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 skills add magnus919/agent-skills --skill litellmgit clone --depth 1 https://github.com/magnus919/agent-skillsWrote 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/magnus919/agent-skills/litellm)<a href="https://agentmods.dev/skills/magnus919/agent-skills/litellm"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/litellm/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/magnus919/agent-skills/litellm"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/litellm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 46 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00173 | $0.04321 |
| Opus 5 | $0.00086 | $0.02160 |
| Sonnet 5 | $0.00035 | $0.00864 |
| Haiku 4.5 | $0.00017 | $0.00432 |
Grade A, and why
litellm 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 7d 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 — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LiteLLM AI Gateway Operations
Use this skill to operate LiteLLM as an organization's AI gateway: run the proxy
(litellm --config config.yaml), route requests to 100+ LLM providers through one
OpenAI-compatible API, manage model lists, routing and reliability, virtual keys,
teams, budgets and rate limits, caching, guardrails, observability, and spend — and
diagnose failures with evidence. LiteLLM ships two surfaces: a Python SDK
(litellm.completion(), in-process) and the proxy (a FastAPI service on port 4000
with keys, budgets, and an admin UI). This is a tool skill for the named tool.
Engine selection and serving methodology belong to
ml-engineering; operating a single engine belongs to
vllm or llama-cpp.
Operating contract
- Record the deployment before tuning it. Capture the pinned image or pip
version,
config.yaml, model list, routing, budgets, env-var references, and data stores in the proxy config record. That record is the rollback unit. - Confirm the target, scope, and rollback path before mutating. Read-only
discovery (health probes,
/v1/models, logs, spend queries) may proceed without confirmation. Mutations — config changes, key mint/revocation, restarts, image upgrades, DB migrations — require an explicit human directive naming the deployment. - A proxy that responds is not a proxy that serves.
/health/livelinessreturning 200 proves liveness only. Verify at the delivery boundary: a representative/v1/chat/completionsrequest returns tokens andx-litellm-model-idnames the deployment you expected. - Keep evidence bounded. Summarize logs and configs; never dump full logs,
.envcontents, master keys, or provider credentials into chat. Spend logs and debug output can contain prompt content — redact before sharing. - Pin versions. LiteLLM releases weekly and changes defaults; every claim here was checked against 1.97.0 (2026-08-22). Re-verify version-sensitive behavior against your installed release before relying on it.
What ships with it
15 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.
- evals/evals.json 14 KB
- README.md 5.4 KB
- references/00-source-index.md 5.9 KB
- references/01-quickstart-and-sdk.md 8.4 KB
- references/02-config-and-routing.md 6.8 KB
- references/03-keys-teams-budgets-spend.md 7.5 KB
- references/04-caching-and-guardrails.md 4.6 KB
- references/05-observability-and-logging.md 5.1 KB
- references/06-deployment.md 7.1 KB
- references/07-security-and-public-hosting.md 7.8 KB
- references/08-troubleshooting.md 11 KB
- scripts/litellm-health 8.5 KB
- templates/proxy-config-record.md 3.0 KB
- templates/proxy-deployment.md 3.2 KB
- tests/test_litellm_health.py 14 KB runs code
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.
- 7d ago First seen · 287 lines · 173 tokens per session scan A a6d59f2dd3b9
litellm is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed today), licensed MIT. It adds 173 tokens to every session and 4,321 once invoked, about $0.0009 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 2026-09-03.
Other skills, from other repositories
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open-source
Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browseruse, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle…
data-leakage-detection
Detect sensitive information disclosure via escalating dialogue probes. Covers system prompt extraction, credential/API key leakage, PII, and internal configuration exposure.
server-inference
Use this skill when the user wants to run or debug MLX-VLM server inference, including uv run mlxvlm.server, /v1/models, /v1/chat/completions, /v1/responses, streaming, OpenAI-compatible clients, health checks, metrics, model unload/reload, adapters, trust-remote-code, and server request/response failures.
groq-inference
Ultra-fast LLM inference on custom LPU hardware. OpenAI-compatible API at api.groq.com. Lowest latency in the industry (500-1000+ tok/s). Supports chat completions, vision, audio (Whisper STT + TTS), tool calling, JSON mode, and streaming. Free tier available. Inference only — no training.
map
Build and commit a Cortex function knowledge graph — maps structural dependencies and domain intent relationships across all AI functions in the project. Supports --reduce (default on) for transitive reduction of the dependency graph.