Telemetry Insights

Scope Detection

Data Access

Database: ~/.blueplane/telemetry.db

Schema Reference

Claude Code (claude_raw_traces table):

• Workspace filter: WHERE cwd LIKE '/path/to/workspace%' (NOT workspace_hash)
• Token fields: input_tokens, cache_creation_input_tokens, cache_read_input_tokens, output_tokens
• Model field: message_model (e.g., "claude-sonnet-4-5-20250929")
• Role field: message_role ('user' | 'assistant')
• Branch field: git_branch

Cursor (cursor_sessions + cursor_raw_traces tables):

• Step 1: Get workspace_hash from cursor_sessions WHERE workspace_path LIKE '/path/to/workspace%'
• Step 2: Get composer_ids from cursor_raw_traces WHERE event_type='composer' AND workspace_hash=?
• Step 3: Filter bubbles by extracting composer_id from item_key: "bubbleId:{composerId}:{bubbleId}"
  • Bubbles have empty workspace_hash in global storage
  • Must join via composer_id from Step 2
• Token field: token_count_up_until_here (cumulative context, NOT per-message tokens)
• Message type: message_type (0=user, 1=assistant, but NULL for ~all events - use heuristics)
• Model: ❌ Not stored in database

Tool Usage Analysis (Claude Code only)

Data source: Decompress event_data blob from claude_raw_traces

import zlib, json
decompressed = zlib.decompress(row['event_data'])
event = json.loads(decompressed)
tools = event['payload']['entry_data']['message']['content']
# Filter: item['type'] == 'tool_use'
# Extract: item['name'], item['input']['file_path'], item['input']['command']

Tool operation churn (detect both productive and unproductive patterns):

Negative patterns (wasted effort):

• Files written then deleted without commit
• Same file edited multiple times in short succession (trial-and-error)
• Bash commands repeated with similar patterns (debugging loops)
• Write operations for files later removed (unwanted artifacts)

Positive patterns (intentional iteration):

• Progressive refinement: Write → Read → Edit (deliberate improvement)
• Test-driven flow: Write test → Run → Edit code → Run (TDD cycle)
• Exploration: Multiple Read/Grep before Write (research-based development)
• Read-before-edit ratio > 0.8 (careful, informed changes)

Workflow efficiency signals:

• Tool distribution (Bash, Write, Edit, Read, Task usage)
• Read-before-edit ratio (higher = more careful)
• File touch count (edits per unique file path)
• Time between tool uses (rapid = reactive, spaced = deliberate)

Cross-reference with git to classify churn:

• Compare Write/Edit file paths against final committed files
• Flag operations on files not in git diff --name-only
• Distinguish exploration (positive) from mistakes (negative)

Raw Metrics

A. Flow & Structure

• analysis_scope, session_duration, active_exchanges, total_ai_responses
• context_switch_count, prompt_timing_buckets, session_summaries

B. Prompting Patterns

• prompt_count, average_prompt_length, median_prompt_length
• prompt_complexity_score (low/medium/high), reprompt_loop_count

C. Token Economics

• total_input_tokens, total_output_tokens, input_output_ratio
• estimated_cost_usd: (input/1M × $3) + (output/1M × $15)

D. Patch Behavior

• patch_count, total_lines_added, total_lines_removed, add_remove_ratio

E. Behavioral Signals

• delegation_style: high-level vs step-by-step indicators
• positive_feedback / negative_feedback counts

F. Capability Usage

• capabilities_invoked, agentic_mode_usage

G. Model Strategy (Claude Code only)

• per_model_usage, per_model_tokens, dominant_model

H. Temporal Productivity

• tokens_per_day, tokens_per_hour_utc, prompts_per_minute_by_session

I. Tool Usage & Workflow Patterns (Claude Code only)

• tool_distribution: counts by tool name (Bash, Write, Edit, Read, etc.)
• read_before_edit_ratio: Read operations / Edit operations
• files_created_not_committed: count of Write file_paths not in git
• file_touch_count: edits per unique file_path
• high_churn_files: files edited 3+ times
• pattern_classification: productive_iteration vs trial_and_error counts

Derived Insights

1. Effort_vs_Progress_Score (0-1): lines_added / total_tokens
2. Context_Sufficiency_Index (0-1): 1 - (corrections / prompts)
3. AI_Utilization_Quality_Score (0-1): weighted agentic + efficiency + success
4. Predicted_Task_Difficulty: easy/moderate/hard based on query rate, switches
5. AI_vs_Human_Burden_Ratio: ai_output_chars / user_input_chars
6. Persistence_vs_Abandonment: reprompt loops vs topic abandonment
7. Patch_Efficiency_Curve: clean (ratio>3) vs thrashy, lines_per_prompt
8. Intent_Shift_Map: task type transitions count
9. Prompt_Quality_vs_Result: success rate by prompt length
10. Confidence_Trajectory: improving/declining/mixed
11. Stuckness_Prediction: risk_level based on correction rate, loops
12. Prompt_Pacing_Profile: rapid_iterator/balanced/deliberate
13. Model_Strategy_Assessment: single_model/tiered_models, cost_awareness
14. Peak_Performance_Windows: top hours UTC
15. Session_Focus_Profile: short_bursts/long_deep_work/mixed
16. Workflow_Quality_Score (0-1): based on read-before-edit ratio, productive vs wasteful churn
17. Development_Discipline: careful (high read-first) vs reactive (low read-first, high trial-and-error)

Output Format

1. RAW_METRICS: { JSON with metrics A-I above }

2. DERIVED_INSIGHTS: { JSON with insights 1-17 above }

3. SESSION_SUMMARY: 6-10 sentences covering:
   - Analysis scope (first sentence)
   - Duration and activity level
   - Workflow patterns
   - Token efficiency
   - Key recommendations

Interpretation Guidelines

Recommendation Triggers

Limitations

• Model info unavailable for Cursor (platform limitation)
• Token counts depend on capture completeness
• Task type inference is keyword-based (heuristic)
• Cost estimates based on Claude 3.5 Sonnet pricing