https://agentmods.dev/badge/skills/xllm-ai/xllm/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.00808 |
| Opus 5.5 | $0.00028 | $0.00323 |
| Sonnet 5.5 | $0.00014 | $0.00162 |
| Haiku 4.5 | $0.00007 | $0.00081 |
Grade A, and why
code-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Workflow
Step 1: Determine the diff
If the user provides explicit SHAs or a PR link, use those. Otherwise, auto-detect:
# Fetch latest remote state
git fetch origin main --quiet
# Detect current branch
CURRENT_BRANCH=$(git branch --show-current)
# Find the merge base with origin/main
MERGE_BASE=$(git merge-base origin/main HEAD)
# Show what changed
git diff --stat $MERGE_BASE..HEAD
git diff $MERGE_BASE..HEAD
If CURRENT_BRANCH is main, warn the user and ask which commits to review.
Step 2: Read project standards
Read the shared rules in custom-code-style.md and the guide for every language in the diff: C++ or Python.
Step 3: Review against the checklist
Correctness:
- Logic handles edge cases and boundary conditions
- Error handling is comprehensive (no silent failures)
- Type safety maintained (no unsafe casts, proper use of
std::optional) - Resource lifecycle correct (RAII, no leaks, proper cleanup order)
Architecture:
- Clean separation of concerns, no layer violations
- Dependencies flow in the correct direction
- Changes align with existing patterns in the codebase
- No unnecessary coupling introduced
Performance & Concurrency:
- No performance regressions on hot paths
- Thread safety: proper locking, no data races
- CUDA/NPU kernels: memory coalescing, occupancy, sync correctness
- No unnecessary copies of large objects (tensors, vectors)
Testing:
- Tests verify actual logic, not just mock wiring
- Edge cases and error paths covered
- Integration tests for cross-component changes
Production Readiness:
- Backward compatibility maintained (or breaking changes documented)
- Migration strategy for schema/config changes
- No hardcoded values that should be configurable
Step 4: Output findings
Use the format below.
Output Format
Strengths
[Specific things done well, with file:line references]
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago Changed 5a2a0031b777
- 1mo ago First seen · 116 lines · 71 tokens per session scan A 000289abb040
code-review is a skill published in the GitHub repository xLLM-AI/xllm (1,604 stars, last pushed yesterday), licensed Apache-2.0. It adds 71 tokens to every session and 808 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
code-review
Run a final code review on a pull request.
code-review-change-size
Change size guidance (800 lines).
code-breaking-changes
Search for breaking changes in external integration surfaces.
review-agent
Perform a read-only, defect-first review of a specified code change and return every actionable finding. Use when another agent delegates review of uncommitted changes, a base-branch diff, a commit, or custom review instructions.
inferencex-report
Automatically fetch InferenceX benchmark data and generate daily performance reports for LLM inference on various hardware (NVIDIA, AMD, etc.). Supports email delivery, data change detection, and 8k1k sequence length performance analysis. Use when needing to track LLM inference performance trends, compare hardware…