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.
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 synthetic-sciences/openscience --skill together-aigit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/together-ai)<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.
<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>- NVIDIA SkillSpector warn
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.
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 | $0.00055 | $0.05732 |
| Opus 5.5 | $0.00022 | $0.02293 |
| Sonnet 5.5 | $0.00011 | $0.01146 |
| Haiku 4.5 | $0.00006 | $0.00573 |
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.
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)
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.
- 1mo ago First seen · 736 lines · 55 tokens per session scan A e1830fbce775
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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