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.
npx skills add lodetomasi/claude-code-training-lab --skill code-reviewgit clone --depth 1 https://github.com/lodetomasi/claude-code-training-labWrote 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/skills/lodetomasi/claude-code-training-lab/code-review)<a href="https://agentmods.dev/skills/lodetomasi/claude-code-training-lab/code-review"><img src="https://agentmods.dev/badge/skills/lodetomasi/claude-code-training-lab/code-review/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/skills/lodetomasi/claude-code-training-lab/code-review"><img src="https://agentmods.dev/badge/skills/lodetomasi/claude-code-training-lab/code-review.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 | $0.00023 | $0.00196 |
| Opus 5.5 | $0.00009 | $0.00078 |
| Sonnet 5.5 | $0.00005 | $0.00039 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
code-review 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 28 lines · 23 tokens per session scan A 3998af3e046b
code-review is a skill published in the GitHub repository lodetomasi/claude-code-training-lab (2 stars, last pushed 8mo ago), with no licence file. It adds 23 tokens to every session and 196 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
dare-review
Audita uma task DARE implementada — cruza a spec com o código real para detectar stubs, mocks fora de testes, funções vazias, TODOs e validar critério-a-critério se a implementação satisfaz o que a spec prometeu. Use antes de marcar uma task como DONE, ou quando o dev pedir revisão manual. Combina análise estática…
codex-cross-review
Use when the user wants Codex to review Claude's work, cross-validate PRs, or run high-volume PR review with dual-model verification using the codex-plugin-cc plugin.
confidence-based-strictness
Use when the complexity or unfamiliarity of a task should increase guardrail enforcement. Scales verification depth based on how confident the system is in the correctness of the generated code.
code-complexity
Enforce cyclomatic complexity limits, function/file length limits, nesting depth, parameter counts, and duplication detection. Keeps code readable and maintainable.
requesting-code-review
Invoke the code-reviewer agent with git-based commit ranges to get structured feedback on code quality, architecture compliance, and correctness.
receiving-code-review
Process code review feedback with technical verification — not performative agreement. Fix real issues, push back on incorrect feedback with evidence.