Borrowing it
Nothing to install: this file belongs to Aviator-Coding/home-ops. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Aviator-Coding/home-ops/main/.claude/skills/litellm-proxy/SKILL.mdgit clone --depth 1 https://github.com/Aviator-Coding/home-opsWrote 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/aviator-coding/home-ops/litellm-proxy)<a href="https://agentmods.dev/skills/aviator-coding/home-ops/litellm-proxy"><img src="https://agentmods.dev/badge/skills/aviator-coding/home-ops/litellm-proxy.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 | $0.00102 | $0.04435 |
| Opus 5 | $0.00051 | $0.02218 |
| Sonnet 5 | $0.00020 | $0.00887 |
| Haiku 4.5 | $0.00010 | $0.00443 |
Grade A, and why
litellm-proxy 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 3d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LiteLLM proxy, operator CRs and the auto-router
Relocated verbatim from AGENTS.md on 2026-09-01 so it loads only when this subsystem is in play.
The text below is unchanged; only line breaks were inserted. AGENTS.md keeps a one-sentence pointer.
Add new findings here or to the owning document, not back into AGENTS.md - see its
"Maintaining this file" section for the rule.
- LiteLLM has no config-file-only way to declare a virtual key with a budget or rate limit - keys with
max_budget/rpm_limit/tpm_limitonly exist via the proxy's/key/generateREST API, and without a connected PostgresDATABASE_URLthat API stores keys in memory only (gone on every pod restart). This is whyai/litellm(governance layer,docs/ai-system/litellm/README.md) depends on the sharedpostgres-17CNPG cluster even though the rest ofai/is stateless-by-preference, and why its virtual keys are minted through that API rather than declared inconfig.yaml. Since captain decision O1 (2026-08-26) the minting is done by the home-operations litellm-operator (kubernetes/apps/base/ai/litellm-operator/, chart pinned in itsapp/helmrelease.yaml):LiteLLMVirtualKeyCRs inai/litellm/app/virtualkeys/replaced a hand-writtenconsumers.json+provision_keys.py+ hook Job + CronJob + RBAC, andLiteLLMModelCRs inapp/models/replaced theconfig.yamlConfigMap.
Eleven things to know before touching it:
(1) LiteLLMProxySpec has no securityContext, serviceAccountName, automountServiceAccountToken, strategy, initContainers or startupProbe field, so the operator-rendered proxy pod carries none of this repo's usual pod hardening and the default SA token is mounted - the accepted-gap table and why a kustomize patch cannot fix it (the operator reconciles that Deployment back) are in kubernetes/apps/base/ai/litellm/README.md.
(2) Because there are no initContainers, DB bootstrap is a standalone postgres-init Job (app/dbinit.yaml), not a CNPG Database CR: that CRD's spec.owner is required and CNPG does not create the role, which would force spec.managed.roles edits on the shared postgres-17 Cluster.
(3) The operator's ValidatingWebhookConfiguration is failurePolicy: Fail for every LiteLLM kind, so ai/litellm must dependsOn ai/litellm-operator with a Deployment healthCheck - with the operator down the API server rejects the CRs rather than leaving them unreconciled.
(4) LiteLLM key aliases are globally unique and the operator has no adopt-by-alias path, so a key that already exists under an alias blocks forever the CR that wants it: POST /key/generate returns 400 Key with alias '<name>' already exists. LiteLLMVirtualKeyReconciler only reaches its GetVirtualKey/UpdateVirtualKey branch when its own output Secret exists; with that Secret absent it can only ever retry generate. This bit the O1 cutover live on 2026-08-27 - the retired provision_keys.py keys survived in the Postgres DB the migration deliberately keeps, so both LiteLLMVirtualKeys sat Ready=False/GenerateFailed while the proxy looked perfectly healthy (Ready, 5 models rendered, still serving on the old keys) and the PushSecrets never wrote 1Password. Deleting the colliding key through the admin API (POST /key/delete) is therefore a required step, not tidy-up, whenever a key comes under operator management or an alias is reused. Expect the credential to rotate (the old value starts returning 401) and its accrued spend history to go with it, so treat it as a real rotation and check who holds that key first.
(5) A config-declared fallback in routerSettings BYPASSES the calling virtual key's model allow-list. Measured on v1.98.0: a key scoped to one local-shaped model was served by anthropic/claude-sonnet-5 and billed real USD, having never named it - litellm/router.py and router_utils/ contain no reference to the key's entitlements at all. A caller-SUPPLIED fallbacks array in the request body is checked (403), so only the router's own chain is unchecked. Consequence, and the reason qwen3.6-35b-a3b deliberately has NO fallback while the otherwise-identical chat-ha alias does: a cloud fallback may only be declared on an alias whose every consumer is already cloud-entitled, or captain decision D4's budget/entitlement split is silently void. Full evidence: docs/ai-system/litellm/fallbacks.md.
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.
- 3d ago First seen · 40 lines · 102 tokens per session scan A a3b6cc0fb07e
litellm-proxy is a skill published in the GitHub repository Aviator-Coding/home-ops (2 stars, last pushed today), licensed MIT. It adds 102 tokens to every session and 4,435 once invoked, about $0.0005 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 2026-09-04.
Other skills, from other repositories
tensorrt-llm
High-throughput LLM inference on NVIDIA GPUs.
google-cloud-solution-guided-gke-ai-migration
Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to…
agent-platform-tuning
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
gke-inference
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).
agent-platform-endpoint-management
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model…
modal
Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.