Imported from redhat-community-ai-tools/harness-eval (
skills/lint/SKILL.md). Install upstream withnpx skills add redhat-community-ai-tools/harness-eval --skill lint. Copyright stays with the author.
Lint Setup
Run 108 deterministic rules + system-level analysis on the user's agent setup. No LLM involved. Fast, reproducible, CI-suitable.
Hard Rules
- This skill does NOT read files qualitatively. It does NOT apply rubrics. It does NOT run cross-type checks. For that, use
/review. - Present the data, don't judge. Report findings as-is. Don't add qualitative commentary.
- If everything passes, say so clearly. Don't manufacture problems.
Step 1: Ask Output Preference
Before doing anything else, ask the user:
Where should i present the results?
- Terminal - print the report here in the conversation
- File - write a markdown report to a file (you'll choose the path)
Wait for their answer before proceeding.
Step 2: Run Static Analysis
Determine the setup path. If the user doesn't specify one, use the current working directory.
uvx --from harness-eval harness-eval harness-lint <setup-path> --format jsonIf uvx is not available, fall back to pip install harness-eval and use harness-eval directly.
Read the JSON output.
Step 3: Present the Report
Read report-format.md and format the results following that structure.
Include all sections: inventory, token budget, context utilization, trigger analysis, dependencies, findings, and inspection summary.
At the very end of the report, include the exact timing:
Evaluated with: harness-eval v{version} (claude-code-plugin)
Duration: [X minutes Y seconds]
Get {version} by running: uvx --from harness-eval harness-eval --version
Record the timestamp of your first tool call in Step 2 and compute the exact difference when you finish.
If the user chose terminal: print the report in the conversation.
If the user chose file: write the report as markdown to the path they specified (or suggest lint-report.md in the current directory). Tell them the file path when done.