| 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.
Prompt Evaluator
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.
Mode 1: Usage Analytics
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.
Mode 2: Prompt Quality Evaluation
Trigger: "evaluate this prompt", "optimize this prompt", or user pastes a prompt for review.
Process
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:
- Ambiguities that could cause wrong output
- Missing constraints (format, length, tone)
- Redundant instructions (same thing said twice)
- Hallucination risks (no grounding, no source constraint)
