Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill productivity-scoregit clone --depth 1 https://github.com/hoangsonww/Claude-Code-Agent-MonitorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hoangsonww/claude-code-agent-monitor/productivity-score)<a href="https://agentmods.dev/skills/hoangsonww/claude-code-agent-monitor/productivity-score"><img src="https://agentmods.dev/badge/skills/hoangsonww/claude-code-agent-monitor/productivity-score/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hoangsonww/claude-code-agent-monitor/productivity-score"><img src="https://agentmods.dev/badge/skills/hoangsonww/claude-code-agent-monitor/productivity-score.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.00949 |
| Opus 5 | $0.00034 | $0.00475 |
| Sonnet 5 | $0.00013 | $0.00190 |
| Haiku 4.5 | $0.00007 | $0.00095 |
Grade A, and why
productivity-score scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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/ CountPreToolUse— should be ~1.0; gap = tool failures - API error rate: Count
APIError/ total events — should be near 0 - From workflow
effectivenessdata: subagent completion rates, task success per type
4. Velocity (20% weight)
From session metadata:
- Turns per session: average
turn_countacross sessions - Turn speed: average
total_turn_duration_ms / turn_count— lower = faster - Events per session: from
avg_events_per_sessionin analytics overview - Thinking depth: average
thinking_blocks— more thinking = more thorough (neutral metric)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 83 lines · 67 tokens per session scan A 45beba641138
productivity-score is a skill published in the GitHub repository hoangsonww/Claude-Code-Agent-Monitor (991 stars, last pushed 4d ago), licensed MIT. It adds 67 tokens to every session and 949 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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