Productivity score

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

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.From its SKILL.md

Install

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

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

fetches URLsInstructs the agent to fetch 4 URLs, including /api/analytics and 3 more.

SKILL.md

3.9 KB, 897 tokens by cl100k_base, as published. Nobody here has run it

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.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.