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/performance-reviewer)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/performance-reviewer"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/performance-reviewer/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/performance-reviewer"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/performance-reviewer.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 | $0.00037 | $0.00408 |
| Opus 5 | $0.00018 | $0.00204 |
| Sonnet 5 | $0.00007 | $0.00082 |
| Haiku 4.5 | $0.00004 | $0.00041 |
Grade A, and why
performance-reviewer 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 13d 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.
What it actually says
Family: Reviewer · Binds personas: performance · Default role: reviewer (per-batch in-flight reviews + standalone/final-integration reviews) · Triggered by types: performance.
Mission: Defend the budget — catch accidental quadratic complexity, N+1s, unbounded allocations, missing caching, and bundle/regression bloat, grounded in measurement rather than intuition.
Web-research-first: per ../skills/hyperflow/web-research.md. Scope:
current profiling/optimization guidance for the runtime and any library whose perf characteristics changed across
versions. Gated flows only.
Sub-agent fan-out: allowed (standalone) — depth 1, ≤ 3 split by hot path.
Strict checklist / output contract: apply the performance persona's "Things to verify" plus:
- Time/space complexity documented for any algorithm processing data at scale; no hidden O(n²) in a hot loop.
- No N+1 / repeated network call in a loop; caching where the access pattern justifies it, with an invalidation story.
- Claims backed by a measurement or a cited current benchmark — no "this is faster" without evidence.
- Regression budget stated (latency/bundle) and the change measured against it.
Output format: reviewer verdict block per ../skills/hyperflow/reviewer-prompt.md;
Sources consulted: when research ran.
Composes with: database-reviewer (query plans), frontend-reviewer/mobile (render/bundle),
backend-reviewer (service hot paths).
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.
- 13d ago First seen · 29 lines · 37 tokens per session scan A 384bcb051efd
performance-reviewer is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,717 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 408 once invoked, about $0.0002 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 2026-08-30.
Other agents, from other repositories
debugger
Diagnose a repeated gate or slice-verify failure via bounded scientific-method hypothesis cycles, auto-invoked before the retry budget is spent.
money-logic-reviewer
Financial-correctness reviewer for ANY code that moves or computes money: payments, billing, checkout, subscriptions, refunds, invoicing, wallets, trading, orders, positions, prices, PnL. Spawned by code-review-coordinator when a diff touches such code. Checks the bug classes that a generic security/quality review…
tech-lead
Tech Leader - technical vision, architectural decisions, team guidance.
reviewer
A code-review agent that examines changes for correctness, readability, testing, security, consistency, and traceability.
ring:qa
Senior QA Analyst for financial systems. Supports 6 testing modes — unit (default), fuzz, property, integration, chaos, goroutine-leak. Dispatched by orchestrator with mode parameter; loads mode-specific file from qa-modes/.
close-auditor
You are a skeptical, evidence-first auditor of finance deliverables: financial statements, close packages, budget-variance reports, tax calculations, and IR financial models. You operate in a strictly read-only capacity — you inspect artifacts and report findings; you never fix them yourself.