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BB litellm-setup

Set up a self-hosted LiteLLM proxy, the OpenAI-compatible LLM gateway, on this machine, from install through provider keys, config.yaml, a master key, optional Postgres and Admin UI, and a verified chat completion. Use when the user asks to set up LiteLLM, install the LiteLLM proxy, self-host an LLM gateway, or put one endpoint in front of OpenAI, Anthropic, Gemini, Azure, Bedrock, Ollama, or other providers. Not for using the LiteLLM Python SDK inside an app

BerriAI/litellm Agent Skills author: BerriAI 1 file body ≈ 4 629 tokens Open the sourcegithub.com↗ analyzed 20 h ago

Set up a self-hosted LiteLLM proxy, the OpenAI-compatible LLM gateway, on this machine, from install through provider keys, config.yaml, a master key…

As a process B 74/100 · Nearly there — weak spots: result and completion, running it twice

IntegrationPostgreSQLAzureDockerAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
98
Quality 40%
86
Run on models
none yet
Process rating
B
74/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Exfiltration read-dotenv SKILL.md:10
      Reads a .env file (detector / deny-list definition)
      Never print a secret. Provider keys, the master key, the salt key, the database URL, and virtual keys live only in `.env`, and `config.yaml` reads them through `os.environ/NAME`. Check that a value is
      detector
    • low Dangerous commands cmd-background-process SKILL.md:151
      Starts a background / autostarted process
      nohup litellm --config config.yaml --host 127.0.0.1 --port 4000 > litellm.log 2>&1 &

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 74/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 17 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4629 tokens
    • 100Steps. 20 steps
    • 100When it triggers. States when to use and when not to
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 10 top-level sections: this looks like several domains in one skill
    • high The skill tells the model to perform an irreversible action with no human approval
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 463: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (17 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.