Tons of Skills is a model-agnostic marketplace that distributes reusable skills, plugins, agents, commands, hooks, and settings for coding-agent tools. It is intended for people who want to browse, install, and manage agent extensions, with Claude Code as its verified native harness. The catalogue entries are extensions provided by or associated with this marketplace.
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/jeremylongshore/tons-of-skills-marketplaceWrote 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/jeremylongshore/tons-of-skills-marketplace/tune)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/tune"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/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/jeremylongshore/tons-of-skills-marketplace/tune"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/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 | $0.00074 | $0.00846 |
| Opus 5 | $0.00037 | $0.00423 |
| Sonnet 5 | $0.00015 | $0.00169 |
| Haiku 4.5 | $0.00007 | $0.00085 |
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 8d 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 — 74 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.
- 8d ago First seen · 74 lines · 74 tokens per session scan A 140662803546
tune is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,717 stars, last pushed today), licensed MIT. It adds 74 tokens to every session and 846 once invoked, about $0.0004 per session on Opus 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.
Other agents, from other repositories
ai-engineer
AI/ML Engineer (Reza Tehrani) - LLM seçimi, prompt engineering, RAG, AI agent mimarisi, fine-tuning.
ai-engineer
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use PROACTIVELY for LLM features, chatbots, or AI-powered applications.
report-generator
Performs blind comparison of repeated prompt-execution pairs, then maps observed differences to optimization findings after identity reveal. Use when original and optimized prompt trials are available.
ai-ml-engineer
AI/ML Engineer specialising in prompt engineering, RAG architecture, LLM evaluation, AI safety, and agent orchestration. Use when: "build an AI feature", "LLM", "ChatGPT", "Claude API", "prompt engineering", "RAG", "vector database", "embeddings", "fine-tuning", "AI agent", "LangChain", "LangGraph", "evaluation"…
prompt-engineer
Optimizes prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts. Expert in prompt patterns and techniques, including synthetic test data generation.
prompt-engineer
Optimizes prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts. Expert in prompt patterns and techniques.