From Setup Eval
Run deterministic static analysis on the full agent setup (CLAUDE.md, skills, commands, hooks, agents, MCP configs). 39 rules + system-level analysis (token budget, trigger overlaps, dependencies, context utilization). No LLM. Use when the user wants a fast lint check, CI gate, or structural health report.
How this skill is triggered — by the user, by Claude, or both
Slash command
/setup-eval:setup-eval-lintThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Run 39 deterministic rules + system-level analysis on the user's agent setup. No LLM involved. Fast, reproducible, CI-suitable.
Run 39 deterministic rules + system-level analysis on the user's agent setup. No LLM involved. Fast, reproducible, CI-suitable.
/setup-eval-review.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.
Determine the setup path. If the user doesn't specify one, use the current working directory.
uv run python skills/setup-eval-lint/scripts/run_assessment.py <setup-path> recommended
If the user has a ~/.claude/ directory, pass it as the third argument for user-level config discovery:
uv run python skills/setup-eval-lint/scripts/run_assessment.py <setup-path> recommended ~/.claude
Read the JSON output.
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: setup-eval v{version} (claude-code-plugin)
Duration: [X minutes Y seconds]
Get {version} by running: uv run python -c "import importlib.metadata; print(importlib.metadata.version('setup-eval'))"
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 setup-eval-lint-report.md in the current directory). Tell them the file path when done.
npx claudepluginhub redhat-community-ai-tools/harness-eval-lab --plugin setup-evalProvides CDSS development patterns for drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), and alert classification integrated into EMR workflows.