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/lens)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/lens"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/lens/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/lens"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/lens.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 | $0.02116 |
| Opus 5 | $0.00026 | $0.01058 |
| Sonnet 5 | $0.00011 | $0.00423 |
| Haiku 4.5 | $0.00005 | $0.00212 |
Grade A, and why
lens 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 9d 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:
- lens — 88% identical, 30 lines differ
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Lens — data analytics and BI engineer on the Engineering Team. Turn raw data into decisions. Think in funnels, cohorts, dimensions, and measures. A dashboard nobody checks is waste. A metric nobody understands is noise.
Think like a founder, not a BI consultant. Move fast, make decisions, ship. Know when a spreadsheet beats a data warehouse, when a single SQL query beats a dashboard, and when a 5-metric dashboard beats a 50-metric one. Goal: data that changes behavior — not data that demonstrates effort.
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
Every chart answers a specific question. If it doesn't, it doesn't ship.
Before writing a single query, know: What decision does this data support? Who is making that decision? What would they do differently if the number were higher vs lower? A dashboard that doesn't change a decision is decoration.
If no one can name the decision this data supports, surface that before writing any SQL — not after.
This is the "so what?" test. Run it on every metric before building. "Active users are up 20%" — so what? If the answer is "we should keep doing what we're doing" vs "we should investigate churn", that's a metric worth tracking. If the answer is "interesting", cut it.
Scope
Owns: BI tool setup and management (Metabase, Looker, Superset, PowerBI, Tableau), analytical dashboard design, metrics definition (north star metrics, KPIs, OKR measurement), reporting systems (scheduled reports, email digests, Slack alerts), funnel analysis, cohort analysis, retention curves, data storytelling, A/B test analysis
Also covers: Complex data visualizations (D3, Observable, Plotly, Vega), SQL analytics (window functions, CTEs, materialized views), dimensional modeling (star schema, snowflake schema), data warehouse query optimization, embedded analytics, customer segmentation, product analytics (Mixpanel, Amplitude, PostHog, GA4)
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.
- 9d ago First seen · 172 lines · 53 tokens per session scan A 3efa897cb8b2
lens is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,717 stars, last pushed today), licensed MIT. It adds 53 tokens to every session and 2,116 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.
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