القائمة
Score a prompt for context engineering quality. Use when the user asks to score, analyze, or optimize a prompt, or when reviewing prompt quality before sending to an LLM.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Analyze recent prompt patterns and suggest optimizations. Use when the user wants to improve their prompting patterns, reduce token costs, or see what the RLM flywheel has learned about their project.
Set up ContextStellar cloud mode for RLM learning and dashboards. Use when the user wants to configure an API key, connect their project, or upgrade from local-only scoring.
Show ContextStellar scoring statistics, RLM learning progress, and grade distribution for the current project. Use when the user asks about their scoring stats, prompt quality trends, or RLM weight learning.
| name | score |
| description | Score a prompt for context engineering quality. Use when the user asks to score, analyze, or optimize a prompt, or when reviewing prompt quality before sending to an LLM. |
Score the provided prompt text for context engineering quality using the ContextStellar scoring API.
Take the prompt text from $ARGUMENTS. If no arguments provided, ask the user to paste or describe the prompt they want scored.
Call the ContextStellar hooks endpoint to score the prompt:
curl -s -X POST "${CONTEXTSTELLAR_BASE_URL:-https://contextstellar.com}/api/v1/hooks/claude-code" \
-H "Authorization: ${CONTEXTSTELLAR_API_KEY}" \
-H "Content-Type: application/json" \
-d "{
\"sessionId\": \"manual-score-$(date +%s)\",
\"type\": \"PreToolUse\",
\"toolName\": \"ManualScore\",
\"toolInput\": { \"content\": $(echo "$PROMPT_TEXT" | jq -Rs .) }
}"
Present the results clearly:
If the score is below B (< 70), proactively suggest specific improvements the user can make.
| Dimension | What It Measures |
|---|---|
| Token Utilization (25%) | Waste: politeness, filler, intensifiers, redundancy |
| Structural Clarity (23%) | XML tags, lists, headers, code blocks, imperative keywords |
| Specificity (20%) | Numbers, format keywords, role assignment, examples, constraints |
| Content Density (17%) | Lexical diversity, n-gram repetition |
| Cache-Friendliness (15%) | Timestamps, UUIDs, date refs, volatile data positioning |