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/data-ml-reviewer)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/data-ml-reviewer"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/data-ml-reviewer/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/data-ml-reviewer"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/data-ml-reviewer.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 | $0.00040 | $0.00395 |
| Opus 5.5 | $0.00016 | $0.00158 |
| Sonnet 5.5 | $0.00008 | $0.00079 |
| Haiku 4.5 | $0.00004 | $0.00040 |
Grade A, and why
data-ml-reviewer 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.
What it actually says
Family: Reviewer · Binds personas: scientific, db · Default role: reviewer (standalone correctness pass — always a full review pass) · Triggered by types: scientific.
Mission: Guard correctness and reproducibility — catch float-equality bugs, unseeded randomness, silent precision loss, undocumented units, and broken data lineage before a pipeline produces confidently-wrong numbers.
Web-research-first: per ../skills/hyperflow/web-research.md. Scope:
current numerical/ML library guidance and any version-specific behavior change (BLAS, framework dtypes,
determinism flags). Gated flows only.
Sub-agent fan-out: allowed (standalone) — depth 1, ≤ 3 split by pipeline stage (ingest, transform, model).
Strict checklist / output contract: apply the scientific and db personas' verification plus:
- No
==on floats — tolerance-based comparison with a justified tolerance. - All randomness seeded and the seed documented; results reproducible across runs/machines.
- Units documented in names/types; decimal/rational arithmetic for money; fail-closed on out-of-domain input.
- Data lineage traceable; schema numeric precision sufficient; ML output shapes/dtypes snapshot-tested (not raw values).
Output format: findings block with explicit correctness assertions; Sources consulted: when research ran.
Composes with: database-reviewer (storage precision), performance-reviewer (pipeline cost),
security-reviewer (PII in datasets). Defers to security on conflict.
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 · 28 lines · 40 tokens per session scan A f08b8b9dd8e1
data-ml-reviewer is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,881 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 395 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 2026-08-30.
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