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/cortex)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/cortex"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/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/jeremylongshore/tons-of-skills-marketplace/cortex"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/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 | $0.00075 | $0.02361 |
| Opus 5.5 | $0.00030 | $0.00944 |
| Sonnet 5.5 | $0.00015 | $0.00472 |
| Haiku 4.5 | $0.00007 | $0.00236 |
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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cortex — 86% identical, 31 lines differ
How it starts
The opening of the file, as written. The whole thing — 200 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.
- 1mo ago First seen · 200 lines · 75 tokens per session scan A 946c80be2712
cortex is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,876 stars, last pushed today), licensed MIT. It adds 75 tokens to every session and 2,361 once invoked, about $0.0003 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.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
Prompt Engineer
A specialized chat mode for analyzing and improving prompts. Every user input is treated as a prompt to be improved. It first provides a detailed analysis of the original prompt within a tag, evaluating it against a systematic framework based on OpenAI's prompt engineering best practices. Following the analysis, it…
prompt_engineer
Prompt engineering specialist for LLM prompt design, few-shot and chain-of-thought structuring, eval harnesses, and RAG retrieval quality. Use when the task requires writing or reviewing prompts, building evaluation datasets, tuning retrieval for a RAG system, or diagnosing regressions in LLM outputs. For example…
prompt-pipeline-runner
Executes the six-stage prompt-writer pipeline and produces two mandatory output artifacts (ready-to-run prompt, confidence report).
data-rag-architect
MANDATORY specialist subagent for Database Architecture, Hybrid Vector/Graph RAG, Real-Time OLAP Analytics (ClickHouse/DuckDB), Reactive Sync Engines, and Fine-Grained Authorization (ReBAC). Delegate to this subagent for schema design, vector search, ETL pipelines, and high-throughput data modeling.
ai-engineer
AI/ML Engineer (Reza Tehrani) - LLM seçimi, prompt engineering, RAG, AI agent mimarisi, fine-tuning.