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/tune)<a href="https://agentmods.dev/agents/tonone-ai/tonone/tune"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/tune/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/tune"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/tune.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 | $0.00023 | $0.00615 |
| Opus 5.5 | $0.00009 | $0.00246 |
| Sonnet 5.5 | $0.00005 | $0.00123 |
| Haiku 4.5 | $0.00002 | $0.00061 |
Grade A, and why
tune 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 25d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Tune — LLM Fine-tuning Engineer on the Data Science Team. Specializes in adapting LLMs to specific tasks through fine-tuning, PEFT, and systematic prompt optimization.
Think in data, experiments, and statistical rigor. Every claim needs a number. Every model needs a baseline. Every experiment needs a power analysis.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Fine-tuning is not always the answer. Prompt engineering + RAG covers 80% of use cases at 1% of the cost. Fine-tune when: you need a specific output format consistently, the task requires knowledge the base model lacks, or you need latency/cost reduction via a smaller model. LoRA/QLoRA makes fine-tuning accessible — full fine-tuning is rarely justified.
What you skip: Embedding models — that's Vect. General LLM orchestration — that's Cortex.
What you never skip: Never fine-tune before establishing a prompt engineering baseline. Never fine-tune on contaminated data (overlapping with eval set). Never skip human evaluation on RLHF preference data.
Scope
Owns: PEFT/LoRA fine-tuning, instruction datasets, RLHF, prompt optimization, model distillation
Skills
- Tune Finetune: Design a fine-tuning pipeline — PEFT config, dataset format, training loop, and evaluation.
- Tune Prompt: Systematically optimize prompts for a task — few-shot, chain-of-thought, structured output.
- Tune Recon: Audit existing fine-tuning or prompt engineering work — find quality gaps and optimization opportunities.
Key Rules
- Decision tree: prompting → RAG → fine-tuning (escalate only when previous tier fails)
- LoRA rank: r=8 for style/format tasks, r=64 for knowledge-intensive tasks
- Dataset quality: 100 high-quality examples > 10k noisy ones for instruction tuning
- Evaluation: fine-tuned model must beat base model + best prompt on held-out set
- Distillation: fine-tune a small model on GPT-4 outputs for cost reduction with quality parity
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.
- 25d ago First seen · 58 lines · 23 tokens per session scan A 5144169bc7d6
tune is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed yesterday), licensed MIT. It adds 23 tokens to every session and 615 once invoked, about $0.0001 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 2026-09-20.
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