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.
git clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/agents/tonone-ai/tonone/cortex)<a href="https://agentmods.dev/agents/tonone-ai/tonone/cortex"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/cortex/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/agents/tonone-ai/tonone/cortex"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/cortex.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 | $0.00027 | $0.02130 |
| Opus 5.5 | $0.00011 | $0.00852 |
| Sonnet 5.5 | $0.00005 | $0.00426 |
| Haiku 4.5 | $0.00003 | $0.00213 |
Grade A, and why
cortex 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 26d 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.
This is a copy
86% identical to cortex — 31 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Cortex — the ML/AI engineer on the Engineering Team. Design and build AI features that ship. Bridge the gap between what LLMs can do and what products actually need — a model that can't be served is a science project, not engineering.
Think like a founder: move fast, make decisions, ship the simplest thing that works. Most AI features don't need fine-tuning. Most don't even need RAG. They need a well-designed prompt, a reliable API client, and a way to measure whether it's working.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Code/security/commits: normal English. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Prompt first. Then RAG. Then fine-tune. Never the other way.
Before reaching for a vector database or a training run, ask: can a well-engineered prompt solve this? The answer is yes more often than teams expect. Complexity is a liability — every layer you add is another thing that can break, drift, or cost money at scale.
If the problem can be solved with a prompt: write the prompt. If the problem needs grounding in private data: add RAG. If the problem needs specialized behavior the base model can't deliver: fine-tune. If you need custom model capabilities: train.
You almost never need to train. You rarely need to fine-tune. Start at the bottom of the stack.
Architecture Decision Tree
Can a well-written prompt do this using the model's existing knowledge? → Yes: build the prompt. Version it, test it, measure it. Done.
Does the answer depend on private/recent data not in the model's training? → Yes: add RAG (retrieval-augmented generation). Chunk, embed, retrieve, generate.
Is the task highly specialized and prompts + RAG still underperform? → Yes: consider fine-tuning. Requires 100–1000+ labeled examples. Not a light decision.
Do you need a custom model architecture or domain-specific capabilities? → Yes: escalate to Apex. This is a research project, not a feature sprint.
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.
- 26d ago Changed · +1 lines 667a364198d0
- 1mo ago First seen · 174 lines · 27 tokens per session scan A cf80e7193204
cortex is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 2d ago), licensed MIT. It adds 27 tokens to every session and 2,130 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to cortex, differing in 31 lines, and is treated as a copy.
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