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Evaluate how you use Claude Code — analyze prompt patterns, feature utilization, and get improvement suggestions. Trigger: /prompts:evaluate, prompt analysis, usage evaluation, how am I using Claude
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
SOC 職業分類に基づく
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/nguyenthienthanh/aura-frog --skill prompt-evaluatorコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SKILL.md を表示中
Vision feedback loop for UI — render the running UI, screenshot it across viewports + dark mode, run deterministic gates then a multimodal critique against the design system, and iterate until it passes. Use after building or changing a UI component/page, when asked to 'check the UI', make it 'look right', or verify a build matches a design spec / Stitch screen.
UI/UX design expertise — component design, design system selection, responsive layout. Includes auto-detection from package.json and Context7 integration for library docs.
Generate a cohesive color-token system from ONE OKLCH brand hue — primary/secondary/background/foreground/muted/border with semantic light + dark, emitted as a Tailwind v4 @theme block or a CSS-variables fallback. Use when starting a design system, defining brand colors, setting up theming/dark-mode, or replacing scattered hardcoded colors with a derived palette.
| name | prompt-evaluator |
| description | Evaluate how you use Claude Code — analyze prompt patterns, feature utilization, and get improvement suggestions. Trigger: /prompts:evaluate, prompt analysis, usage evaluation, how am I using Claude |
| autoInvoke | false |
| priority | 30 |
| triggers | ["/prompts:evaluate","evaluate my prompts","how am I using Claude","usage analysis","prompt patterns","improve my workflow","evaluate this prompt","optimize this prompt"] |
| context | fork |
| allowed-tools | Read, Bash, Glob, Grep |
| user-invocable | false |
AI-consumed reference. Optimized for Claude to read during execution. Human-readable explanation: see docs/architecture/HIERARCHICAL_PLANNING.md or docs/getting-started/ depending on topic.
Two modes: Usage Analytics (how you use Claude Code) and Prompt Quality (evaluate/optimize a specific prompt).
For task-intake validation (before executing a user request), use the 6-dimension benchmark in rules/core/prompt-validation.md — that's a different check (task completeness, not prompt craftsmanship) and should run at /run pre-execution per rules/core/no-assumption.md.
Trigger: /prompts:evaluate [--days N]
node "${CLAUDE_PLUGIN_ROOT}/scripts/metrics/evaluate-prompts.cjs" --days 7
Analyzes prompt logs from .claude/metrics/prompts/{date}.jsonl. Reports: intent distribution, feature utilization, complexity profile, suggestions, usage score (0-100).
If no data: prompt-logger hook collects automatically. Return after a few sessions.
Trigger: "evaluate this prompt", "optimize this prompt", or user pastes a prompt for review.
Step 1 — Classify. Infer intent, task type (coding/creative/RAG/agent/reasoning), constraints (format, tone, tools), target model.
Step 2 — Score. Rate 0-10 on 5 dimensions:
dimensions[5]{dimension,what_to_check}:
Clarity,"Is intent unambiguous? Can the model misinterpret?"
Instruction Quality,"Are steps specific? Is the task decomposed well?"
Efficiency,"Token waste? Redundant phrasing? Could say the same in fewer words?"
Robustness,"Edge cases handled? Hallucination controls? Fallback behavior?"
Output Alignment,"Format specified? Easy to parse? Deterministic output?"
Calibration: 0-3 poor, 4-6 acceptable, 7-8 good, 9-10 excellent.
Step 3 — Detect Issues. List specific problems:
Step 4 — Optimize. Rewrite in two versions:
Goals: preserve intent, reduce tokens, improve structure, add missing constraints, ensure parseable output.
{
"task_type": "...",
"score": 0-10,
"breakdown": {
"clarity": 0-10,
"instruction": 0-10,
"efficiency": 0-10,
"robustness": 0-10,
"output_alignment": 0-10
},
"issues": ["specific issue 1", "specific issue 2"],
"suggestions": ["actionable suggestion 1"],
"optimized_prompt": {
"minimal": "...",
"production": "..."
}
}
principles[6]{principle}:
Principle > checklist — 3 clear rules beat 20 vague ones
Show don't tell — one example > paragraph of explanation
Structured output > prose — JSON/TOON/tables when parseable output needed
Remove what the model already knows — don't teach coding basics to Claude
Constraint what varies — only specify behavior the model wouldn't do by default
Token budget awareness — every word costs money at scale
Trigger: user suspects a prompt is unstable, or before shipping a prompt to production.
Run the prompt N = 3 times (separate contexts, identical input). Compare outputs.
variance_level = percentage_of_non_matching_content_across_runs
- <10% STABLE — ship as-is
- 10-30% LOW VARIANCE — minor differences, usually acceptable
- 30-60% UNSTABLE — prompt needs constraints to reduce ambiguity
- >60% CHAOS — rewrite the prompt before any use
| Dimension | What "same" means |
|---|---|
| Structure | Same sections, same formatting, same order |
| Key facts | Same numbers, names, file paths |
| Decision | Same conclusion/recommendation |
| Format | Same JSON keys, same table headers |
Prompt: "Review this PR and list issues"
Runs: 3
Variance: 42% (UNSTABLE)
Divergence:
- Run 1 listed 5 issues (security, perf, style)
- Run 2 listed 3 issues (missed perf and style findings)
- Run 3 listed 7 issues (added subjective style nitpicks)
Root cause: No constraint on issue categories or severity threshold.
Recommendation: Change prompt to: "List issues at severity >= warning. Categories: security, correctness, performance. Skip style/formatting."
Cost: 3× single-run. Worth it for prompts that will run 50+ times.
antipatterns[6]{pattern,fix}:
"You are an expert...",Remove — model capability is fixed by model choice
"Please note that...",Remove — filler phrase
"It is important to always...",Rewrite as direct instruction
Repeating the same rule in 3 sections,Deduplicate — state once
Explaining what JSON is,Remove — model knows JSON
Step-by-step for trivial tasks,Remove steps — just state the goal