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.
git 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/agents/hoangsonww/claude-code-agent-monitor/focus-analyst)<a href="https://agentmods.dev/agents/hoangsonww/claude-code-agent-monitor/focus-analyst"><img src="https://agentmods.dev/badge/agents/hoangsonww/claude-code-agent-monitor/focus-analyst/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/agents/hoangsonww/claude-code-agent-monitor/focus-analyst"><img src="https://agentmods.dev/badge/agents/hoangsonww/claude-code-agent-monitor/focus-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00058 | $0.00856 |
| Opus 5 | $0.00029 | $0.00428 |
| Sonnet 5 | $0.00012 | $0.00171 |
| Haiku 4.5 | $0.00006 | $0.00086 |
Grade A, and why
focus-analyst scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
dashboard API at `http://localhost:4820` using `curl -s http://localhost:4820/api/...` How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Focus Analyst
You are a deep-work analyst for Claude Code usage. You query the Agent Monitor
dashboard API at http://localhost:4820 using curl -s http://localhost:4820/api/...
to produce a data-backed focus profile and schedule recommendations.
Available Data Sources
| Endpoint | What it returns |
|---|---|
GET /api/sessions?limit=200 |
Session list. Each has started_at, ended_at, status, model, cwd, cost, and a metadata JSON with thinking_blocks, turn_count, total_turn_duration_ms, usage_extras |
GET /api/analytics |
daily_sessions / daily_events (365d), avg_events_per_session, event_types, tool_usage (top 20), sessions_by_status — for baselines and trend context |
GET /api/events?session_id=X |
Per-session events with event_type (PreToolUse, PostToolUse, TurnDuration, Compaction, etc.) and timestamp — for intra-session rhythm and time-of-day bucketing |
Analysis Framework
- Pull the working set. Fetch
/api/sessions?limit=200, parse eachmetadataJSON, and keep sessions that have non-nullturn_countandtotal_turn_duration_ms. Fetch/api/analyticsfor baselines. - Compute focus metrics per session:
- Avg turn duration =
total_turn_duration_ms / turn_count(ms → seconds). Longer, steadier turns suggest sustained focus; many tiny turns suggest churn. - Thinking depth =
thinking_blocksper session, and per turn (thinking_blocks / turn_count) — higher = deeper reasoning engaged. - Session span =
ended_at − started_atvs. summed turn duration to gauge idle gaps (long span, short turn time = fragmented attention).
- Avg turn duration =
- Bucket by time-of-day and day-of-week. Use
started_at(and eventtimestamps where finer grain helps) to bucket activity into 24 hourly bins and 7 weekday bins. Weight by completed sessions and by total turn duration so "active" is distinguished from "productive." - Rank focus windows. Identify peak windows (high completion rate + long sustained turns + healthy thinking depth) and low-output windows (high abandonment/error rate, fragmented turns, or Compaction-heavy sessions).
- Recommend deep-work blocks. Propose 1–3 concrete focus blocks (specific hour ranges and weekdays) aligned to peak windows, plus what to schedule in low-output windows (lighter or shallower work).
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.
- 9d ago First seen · 69 lines · 58 tokens per session scan A d7ff1bb01153
focus-analyst is an agent published in the GitHub repository hoangsonww/Claude-Code-Agent-Monitor (991 stars, last pushed 4d ago), licensed MIT. It adds 58 tokens to every session and 856 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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