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 Veightor/chutes-agent-toolkit --skill chutes-aigit clone --depth 1 https://github.com/Veightor/chutes-agent-toolkitWrote 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/veightor/chutes-agent-toolkit/chutes-ai)<a href="https://agentmods.dev/skills/veightor/chutes-agent-toolkit/chutes-ai"><img src="https://agentmods.dev/badge/skills/veightor/chutes-agent-toolkit/chutes-ai/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/veightor/chutes-agent-toolkit/chutes-ai"><img src="https://agentmods.dev/badge/skills/veightor/chutes-agent-toolkit/chutes-ai.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 | $0.00038 | $0.01245 |
| Opus 5.5 | $0.00015 | $0.00498 |
| Sonnet 5.5 | $0.00008 | $0.00249 |
| Haiku 4.5 | $0.00004 | $0.00125 |
Grade A, and why
chutes-ai 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 17d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chutes.ai for Hermes
Use this skill when the user asks about:
- Chutes.ai setup or usage
- open-source model inference through Chutes
- model routing / failover / latency / throughput
- TEE / confidential compute on Chutes
- billing, balances, discounts, or quotas on Chutes
- using Chutes as a Hermes model backend
Source of truth for live model metadata
When current model inventory, pricing, supported features, TTFT/TPS, or TEE status matter, use:
https://llm.chutes.ai/v1/models
Do not treat static model snapshots as authoritative.
Base URLs
- Management API:
https://api.chutes.ai - Inference API:
https://llm.chutes.ai/v1
Authentication
Use a cpk_... API key. Store it as CHUTES_API_KEY in ~/.hermes/.env or in this repo's keychain-backed credential manager; never paste the raw key into config examples or chat.
Chutes auth was re-verified in the shared Chutes skills on 2026-06-11 and again 2026-10-07: use standard bearer authorization semantics (Authorization: Bearer plus the cpk_... value) for configured providers. GET /v1/models is public and should not be used as proof that a specific auth header works. Older April notes about X-API-Key are superseded for Hermes-facing setup.
Hermes setup pattern
Typical Hermes config:
providers:
chutes:
name: Chutes
base_url: https://llm.chutes.ai/v1
key_env: CHUTES_API_KEY
transport: chat_completions
default_model: default:latency
discover_models: true
models:
default: {}
"default:latency": {}
"default:throughput": {}
model:
provider: custom:chutes
default: default:latency
Legacy Hermes configs may use custom_providers: with equivalent name, base_url, key_env, api_mode, and model fields.
The former research opt-in endpoint (research-data-opt-in-proxy.chutes.ai) is defunct — verified 404 and no longer advertised as of 2026-10-07. Do not configure it.
Routing guidance
Chutes supports:
defaultdefault:latencydefault:throughput- inline routing strings like
modelA,modelB,modelC:latency
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.
- 17d ago Changed · -11 lines 0dffdeed2b79
- 1mo ago First seen · 137 lines · 38 tokens per session scan A b7bc2957fb65
chutes-ai is a skill published in the GitHub repository Veightor/chutes-agent-toolkit (7 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 1,245 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
bittensor
Use when a developer asks what a Bittensor subnet does, whether it's up right now, how to call/integrate its API, or what mining/validating one costs and earns — e.g. "which subnet does image generation", "is subnet 7 healthy", "call the Beam API for me", "does mining subnet 3 need a GPU", "what does the median miner…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.