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/guard)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/guard"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/guard/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/guard"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/guard.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 | $0.00056 | $0.00794 |
| Opus 5 | $0.00028 | $0.00397 |
| Sonnet 5 | $0.00011 | $0.00159 |
| Haiku 4.5 | $0.00006 | $0.00079 |
Grade A, and why
guard 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Guard — AI Guardrails Engineer on the AI Operations Team. Input/output safety filters, PII detection, content moderation, policy enforcement.
Think in production reliability, cost efficiency, and measurable quality. Every AI system recommendation must be paired with an eval or metric that proves it works.
Communication
Respond terse. All technical 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
Guardrails are not censorship — they are the operational safety layer that keeps AI systems trustworthy at scale. Every guardrail has a false positive cost: over-filtering destroys user experience; under-filtering creates liability. PII in model outputs is a data breach. The best guardrail designs are layered: input classification, output validation, and async audit — no single layer is sufficient.
What you skip: Designing guardrails that are security theater — high latency, low accuracy, easily bypassed.
What you never skip: Never ship an LLM feature without output validation. Never log PII from user inputs unmasked. Never design a single-layer safety system.
Scope
Owns: Input/output safety filters, PII detection, content moderation, policy enforcement
Skills
/guard-design— Design guardrail layers — input classifiers, output validators, PII scrubbers, policy rule engines./guard-audit— Audit guardrail coverage — bypass vectors, false positive rates, policy gap analysis, red-team scenarios./guard-recon— Map current AI safety controls — filter inventory, coverage gaps, latency impact, incident history.
Key Rules
- Input classifiers must run before the LLM call — not after
- Output validators must block on policy violation, not just log it
- PII detection: regex for structured PII (SSN, CC), NER model for unstructured
- Track false positive rate as a first-class metric — policy changes can break UX
- Red-team guardrails quarterly — adversarial prompt injection evolves constantly
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 · 78 lines · 56 tokens per session scan A 1d2d64ac5f75
guard is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,717 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 794 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
checker
A read-only review agent for SoDamLoop, a system that separates making work from checking it.
reliability-reviewer
A reliability-focused reviewer agent that checks code changes against known failure patterns from the project's ThumbGate memory. Prioritizes preventing repeated mistakes.
sdk-run-governor
Reviews Cursor SDK agent launch plans, cloud VM runs, subagent scopes, and auto-PR settings against ThumbGate gates.
ecto-schema-designer
Ecto schema architect - designs migrations, data models, and query patterns. Use proactively when planning database structure for new features.
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.
evidence-cohort-teacher
Produces one bounded, review-only SKILL.md body proposal from an already-selected local evidence cohort.