From contextstellar
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.
How this skill is triggered — by the user, by Claude, or both
Slash command
/contextstellar:optimizeThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Analyze the user's recent scoring patterns and provide actionable optimization recommendations.
Analyze the user's recent scoring patterns and provide actionable optimization recommendations.
curl -s "${CONTEXTSTELLAR_BASE_URL:-https://contextstellar.com}/api/v1/hooks/stats?projectId=${CONTEXTSTELLAR_PROJECT_ID}" \
-H "Authorization: ${CONTEXTSTELLAR_API_KEY}"
Grade Distribution Analysis:
RLM Learned Weights (if available):
Token Utilization (if low):
Structural Clarity (if low):
<context>, <instructions>, <output_format>)Specificity (if low):
Content Density (if low):
Cache-Friendliness (if low):
If $ARGUMENTS contains a prompt, score it and show before/after with specific edits.
End with the user's trend direction and encourage continued improvement.
npx claudepluginhub sunnypatneedi/claude-code-contextstellarScores prompts across 7 dimensions and restructures using 8 Anthropic techniques like XML tags and chain-of-thought. Auto-triggers on PreToolUse for unstructured subagent prompts; manual via /reprompt-orator.
Analyzes context window usage and session habits to provide token efficiency coaching for Claude Code/Codex. Use when building new projects, diagnosing sluggish sessions, or designing multi-agent systems.
Optimizes prompts for AI performance via chain-of-thought, few-shot examples, token reduction, RAG integration, and model-specific tuning like GPT-4 or Claude. Activates on improve/refine/engineering requests.