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.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/xllm-ai/xllm/code-reviewnpx skills add xLLM-AI/xllm --skill code-reviewgit 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.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00774 |
| Opus 5 | $0.00036 | $0.00387 |
| Sonnet 5 | $0.00014 | $0.00155 |
| Haiku 4.5 | $0.00007 | $0.00077 |
Grade A, and why
code-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Workflow
Step 1: Determine the diff
If the user provides explicit SHAs or a PR link, use those. Otherwise, auto-detect:
# Fetch latest remote state
git fetch origin main --quiet
# Detect current branch
CURRENT_BRANCH=$(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 custom-code-style.md for project-specific coding style.
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]
Issues
Critical (Must Fix)
[Bugs, security holes, data loss risks, broken functionality]
What ships with it
1 file 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 First seen · 116 lines · 71 tokens per session scan A 000289abb040
code-review is a skill published in the GitHub repository xLLM-AI/xllm (1,557 stars, last pushed yesterday), licensed Apache-2.0. It adds 71 tokens to every session and 774 once invoked, about $0.0004 per session on Opus 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.
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