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Ai Llm Inference

Listed
vasilyu1983·vlatest·ai-ml·MIT

Helps with AI, LLMs as part of building AI and machine learning applications workflows.

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This skill no longer installs under the name ai-llm-inference. vasilyu1983/ai-agents-public is deleted or private, so it cannot be installed from.

We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-22. We don't show a command we know will fail.

Repository install instructions

What we know about this skill

Publisher

vasilyu1983

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 15 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: api-docs.deepseek.com, arxiv.org, blog.vllm.ai, developer.nvidia.com, developers.redhat.com….

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    docs.vllm.aiundeclareddocs.sglang.ioundeclarednvidia.github.ioproject documentationgithub.comsource hostdeveloper.nvidia.comundeclareddocs.nvidia.comundeclareddevelopers.redhat.comundeclaredlmcache.aiundeclaredblog.vllm.aiundeclaredllm-d.aiundeclaredwww.oreilly.comundeclaredarxiv.orgundeclaredwww.usenix.orgundeclaredopentelemetry.ioundeclaredowasp.orgundeclaredvllm.aiundeclaredapi-docs.deepseek.comundeclaredhuggingface.comodel providerapi.openai.commodel provider

    What it found (3 of 76)

    • skills/universal/ai-llm-inference/data/sources.json:13contacts docs.vllm.ai
      "url": "https://docs.vllm.ai/",
    • skills/universal/ai-llm-inference/data/sources.json:19contacts docs.vllm.ai
      "url": "https://docs.vllm.ai/en/latest/features/structured_outputs/",
    • skills/universal/ai-llm-inference/data/sources.json:25contacts docs.vllm.ai
      "url": "https://docs.vllm.ai/en/latest/features/disagg_prefill/",

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned vasilyu1983/ai-agents-public at 3ee3637d25.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
3ee3637d25
Manifest
skills/universal/ai-llm-inference/SKILL.md
Scanned
42 files, 7,368 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill provides production-ready operational patterns for optimizing Large Language Model (LLM) inference performance, cost, and reliability. It centralizes decision rules, optimization strategies, configuration templates, and operational checklists specifically for LLM inference workloads. The focus is on practical execution; the skill avoids theoretical explanations and focuses on what Codex can directly implement.

When to use it

This skill is useful in the following scenarios:

  • Optimizing LLM inference latency or throughput
  • Choosing quantization strategies (FP8/FP4/INT8/INT4)
  • Configuring vLLM, TensorRT-LLM, or DeepSpeed inference
  • Scaling LLM inference across GPUs using tensor/pipeline parallelism
  • Building high-throughput LLM APIs

Key capabilities

  • SLO Management: Defines and monitors Service Level Objectives (SLOs) for latency.
  • Tail Latency Control: Implements scheduling, timeouts, admission control, queue caps, and backpressure to prevent latency spikes.
  • Throughput Optimization: Utilizes continuous batching and KV-cache aware serving techniques like PagedAttention/vLLM.
  • Cost Reduction: Employs model tiering and caching strategies.
  • Long Context Handling: Optimizes prefill processes for long input sequences.
  • Parallelism Strategies: Supports tensor, pipeline, and data parallelism to distribute models across GPUs.
  • Reliability Patterns: Implements timeouts, circuit breakers, and idempotency for high availability.
  • Speculative Decoding: Utilizes speculative decoding when latency is critical and a slightly lower quality draft model is acceptable.

Example prompts

Here are some example prompts you could give an AI agent with this skill:

  • "How can I reduce the inference latency of my LLM API?"
  • "What quantization strategies should I use to reduce cost per token?"
  • "Configure vLLM for optimal performance."
  • “Scale my LLM across multiple GPUs.”

Tips & gotchas

  • Quantization Validation: Always validate any quantization strategy on your evaluation dataset to ensure acceptable quality.
  • Scope Limitations: This skill focuses solely on inference optimization and does not cover prompting, tuning, RAG pipeline construction, deployment, or safety/governance—those are handled by other skills.
  • Practical Focus: The skill provides actionable strategies; it doesn't delve into the underlying theory.