AugmentClaude
BackendDashboards

Productivity Score

By hoangsonww· hoangsonww/Claude-Code-Agent-Monitor· 0

Calculate your agent's productivity score from session metrics and cache efficiency data.

Installation

  1. Make sure Claude is on your device and in your terminal.

    Skills load from ~/.claude/skills/ when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.

    One-time setup
    npm i -g @anthropic-ai/claude-code

    Already have it? Skip ahead.

  2. Paste into Claude Code or into your terminal.
    Install
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    This copies the whole skill folder into ~/.claude/skills/productivity-score-hoangsonww/ — the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

    Faster alternative (instruction-only skills)

    Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.

    Quick install (SKILL.md only)
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  3. Restart Claude Code.

    Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.

  4. Just ask Claude.

    Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

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.

What this skill does

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

EndpointReturns
GET /api/analyticsToken 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=100Sessions with metadata JSON: thinking_blocks, turn_count, total_turn_duration_ms, usage_extras (service_tier, speed, inference_geo)
GET /api/pricing/costTotal 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.

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