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 rhtevan/agentfs --skill goose-litellm-providergit clone --depth 1 https://github.com/rhtevan/agentfsWrote 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/rhtevan/agentfs/goose-litellm-provider)<a href="https://agentmods.dev/skills/rhtevan/agentfs/goose-litellm-provider"><img src="https://agentmods.dev/badge/skills/rhtevan/agentfs/goose-litellm-provider/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/rhtevan/agentfs/goose-litellm-provider"><img src="https://agentmods.dev/badge/skills/rhtevan/agentfs/goose-litellm-provider.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00019 | $0.02295 |
| Opus 5 | $0.00010 | $0.01148 |
| Sonnet 5 | $0.00004 | $0.00459 |
| Haiku 4.5 | $0.00002 | $0.00230 |
Grade A, and why
goose-litellm-provider 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 9d 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Configure Goose with a Local LiteLLM Proxy Provider
Set up Goose (CLI and Desktop) to use a local LiteLLM proxy as a
custom provider. This covers the RedHat provider pattern — a local
LiteLLM proxy backed by Vertex AI Claude models (no auth,
http://localhost:4000).
For remote MaaS (Model as a Service) setup, see the goose-maas-provider
skill instead.
Prerequisites
- Goose installed (
gooseCLI or Goose Desktop) - LiteLLM proxy running locally (see skill
litellm-vertex-ai-proxyto set one up); must be accessible athttp://localhost:4000(default LiteLLM port) - Use skill
litellm-proxy-statusto verify the proxy is healthy before proceeding
Model Selection Architecture
Goose uses two separate model settings for this provider. They are independent — neither overwrites the other.
| Setting | Where | Purpose |
|---|---|---|
| Default model | config.yaml → providers.custom_redhat.model |
Main conversation model |
| Fast model | custom_redhat.json → fast_model |
Lightweight model for auxiliary calls (tool-selection, classification, session titles) |
Reference Configuration
The custom provider is defined as a JSON file under
~/.config/goose/custom_providers/ with a matching entry in
~/.config/goose/config.yaml.
Custom Provider JSON
File: ~/.config/goose/custom_providers/custom_redhat.json
⚠️ Use this exact schema. Do NOT write from memory or improvise field names. Copy this template and substitute only the marked placeholders.
{
"name": "custom_redhat",
"engine": "openai",
"display_name": "RedHat",
"description": "Local LiteLLM proxy to Vertex AI (Claude models)",
"api_key_env": "",
"base_url": "http://localhost:4000",
"models": [
{
"name": "claude-opus-4-6",
"context_limit": 1000000,
"input_token_cost": null,
"output_token_cost": null,
"currency": null,
"supports_cache_control": null,
"reasoning": false
},
{
"name": "claude-sonnet-4-6",
"context_limit": 1000000,
"input_token_cost": null,
"output_token_cost": null,
"currency": null,
"supports_cache_control": null,
"reasoning": false
},
{
"name": "claude-haiku-4-5",
"context_limit": 200000,
"input_token_cost": null,
"output_token_cost": null,
"currency": null,
"supports_cache_control": null,
"reasoning": false
}
],
"headers": null,
"timeout_seconds": 600,
"supports_streaming": true,
"requires_auth": false,
"catalog_provider_id": null,
"base_path": null,
"env_vars": null,
"dynamic_models": null,
"skip_canonical_filtering": false,
"model_doc_link": null,
"setup_steps": [],
"fast_model": "claude-haiku-4-5",
"preserves_thinking": true
}
What ships with it
3 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.
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.
- 9d ago First seen · 274 lines · 19 tokens per session scan A 761d005994b5
goose-litellm-provider is a skill published in the GitHub repository rhtevan/agentfs (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 19 tokens to every session and 2,295 once invoked, about $0.0001 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 2026-08-31.
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