Productivity Score

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 1f059c400d07 · 16 Oct 2026Report an issue or request a re-scan

• Publisherhoangsonww GitHub userOwner of the repository this skill is read from
• Source folderhoangsonww/Claude-Code-Agent-Monitor/plugins/ccam-analytics/skills/productivity-scoreThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict1f059c400d07A new audit runs when the source changes
• Repository statusNot archivedLast push 4 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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 /…

---
name: productivity-score
description: >
  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.
---

# 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.

Excerpt, first 3,623 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/hoangsonww/Claude-Code-Agent-Monitor
git -C Claude-Code-Agent-Monitor checkout 1f059c400d07

Works in

1. 2026-10-16
   ✓SAFE B 89 / 100

   first audit · source 1f059c400d07

Questions about Productivity Score

• github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plug — the audited source at commit 1f059c400d07 and every finding aboveObserved · 16 Oct 2026

Spotted something wrong? Report a correction · Methodology