Score the provided prompt text for context engineering quality using the ContextStellar scoring API.
What to do
-
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:
- Overall Score and Grade (A+ through F)
- Dimension Breakdown — show each of the 5 dimensions with their individual scores
- Tips — list the actionable optimization suggestions
- Token Count and potential Token Savings
-
If the score is below B (< 70), proactively suggest specific improvements the user can make.
Scoring Dimensions
| 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 |
Grade Scale
- A+ (90+): Excellent — production-ready prompt
- A (80-89): Great — minor optimizations possible
- B (70-79): Good — some waste patterns detected
- C (60-69): Fair — significant optimization opportunity
- D (50-59): Poor — major restructuring recommended
- F (<50): Failing — fundamental issues