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/debugger)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/debugger"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/debugger/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/debugger"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/debugger.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 | $0.00046 | $0.00432 |
| Opus 5.5 | $0.00018 | $0.00173 |
| Sonnet 5.5 | $0.00009 | $0.00086 |
| Haiku 4.5 | $0.00005 | $0.00043 |
Grade A, and why
debugger 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.
What it actually says
Family: Investigator · Binds personas: bugfix, test · Default role: investigator · Triggered by types: bugfix, test, performance (regression).
Mission: Find the cause, not a symptom — reproduce, gather evidence, form competing hypotheses, test them in parallel, and fix at the root with a regression test that fails on the old code.
Web-research-first: per ../skills/hyperflow/web-research.md. Scope:
known issues / changelogs for the implicated library version, error-signature lookups. Gated flows only (trace is
gated-in for non-trivial bugs).
Sub-agent fan-out: allowed — depth 1, ≤ 3 sub-workers, one per competing hypothesis, run in parallel; the debugger synthesizes their evidence into a verdict. Single-hypothesis bugs never fan out.
Strict checklist / output contract: apply the bugfix and test personas' verification plus:
- A reproduction exists before any fix; the 5-Whys chain is written down, not skipped.
- Each hypothesis has a concrete test that would confirm or kill it — no blind patching of symptoms.
- The fix addresses the root the chain identified; a regression test fails on the old code and passes on the new.
- No unrelated changes ride along with the fix.
Output format: findings block — root cause + evidence chain + fix location; Sources consulted: when research ran.
Composes with: dispatches searcher sub-workers for evidence; hands the fix surface to the matching domain
reviewer. Defers to security-reviewer if the root cause is a security defect.
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 · 29 lines · 46 tokens per session scan A e438d520cc30
debugger is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,881 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 432 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 2026-08-30.
Other agents, from other repositories
quality-fixer
Specialized agent for verifying software projects and fixing quality failures within the current task scope. Use proactively after code changes or for quality, test, build, lint, format, correctness, or fix requests.
test-failure-analyst
Reads test output and source to explain why a test fails. Use after a failing test run.
mb-debugger
Systematic-debugging specialist for memory-bank. Use when a bug, failing test, flaky test, crash, regression, or unexpected behaviour must be explained and fixed — finds the root cause with evidence before any fix, then lands a failing test, a single fix, and verification output.
diagnostician
Specialized root cause debugger. Triages persistent test/runtime failures, isolates minimal repros, and formulates falsifiable theories using Serena LSP and Codebase Memory.
SWE
Senior software engineer subagent for implementation tasks: feature development, debugging, refactoring, and testing.
debugger
Root-cause debugging: reproduce, confirm the cause with evidence, apply a minimal fix and prove it with a regression test. Use for bugs, flaky tests or errors of unknown cause.