together-ai-inference

together-ai-inference is a skill for Claude Code, Codex from synthetic-sciences/openscience. It costs 55 tokens per session (5,732 once invoked), scanned A, original, Apache-2.0.

A cloud service for running open-source AI models through an API that follows the OpenAI API style. It also provides embeddings, fine-tuning, image generation, and batch processing.

In plain words
What is it for?
Use it for chat or other model inference, embeddings, fine-tuning, image generation, function calls, structured responses, and batch jobs.
Why use it?
It gives applications access to hosted open-source models without requiring the developer to manage GPU servers.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it for chat or other model inference, embeddings, fine-tuning, image generation, function calls, structured responses, and batch jobs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/synthetic-sciences/openscience/together-ai
About the project

synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.

synthetic-sciences/openscience · 4,233 stars · on GitHub · openscience.sh

Install

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.

Any agent
npx skills add synthetic-sciences/openscience --skill together-ai
Clone the repo
git clone --depth 1 https://github.com/synthetic-sciences/openscience

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for together-ai-inference

README.md
[![agentmods](https://agentmods.dev/badge/skills/synthetic-sciences/openscience/together-ai/github.svg)](https://agentmods.dev/skills/synthetic-sciences/openscience/together-ai)
Your own site
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/together-ai"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/together-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.

agentmods 80×15 button for together-ai-inference

Your own site · 80×15
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/together-ai"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/together-ai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,732 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 5 findings, 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 Data Exfiltration · line 14
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 68
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 464
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 582
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 623
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00055 $0.05732
Opus 5.5 $0.00022 $0.02293
Sonnet 5.5 $0.00011 $0.01146
Haiku 4.5 $0.00006 $0.00573

Measured 1mo ago against content hash e1830fbce775, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

together-ai-inference 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 1mo 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.

backend/cli/skills/cloud-compute/together-ai/SKILL.md · 736 lines

How it starts

The opening of the file, as written. The whole thing — 736 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Together AI — Serverless Inference & Fine-Tuning

Together AI is an AI cloud platform providing serverless inference on 200+ open-source models through an OpenAI-compatible API. It supports chat completions, embeddings, fine-tuning, image generation, and batch processing at https://api.together.xyz/v1.

When to Use Together AI

Use Together AI when:

  • You need fast serverless inference on open-source models (Llama, DeepSeek, Qwen, Mistral)
  • You want an OpenAI-compatible API so you can swap providers with a single line change
  • You need to fine-tune open-source models without managing GPU infrastructure
  • You want cost-effective inference with pay-per-token pricing
  • You need function calling, JSON mode, or structured outputs from open-source models
  • You want batch processing at 50% lower cost for non-urgent workloads
  • You need embeddings or image generation alongside chat completions

Use alternatives instead:

Need Use Instead
Managed LoRA fine-tuning with training platform Tinker
Self-hosted inference with full control vLLM, TensorRT-LLM
Dedicated GPU instances Lambda Labs, RunPod
Serverless GPU with custom containers Modal
Multi-cloud cost optimization SkyPilot
Proprietary models (GPT-4o, Claude) OpenAI, Anthropic directly

Credential Setup

Credentials are auto-injected by openscience when connected via the dashboard.

# Verify credentials
[ -n "$TOGETHER_API_KEY" ] && echo "TOGETHER_API_KEY set" || echo "NOT SET"

If not set: add your Together AI key in Customize → Models or export TOGETHER_API_KEY locally.

Quick Start

Install

pip install together openai

Set API Key

import os
os.environ["TOGETHER_API_KEY"] = "your-api-key"

# Or export in shell:
# export TOGETHER_API_KEY="your-api-key"

Get your API key from https://api.together.xyz/settings/api-keys

Basic Chat Completion

from together import Together

client = Together()

response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct-Reference",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain gradient descent in one paragraph."},
    ],
    max_tokens=256,
    temperature=0.7,
)

print(response.choices[0].message.content)

Read the full file on GitHub · 736 lines

Changes

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.

  1. 1mo ago First seen · 736 lines · 55 tokens per session scan A e1830fbce775

Subscribe to this mod's changes

together-ai-inference is a skill published in the GitHub repository synthetic-sciences/openscience (4,233 stars, last pushed 2d ago), licensed Apache-2.0. It adds 55 tokens to every session and 5,732 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 2026-09-03.

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