菜单
Run deterministic static analysis on the full agent setup (CLAUDE.md, skills, commands, hooks, agents, MCP configs). 64 rules + system-level analysis (token budget, trigger overlaps, dependencies). No LLM. Use when the user wants a fast lint check, CI gate, or structural health report.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Deep-evaluate a single skill with static analysis and qualitative issue detection, both individually and in context of the full setup. Use when the user wants to check if a specific skill is worth keeping, well-built, or redundant.
Full qualitative review of the agent setup. Reads every file, applies per-component rubrics, runs 21 cross-type optimization checks, and produces KEEP/REVIEW/REMOVE verdicts. Use when the user wants a deep review, redundancy check, or quality assessment of their setup.
Deep security audit of the agent setup. Runs all deterministic security rules (prompt injection, credential access, data exfiltration, obfuscation, reverse shells, AST behavioral analysis, taint tracking, MCP permission analysis, tool poisoning, YARA signatures, CVE lookups) plus LLM-based semantic security review. Use when the user asks about security, safety, wants to audit their setup, or needs a pre-deployment security check.
A skill that contains injection patterns for testing
Review code for issues
Run lint checks on the codebase
| name | lint |
| description | Run deterministic static analysis on the full agent setup (CLAUDE.md, skills, commands, hooks, agents, MCP configs). 64 rules + system-level analysis (token budget, trigger overlaps, dependencies). No LLM. Use when the user wants a fast lint check, CI gate, or structural health report. |
| allowed-tools | ["Bash","Read"] |
Run 39 deterministic rules + system-level analysis on the user's agent setup. No LLM involved. Fast, reproducible, CI-suitable.
/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/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/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: harness-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('harness-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 lint-report.md in the current directory). Tell them the file path when done.