Claude-Code-Agent-Monitor productivity-score

install

source · Clone the upstream repo

git clone https://github.com/hoangsonww/Claude-Code-Agent-Monitor

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/hoangsonww/Claude-Code-Agent-Monitor "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/ccam-analytics/skills/productivity-score" ~/.claude/skills/hoangsonww-claude-code-agent-monitor-productivity-score && rm -rf "$T"

manifest: plugins/ccam-analytics/skills/productivity-score/SKILL.md

source content

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.