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Ai Llm

Listed
vasilyu1983·vlatest·ai-ml·MIT

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

Repository

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This skill no longer installs under the name ai-llm. 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 26 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: ai.google.dev, ai.pydantic.dev, arxiv.org, crfm.stanford.edu, developers.openai.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

    arxiv.orgundeclarednvlpubs.nist.govundeclaredeur-lex.europa.euundeclaredowasp.orgundeclaredmodelcontextprotocol.iodocumentationopentelemetry.ioundeclaredwww.oreilly.comundeclaredwww.manning.comundeclaredplatform.openai.comdocumentationopenai.comundeclareddocs.anthropic.comdocumentationai.google.devundeclaredplatform.claude.comundeclareddevelopers.openai.comundeclaredhelp.openai.comundeclaredhuggingface.comodel providerdocs.vllm.aiundeclareddocs.litellm.aiundeclareddocs.llamaindex.aiundeclaredlangchain-ai.github.ioproject documentationdocs.haystack.deepset.aiundeclaredqdrant.techundeclareddocs.pinecone.ioundeclaredmicrosoft.github.ioproject documentationai.pydantic.devundeclaredlearn.microsoft.comundeclaredopenai.github.ioproject documentationgoogle.github.ioproject documentationgithub.comsource hostcrfm.stanford.eduundeclareddocs.langchain.comundeclareddocs.arize.comundeclaredwandb.aiundeclareddocs.confident-ai.comundeclaredwww.braintrust.devundeclared

    What it found (3 of 55)

    • skills/universal/ai-llm/data/sources.json:16contacts arxiv.org
      "url": "https://arxiv.org/abs/1706.03762",
    • skills/universal/ai-llm/data/sources.json:25contacts arxiv.org
      "url": "https://arxiv.org/abs/2106.09685",
    • skills/universal/ai-llm/data/sources.json:440contacts arxiv.org
      "url": "https://arxiv.org/abs/2305.18290",

  • 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/SKILL.md
Scanned
13 files, 3,024 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 assists with developing, evaluating, deploying, and operating Large Language Model (LLM) systems according to modern production standards. It covers the entire LLM lifecycle, from initial strategy selection and dataset design to ongoing quality monitoring and incident response. The focus is on treating models as components within a larger system, emphasizing cost awareness, security-by-design, and repeatable evaluation processes.

When to use it

  • New product development: To guide the selection of LLM architecture (prompting, RAG, or fine-tuning).
  • Model procurement decisions: To evaluate potential models based on quality, latency, cost, privacy, and licensing considerations using a scoring matrix.
  • Cost optimization efforts: When needing to implement tiered model usage and caching strategies to minimize expenses per successful outcome.
  • Fine-tuning investments: To analyze the return on investment (ROI) of fine-tuning approaches like PEFT/LoRA.
  • Ensuring system reliability: When defining prompt contracts with structured output, constraints, and refusal rules for integration purposes.

Key capabilities

  • Architecture Selection Guidance: Provides decision trees to choose between prompting, RAG, or fine-tuning based on needs.
  • Model Scoring & Evaluation: Supports the creation of scoring matrices and automated evaluation pipelines (golden sets, A/B testing).
  • Cost Optimization Strategies: Includes techniques like tiered models, caching, prompt caching, and budget guardrails.
  • Fine-Tuning ROI Analysis: Offers tools to calculate break-even points and compare total cost of ownership for fine-tuning approaches.
  • Prompt Contract Definition: Enables the creation of structured prompts with constraints (JSON schema, token limits).
  • RAG Integration Patterns: Provides patterns for hybrid retrieval, reranking, packing, citing, and verifying information.

Example prompts

  • "What LLM architecture should I choose for a new product requiring current knowledge?"
  • "Can you show me how to calculate the ROI of fine-tuning an LLM with LoRA?"
  • "How can I implement prompt contracts to ensure reliable output from my LLM application?"

Tips & gotchas

  • Start Simple: Begin with simpler approaches like prompting before moving to more complex solutions such as RAG or fine-tuning.
  • Cost Awareness: Focus on measuring cost per successful outcome, not just tokens used.
  • Security First: Treat prompt injection and data leakage vulnerabilities as production code concerns.