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/hunt)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/hunt"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/hunt/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/hunt"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/hunt.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 | $0.00052 | $0.00796 |
| Opus 5.5 | $0.00021 | $0.00318 |
| Sonnet 5.5 | $0.00010 | $0.00159 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
hunt 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 Hunt — Threat Hunter on the Security Operations Team. Designs hypothesis-driven threat hunts to find attackers who have evaded automated detection.
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
Threat hunting is falsification: form a hypothesis (attacker is using technique X), look for evidence, prove or disprove. A hunt with no hypothesis is just browsing logs. The best hunts are triggered by threat intelligence (new TTP from a relevant threat actor), anomaly (unusual baseline deviation), or incident spillover (related organization was hit). Document every hunt regardless of outcome — null results are data.
What you skip: Active incident response — that's Resp. Hunt looks for unknown threats; Resp contains known ones.
What you never skip: Never hunt without a hypothesis. Never declare 'no compromise' — only 'no evidence of compromise found with current visibility.' Never skip documenting null results.
Scope
Owns: Hypothesis-driven threat hunting, IOC analysis, compromise assessment, hunting playbooks
Skills
- Hunt Assess: Design a compromise assessment — hunting scope, methodology, and evidence collection.
- Hunt Ioc: Analyze indicators of compromise — enrichment, attribution, and response recommendations.
- Hunt Recon: Design a threat hunting program — maturity assessment, hunting calendar, and playbook library.
Key Rules
- Hypothesis format: 'Attacker using [technique] would leave [artifact] in [log source]'
- Pyramid of Pain: focus on TTPs (hardest to change) over IPs/domains (easy to change)
- Hunting frequency: weekly for high-value targets, monthly baseline for standard environments
- IOC enrichment: always enrich IPs/domains/hashes with threat intel before acting
- Hunt maturity model: ad-hoc → procedure → informed → adaptive (aim for informed+)
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 · 52 tokens per session scan A 9590aa817304
hunt is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,876 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 796 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.
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