Productivity score

Skill hoangsonww/Claude-Code-Agent-Monitor/plugins/ccam-analytics/skills/productivity-score

🚀 A real-time monitoring dashboard for Claude Code, built with SQLite3, Node.js, Express, React, Vite, TailwindCSS, and WebSockets. It tracks sessions, agent activity, tool usage, and subagent orchestration, providing live analytics, a Kanban status board, status notifications, a cute buddy, and an interactive web UI/MacOS/Windows native app.

Install

npx -y skills add hoangsonww/Claude-Code-Agent-Monitor --skill productivity-score

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What its author says it does

Copied from the file, not written here

Calculate a productivity score using actual Agent Monitor metrics — session completion rates, cache efficiency (cache_read vs input), compaction pressure (baseline tokens), turn velocity (turn_count / total_turn_duration_ms), tool success ratio (PreToolUse vs PostToolUse), and the workflow intelligence API's complexity and effectiveness scores.

SKILL.md

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Productivity Score

Calculate a productivity scorecard from the Agent Monitor's real data.

Input

The user provides: $ARGUMENTS

Options: "today", "this week", "last 30 days", a session ID, or "compare" for period comparison.

Data Sources

Endpoint	Returns
`GET /api/analytics`	Token totals (`total_input`, `total_output`, `total_cache_read`, `total_cache_write` — baselines pre-summed), tool_usage top 20, daily_events/sessions, event_types, sessions_by_status, agents_by_status, avg_events_per_session, total_subagents
`GET /api/sessions?limit=100`	Sessions with metadata JSON: `thinking_blocks`, `turn_count`, `total_turn_duration_ms`, `usage_extras` (service_tier, speed, inference_geo)
`GET /api/pricing/cost`	Total cost with per-model breakdown
`GET /api/workflows/{sessionId}`	11 workflow datasets: stats, orchestration, toolFlow, effectiveness, patterns, modelDelegation, errorPropagation, concurrency, complexity, compaction, cooccurrence

Score Components (each 0–100)

1. Completion Rate (20% weight)

From sessions_by_status:

completed / (completed + error + abandoned) × 100
Bonus for high completed-to-active ratio
Penalty for abandoned sessions (wasted work)

2. Token Efficiency (20% weight)

From analytics tokens (baselines are pre-summed into totals):

Cache hit rate: total_cache_read / (total_cache_read + total_input) × 100
- Above 60% = excellent, below 30% = poor
Output concentration: total_output / total_input — 0.3–0.8 is balanced

3. Tool Effectiveness (20% weight)

From event_types:

Success ratio: Count PostToolUse / Count PreToolUse — should be ~1.0; gap = tool failures
API error rate: Count APIError / total events — should be near 0
From workflow effectiveness data: subagent completion rates, task success per type

4. Velocity (20% weight)

From session metadata:

Turns per session: average turn_count across sessions
Turn speed: average total_turn_duration_ms / turn_count — lower = faster
Events per session: from avg_events_per_session in analytics overview
Thinking depth: average thinking_blocks — more thinking = more thorough (neutral metric)

5. Cost Efficiency (20% weight)

From pricing:

Cost per completed session: total_cost / completed_sessions
Cost trend: comparing current period to previous (decreasing = improving)
Model optimization: sessions using expensive models (Opus) for tasks subagents handle with Haiku/Sonnet

Overall Score

Weighted sum → letter grade:

A+ (95-100), A (90-94), B+ (85-89), B (80-84), C+ (75-79), C (70-74), D (60-69), F (<60)

Output Format

═══════════════════════════════════════
  PRODUCTIVITY SCORE: 87/100 (B+)
═══════════════════════════════════════
  Completion Rate   ████████░░  80/100
  Token Efficiency  █████████░  92/100
  Tool Effectiveness████████░░  85/100
  Velocity          █████████░  88/100
  Cost Efficiency   █████████░  90/100
═══════════════════════════════════════

Then: top 3 strengths, top 3 improvement areas with actionable steps, and period comparison if available.