Telemetry Pr Insights

Telemetry PR Insights

Scope

Default: Branch Lifetime

1. Find merge-base: git merge-base HEAD origin/main
2. Get timestamp: git show -s --format=%cI <merge_base>
3. Query events where timestamp >= branch_start for current workspace

Fallback: Last 7 Days

If Git unavailable or merge-base fails.

Data Access (Combined Platforms)

Database: ~/.blueplane/telemetry.db

• Claude Code (claude_raw_traces):

  • Filter by cwd LIKE '/path/to/workspace%'
  • Tokens: input_tokens, cache_creation_input_tokens, cache_read_input_tokens, output_tokens
  • Model: message_model, Role: message_role, Branch: git_branch

• Cursor (cursor_sessions + cursor_raw_traces):

  • 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 composer_id extracted from item_key = "bubbleId:{composerId}:{bubbleId}"
  • Tokens: token_count_up_until_here (cumulative only; use for activity, not precise totals)
  • Message type: message_type (0=user, 1=assistant, often NULL for ~all events)
  • Model: not stored (always treated as unknown)

For full schema details, see the Data Access / Schema Reference section in telemetry-insights/SKILL.md. This skill uses the same combined Cursor + Claude access patterns, but scoped to the PR branch window.

Tool Usage & Workflow Patterns (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 (e.g., created but not in git)
• 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)

Metrics (Lean Subset)

{
  "window": {
    "analysis_scope": "branch_pr|time_period",
    "branch_name": "...",
    "branch_start": "YYYY-MM-DD",
    "days_active": N
  },
  "activity": {
    "total_prompts": N,
    "total_responses": N,
    "breakdown": {
      "claude_code": {"prompts": N, "responses": N},
      "cursor": {"prompts": N, "responses": N}
    },
    "active_days": N
  },
  "tokens": {
    "note": "Claude Code only - Cursor does not provide per-message token counts",
    "total_input": N,
    "total_output": N,
    "ratio": X.XX
  },
  "models": {
    "note": "Claude Code only - Cursor does not persist model names",
    "breakdown": {"model-name": N}
  },
  "daily": {
    "YYYY-MM-DD": {"input": N, "output": N, "prompts": N}
  },
  "tool_usage": {
    "note": "Claude Code only - extracted from event_data blob",
    "distribution": {"Bash": N, "Write": N, "Edit": N, "Read": N},
    "workflow_signals": {
      "read_before_edit_ratio": X.XX,
      "files_created_not_committed": N,
      "high_churn_files": ["path/to/file"]
    },
    "pattern_classification": {
      "productive_iteration": N,
      "trial_and_error": N
    }
  }
}

Derived Insights (3-5 max)

• Efficiency: token ratio, cache usage, assessment (Claude Code only)
• Activity Pattern: steady/bursty/spiky, platform distribution
• Model Strategy: dominant model, cost awareness (Claude Code only)
• Platform Usage: Claude Code vs Cursor breakdown, workflow insights
• Workflow Quality: Tool usage patterns, churn classification (productive vs wasteful), read-before-edit discipline

Output Format

Executive Summary (2-4 sentences)

First sentence MUST state time frame:

• "Since this branch diverged from main on YYYY-MM-DD, ..."
• "Over the last 7 days, ..."

Then: activity level, efficiency, standout patterns.

Key Metrics (compact)

Activity (combined across platforms)

• Total prompts / responses
• Breakdown by platform (Claude Code vs Cursor)
• Active days

Tokens (Claude Code only)

• Input/output tokens and ratio
• Per-day snapshot (3-5 days max)

Models (Claude Code only)

• Model distribution
• Strategic usage patterns

Tool Usage & Workflow (Claude Code only, optional)

• Tool operation distribution
• Files created but not committed (potential waste)
• Workflow patterns (productive iteration vs trial-and-error)
• Read-before-edit ratio

Recommendations (3-5 bullets)

Pragmatic, low-risk suggestions. No personal commentary.

Privacy

• Never include raw code content
• No verbatim prompts/responses
• Focus on aggregated metrics only