Analyze the user's recent scoring patterns and provide actionable optimization recommendations.
What to do
- Fetch the full stats to understand current scoring patterns:
curl -s "${CONTEXTSTELLAR_BASE_URL:-https://contextstellar.com}/api/v1/hooks/stats?projectId=${CONTEXTSTELLAR_PROJECT_ID}" \
-H "Authorization: ${CONTEXTSTELLAR_API_KEY}"
- Analyze the response and build a report:
Grade Distribution Analysis:
- If most grades are C/D/F: focus on structural clarity and specificity
- If most grades are B: focus on cache-friendliness and token utilization
- If most grades are A/A+: congratulate and show what's working
RLM Learned Weights (if available):
- Compare learned weights to defaults
- Highlight which dimensions the RLM has up-weighted (these matter most for this project)
- Highlight which dimensions were down-weighted (these matter less)
- Provide specific recommendations based on the weakest dimensions:
Token Utilization (if low):
- Remove politeness phrases ("please", "could you", "I'd like you to")
- Cut filler words ("basically", "actually", "just", "really")
- Eliminate redundancy (saying the same thing twice)
Structural Clarity (if low):
- Add XML tags to delimit sections (
<context>,<instructions>,<output_format>) - Use numbered lists for multi-step instructions
- Add headers for long prompts
Specificity (if low):
- Add concrete numbers ("return top 5" not "return some")
- Specify output format explicitly ("respond in JSON with fields: ...")
- Include examples of expected output
Content Density (if low):
- Reduce repeated phrases
- Increase vocabulary diversity
- Remove n-gram repetition
Cache-Friendliness (if low):
- Move static content (system prompt, examples) before dynamic content (user message)
- Remove timestamps, dates, and UUIDs from the beginning of prompts
- Group volatile data at the end
-
If
$ARGUMENTScontains a prompt, score it and show before/after with specific edits. -
End with the user's trend direction and encourage continued improvement.