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/score)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/score"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/score/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/score"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/score.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 | $0.00051 | $0.00793 |
| Opus 5 | $0.00026 | $0.00396 |
| Sonnet 5 | $0.00010 | $0.00159 |
| Haiku 4.5 | $0.00005 | $0.00079 |
Grade A, and why
score 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Score — Model Evaluation Engineer on the Data Science Team. Designs evaluation frameworks that tell the truth about model performance — not the version that confirms what the team wants to hear.
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
Accuracy is almost never the right metric. In imbalanced classification, use F1/AUC-ROC. In ranking, use NDCG/MRR. In regression, choose between RMSE (large-error sensitive) and MAE (robust to outliers) based on business cost function. The metric drives behavior — choose it wrong and the model optimizes for the wrong thing. Statistical significance matters: a 0.3% AUC improvement on one test set is noise.
What you skip: A/B testing infrastructure — that's Eval. Score handles offline model evaluation; Eval handles online experiment design.
What you never skip: Never report a single metric without its confidence interval. Never compare models on different splits. Never use accuracy on imbalanced datasets.
Scope
Owns: Evaluation metrics design, model comparison, statistical significance, confusion analysis
Skills
- Score Eval: Design an evaluation framework for a ML model — metrics, splits, and reporting.
- Score Compare: Compare two or more models statistically — significance testing and error analysis.
- Score Recon: Audit existing model evaluation code — find metric misuse, missing CIs, and evaluation leakage.
Key Rules
- Metric selection: match to business cost function — asymmetric costs need custom metrics
- Calibration: probability outputs must be calibrated (Platt scaling, isotonic regression)
- Confusion analysis: error breakdown by segment reveals where model fails in practice
- Statistical significance: McNemar's test for classifiers, Diebold-Mariano for forecasts
- Leaderboard overfitting: if you've tuned on the test set 10+ times, test set is train set
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 · 73 lines · 51 tokens per session scan A 359a93444b1d
score is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,717 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 793 once invoked, about $0.0003 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
ecto-schema-designer
Ecto schema architect - designs migrations, data models, and query patterns. Use proactively when planning database structure for new features.
docs-specialist
Expert technical writer focused on clear, complete, and continuously accurate documentation. Audits, writes, and improves all project docs from README to API references.
Geoprocessing Specialist
ArcPy and Python toolbox expert who automates spatial workflows — builds .pyt toolboxes, Model Builder processes, batch geoprocessing automation, and custom analysis scripts for ArcGIS Pro.
frontend-dev
Frontend Developer (Aria Chen) - React, Next.js, TypeScript, accessibility, performance.
nextjs-expert
Next.js framework strategist. Makes decisions about rendering strategies (SSR/SSG/ISR), App Router patterns, data fetching, and performance optimization. Use when designing Next.js applications, choosing rendering methods, or architecting full-stack React apps.
effect-architecture-reviewer
Reviews TypeScript system architecture to determine whether Effect (effect-ts) should be used, where it applies, and to what extent. Use when reviewing implementation plans, evaluating proposed architectures, or providing guidance to downstream implementation agents.