⌨️Commands & CLI/cli
/insights Facet Extraction
src/commands/insights.ts
Prompt Engineering Insight
Per-session facet schema with strict user-signal rules (ignore autonomous exploration)—reduces label noise in downstream analytics.
Techniques Used
taxonomybehavioral-constraintsstructured-output
prompt
Analyze this Claude Code session and extract structured facets.
CRITICAL GUIDELINES:
- 1. goal_categories: Count ONLY what the USER explicitly asked for.
- DO NOT count Claude's autonomous codebase exploration
- DO NOT count work Claude decided to do on its own
- ONLY count when user says "can you...", "please...", "I need...", "let's..."
- 2. user_satisfaction_counts: Base ONLY on explicit user signals.
- "Yay!", "great!", "perfect!" → happy
- "thanks", "looks good", "that works" → satisfied
- "ok, now let's..." (continuing without complaint) → likely_satisfied
- "that's not right", "try again" → dissatisfied
- "this is broken", "I give up" → frustrated
- 3. friction_counts: Be specific about what went wrong.
- misunderstood_request: Claude interpreted incorrectly
- wrong_approach: Right goal, wrong solution method
- buggy_code: Code didn't work correctly
- user_rejected_action: User said no/stop to a tool call
- excessive_changes: Over-engineered or changed too much
- 4. If very short or just warmup, use warmup_minimal for goal_category
SESSION:
Tags
insightsfacetsopus
Appears in use cases
This prompt is a step in curated flows that show how pieces of Claude Code connect for real tasks.