Borrowing it
Nothing to install: this file belongs to mehdic/bazinga. 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/mehdic/bazinga/main/.claude/skills/lint-check/SKILL.mdgit clone --depth 1 https://github.com/mehdic/bazingaWrote 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/mehdic/bazinga/lint-check)<a href="https://agentmods.dev/skills/mehdic/bazinga/lint-check"><img src="https://agentmods.dev/badge/skills/mehdic/bazinga/lint-check/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/mehdic/bazinga/lint-check"><img src="https://agentmods.dev/badge/skills/mehdic/bazinga/lint-check.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 | $0.00062 | $0.01056 |
| Opus 5.5 | $0.00025 | $0.00422 |
| Sonnet 5.5 | $0.00012 | $0.00211 |
| Haiku 4.5 | $0.00006 | $0.00106 |
Grade A, and why
lint-check 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 2mo 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Linting Skill
You are the lint-check skill. When invoked, you run appropriate linters based on project language and provide structured quality reports.
When to Invoke This Skill
Invoke this skill when:
- Tech Lead is reviewing code changes
- Before approving pull requests
- Code quality issues suspected
- Before merge to main branch
- Style compliance check needed
Do NOT invoke when:
- Generated code (migrations, protobuf, auto-generated files)
- Third-party code (vendor/, node_modules/)
- Work-in-progress drafts not ready for review
- Emergency hotfixes (skip linting to save time)
Your Task
When invoked:
- Execute the lint checking script
- Read the generated lint report
- Return a summary to the calling agent
Step 1: Execute Lint Check Script
Use the Bash tool to run the pre-built linting script.
On Unix/macOS:
bash .claude/skills/lint-check/scripts/lint.sh
On Windows (PowerShell):
pwsh .claude/skills/lint-check/scripts/lint.ps1
Cross-platform detection: Check if running on Windows (
$env:OScontains "Windows" orunamedoesn't exist) and run the appropriate script.
This script will:
- Detect project language (Python, JavaScript, Go, Ruby, Java)
- Run appropriate linter (ruff/pylint, eslint, golangci-lint, rubocop, checkstyle/pmd)
- Parse results and categorize by severity
- Generate
bazinga/artifacts/{SESSION_ID}/skills/lint_results.json
Step 2: Read Generated Report
Use the Read tool to read:
bazinga/artifacts/{SESSION_ID}/skills/lint_results.json
Extract key information:
tool- Linter usederror_count- Must-fix issueswarning_count- Should-fix issuesinfo_count- Optional improvementsissues- Array of findings with file/line/rule/message
Step 3: Return Summary
Return a concise summary to the calling agent:
Lint Check Report:
- Language: {language}
- Tool: {tool_name}
- Errors: {count} (must fix)
- Warnings: {count} (should fix)
- Info: {count} (optional)
Top issues:
1. {file}:{line} - {message}
2. {file}:{line} - {message}
3. {file}:{line} - {message}
Details saved to: bazinga/artifacts/{SESSION_ID}/skills/lint_results.json
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 167 lines · 62 tokens per session scan A 211ed956808d
lint-check is a skill published in the GitHub repository mehdic/bazinga (22 stars, last pushed 9mo ago), licensed MIT. It adds 62 tokens to every session and 1,056 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.
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