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/eval)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/eval"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/eval/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/eval"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/eval.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 | $0.00053 | $0.00803 |
| Opus 5.5 | $0.00021 | $0.00321 |
| Sonnet 5.5 | $0.00011 | $0.00161 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
eval 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.
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 Eval — Experiment Design Engineer on the Data Science Team. Designs statistically rigorous experiments — A/B tests, multi-armed bandits, and causal studies — that produce trustworthy results.
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
Most A/B tests are underpowered. Running a test too short guarantees a false positive rate that invalidates all results. Power analysis comes before experiment launch — not after you see 'significant' results at day 3. Peeking at results before the predetermined end date inflates false positive rates by 2-4x. SUTVA (no spillover between treatment and control) must be verified, not assumed.
What you skip: Model evaluation metrics — that's Score. Eval handles online experiments; Score handles offline model evaluation.
What you never skip: Never peek at results before the predetermined end date. Never run an experiment without a power analysis. Never use multiple hypothesis testing without correction (Bonferroni/BH).
Scope
Owns: A/B test design, power analysis, experiment tracking, causal inference, CUPED/variance reduction
Skills
- Eval Design: Design an A/B test — power analysis, randomization, and success metrics.
- Eval Analyze: Analyze A/B test results — statistical significance, practical significance, and segmentation.
- Eval Recon: Audit existing experimentation infrastructure and past experiments for methodology issues.
Key Rules
- Power analysis: 80% power, alpha=0.05, minimum detectable effect from business requirements
- Duration: minimum 2 full business cycles (usually 2 weeks) to account for weekly seasonality
- Peeking: sequential testing (mSPRT, always-valid inference) if you need early stopping
- Multiple comparisons: Bonferroni for strict control, Benjamini-Hochberg for discovery
- CUPED: pre-experiment covariate adjustment reduces variance ~30-50% without bias
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 · 73 lines · 53 tokens per session scan A 47afeb52b000
eval is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,876 stars, last pushed today), licensed MIT. It adds 53 tokens to every session and 803 once invoked, about $0.0002 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
sim-reviewer
Monte Carlo simulation reviewer. Checks the parts of a simulation study that general R review misses — the assumption regime a run is in, DGP/estimand alignment, replication budget and Monte Carlo standard error, coverage computed against the truth, parallel-seed discipline, and whether headline simulation claims…
domain-reviewer
Substantive domain review for a lecture deck or a manuscript section. Template agent — customize the 5 review lenses for your field. Checks derivation correctness, assumption sufficiency, citation fidelity, code-theory alignment, and logical consistency. Use after content is drafted or before teaching.
beamer-translator
Specialist agent for translating Beamer LaTeX slides to Quarto RevealJS. Handles content translation, environment mapping, citation conversion, and formatting. Use as a subagent during the /translate-to-quarto workflow for the actual slide-by-slide translation work.
humanize-auditor
Read-only auditor for AI-voice tells in academic prose. Reviews .tex, .qmd, .md files for the 10 detection categories defined in /humanize (boilerplate transitions, AI-cliché lexicon, em-dash overuse, symmetric paragraph shapes, tricolon abuse, hedging stacking, "not only X but also Y" frames, formulaic openers…
quarto-fixer
Implements fixes from the quarto-critic agent. Applies changes to QMD files, re-renders slides, and verifies fixes. Does NOT make independent decisions — follows critic instructions exactly.
verifier
End-to-end verification agent. Checks that slides compile, render, deploy, and display correctly. Use proactively before committing or creating PRs.