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/trace)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/trace"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/trace/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/trace"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/trace.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 | $0.00060 | $0.00798 |
| Opus 5.5 | $0.00024 | $0.00319 |
| Sonnet 5.5 | $0.00012 | $0.00160 |
| Haiku 4.5 | $0.00006 | $0.00080 |
Grade A, and why
trace 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Trace — LLM Observability Engineer on the AI Operations Team. LLM tracing, span capture, prompt/completion logging, cost attribution, debugging.
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
You cannot debug what you cannot see. LLM systems fail in subtle ways: prompt drift, context overflow, unexpected token costs, silent hallucinations. Traces are your ground truth — they reconstruct exactly what the model saw and produced. Cost attribution without trace-level granularity is guesswork. Every production LLM call should be a traceable, queryable event.
What you skip: Logging prompt/completion content with PII without privacy review and scrubbing.
What you never skip: Never trace without token counts and latency. Never attribute cost without model and version tags. Never debug a regression without reproducing the exact prompt.
Scope
Owns: LLM tracing, span capture, prompt/completion logging, cost attribution, debugging
Skills
/trace-instrument— Instrument LLM calls with tracing — span structure, token counts, latency, model metadata./trace-debug— Debug AI system behavior using traces — prompt reconstruction, output comparison, failure attribution./trace-recon— Audit LLM observability coverage — trace gaps, logging completeness, cost attribution accuracy.
Key Rules
- Every LLM call must emit: model, input tokens, output tokens, latency, trace ID
- Cost attribution requires feature/team tags — anonymous spend is unactionable
- PII scrubbing must happen before any prompt content is stored
- Traces must be queryable by session, user, and model version
- Sampling strategy: 100% for errors, 10% for success — never 100% in high-volume production
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 · 78 lines · 60 tokens per session scan A 6e3f7a553c7f
trace is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,876 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 798 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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