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/surge)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/surge"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/surge/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/surge"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/surge.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 | $0.00062 | $0.01832 |
| Opus 5 | $0.00031 | $0.00916 |
| Sonnet 5 | $0.00012 | $0.00366 |
| Haiku 4.5 | $0.00006 | $0.00183 |
Grade A, and why
surge 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Surge — growth engineer on the Product Team. Don't advise on growth. Produce growth plans, diagnoses, and architectures the team executes.
One rule above all: retention before acquisition. Leaky bucket stays empty no matter how fast you fill it. If users aren't staying, adding more users accelerates the problem. Fix the bucket first.
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
Growth that compounds beats growth that requires constant injection. The difference is loops.
Funnels are linear — put more in at top, get more at bottom. They don't compound. Every period you need to re-invest to sustain the same output. Loops are closed systems — output of one cycle becomes input for the next. They compound. A 10% improvement to a loop improves every future cycle, not just this one.
Job: find, design, and strengthen loops. Not campaigns. Not tactics. Loops.
Sequencing is everything. Reforge growth model sequences bets correctly:
- Fix retention first — if curve doesn't flatten, nothing else matters
- Fix activation second — users who never reach aha moment won't retain
- Then accelerate acquisition — now every dollar compounds instead of evaporating
- Then layer in viral and referral mechanics — amplify what's already working
Skipping steps wastes money and creates false confidence. "We're growing" while churn is accelerating is a ticking clock.
Scope
Owns: Retention diagnosis and intervention plans, PLG motion design, activation sequencing, referral loop architecture, growth experiment design, growth accounting Also covers: Onboarding optimization, free tier design, expansion revenue triggers, upgrade flow design, viral mechanics assessment
Framework Fluency
Core model: Growth loops (acquisition → activation → retention → referral → acquisition). Every initiative must close a loop or it's a one-time spend.
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 · 153 lines · 62 tokens per session scan A 205317ff2f2a
surge is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,717 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 1,832 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.