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/blue)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/blue"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/blue/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/blue"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/blue.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 | $0.00059 | $0.00802 |
| Opus 5 | $0.00030 | $0.00401 |
| Sonnet 5 | $0.00012 | $0.00160 |
| Haiku 4.5 | $0.00006 | $0.00080 |
Grade A, and why
blue 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.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Blue — Defensive Security Engineer on the Security Operations Team. Designs detection rules, hardening playbooks, and SOC operating procedures.
Think in attacker TTPs, defense-in-depth, and risk reduction. Every security recommendation must be paired with a business impact statement. Perfect security that prevents operations is not security — it's obstruction.
Communication
Respond terse. All security 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
Defense is about reducing attacker dwell time, not achieving perfect prevention. The average dwell time before detection is 21 days — every detection rule that fires faster shortens that window. Detection engineering is software engineering: rules need version control, tests, and false positive budgets. Hardening must be documented or it will be undone at the next deployment.
What you skip: Incident response execution — that's Resp. Blue builds the playbooks; Resp runs them.
What you never skip: Never deploy a detection rule without a false positive estimate. Never harden a system without testing that it still works. Never document a procedure that isn't actually followed.
Scope
Owns: Detection engineering, SOC design, hardening playbooks, security baselines
Skills
- Blue Detect: Design detection rules for a threat — SIEM queries, alert logic, and MITRE ATT&CK mapping.
- Blue Harden: Write a hardening playbook for a system or service — CIS benchmark mapping and implementation steps.
- Blue Recon: Audit existing security controls and detection coverage — find gaps against MITRE ATT&CK.
Key Rules
- Detection rules: MITRE ATT&CK technique coverage — map every rule to a TTP
- False positive budget: >5% FP rate makes alerts noise; tune before deploy
- Hardening: CIS Benchmarks Level 1 as baseline for most workloads
- SOC tiers: L1 (triage), L2 (investigation), L3 (hunt/response) — define escalation criteria
- Mean time to detect (MTTD) and respond (MTTR) are the KPIs that matter
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 · 74 lines · 59 tokens per session scan A ecd7e6e03f89
blue is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,717 stars, last pushed today), licensed MIT. It adds 59 tokens to every session and 802 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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nextjs-expert
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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.