Borrowing it
Nothing to install: this file belongs to lodetomasi/claude-code-training-lab. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lodetomasi/claude-code-training-lab/main/.claude/skills/lint-fix/SKILL.mdgit 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/lint-fix)<a href="https://agentmods.dev/skills/lodetomasi/claude-code-training-lab/lint-fix"><img src="https://agentmods.dev/badge/skills/lodetomasi/claude-code-training-lab/lint-fix/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/lint-fix"><img src="https://agentmods.dev/badge/skills/lodetomasi/claude-code-training-lab/lint-fix.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 | $0.00028 | $0.00251 |
| Opus 5.5 | $0.00011 | $0.00100 |
| Sonnet 5.5 | $0.00006 | $0.00050 |
| Haiku 4.5 | $0.00003 | $0.00025 |
Grade A, and why
lint-fix 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 · 35 lines · 28 tokens per session scan A a9c4e1e334ef
lint-fix 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 28 tokens to every session and 251 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-blueprint
Gera um Task List estruturado a partir do Design aprovado. Use quando o usuário aprovar o DESIGN.md. Cria um documento BLUEPRINT.md com arquitetura, endpoints, modelo de dados e plano de execução.
skill-fastapi-api
Padrões DARE para APIs REST em Python + FastAPI + Pydantic + uvicorn. Routers, dependency injection, Pydantic v2 schemas, async SQLAlchemy 2.0, autenticação OAuth2 + JWT, rate limit com slowapi, pytest + httpx, OpenAPI auto-gerado.
skill-mcp-server
Padrões DARE para servidores MCP (Model Context Protocol) em TypeScript ou Python. Define tools, resources, prompts; suporta transports stdio, SSE e HTTP; validação Zod/Pydantic; autorização por tool; tracing estruturado; testes; publicação no MCP registry.
skill-rails-api
Padrões DARE para APIs em Ruby on Rails 8 — API mode, ActiveRecord, Solid Queue, Solid Cable, Action Cable, strong parameters, services (interactors), serializers (Blueprinter/Alba), Devise/JWT, rack-attack, rswag/grape-swagger.
dare-laravel-api
Padrões DARE para APIs REST em Laravel 11 + PHP 8.3 — Strict Types, FormRequests, Services, JsonResources, Eloquent + casts, tratamento global de exceções, testes Feature/Pest, PHPStan/Larastan, Pint.
dare-llm-integration
Integração com LLMs (Large Language Models) em projetos DARE. Fornece abstração LLMProvider, cache em memória com TTL, rate limiting via token bucket, prompt templates versionados e validação de output via JSON Schema. Cobre antipatterns crítico de prompt injection e LLM output não validado.