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Claude-Code-Monitor/plugins/ccam-insights/commands/insights.md
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nntrivi2001 57dc91585d feat: Claude Code Monitor — lanes, pipelines and a merged workspace
Internal SmartGift build of a Claude Code monitoring dashboard.

Lanes: a durable unit of parallel agent work, one per working directory,
tracked across session restarts. Managed lanes are git worktrees the
dashboard provisions and can reset or remove behind a three-check destroy
guard and a counted preflight; adopted lanes are directories you already
own and are never destroyable.

Pipelines: a lane moves through pipeline stages. A stage the agent declares
with evidence renders green; a stage inferred from the tool-event stream
renders dashed amber and never counts as done. Detection is forward-only
within a 30-minute window, and never writes the declared stage.

Workspace: one page at /run with a lane grid, the selected lane's pipeline,
and a full Claude console behind a disclosure.
2026-07-30 14:39:03 +07:00

1.6 KiB

description
description
Surface the top 3 data-backed insights about your Claude Code usage right now

Produce the top 3 insights about Claude Code usage right now, each backed by real numbers from the Agent Monitor dashboard.

  1. Fetch high-level state:

    • curl -s http://localhost:4820/api/stats → total_sessions, active_sessions, active_agents, total_events, events_today, agents_by_status, sessions_by_status.
    • curl -s http://localhost:4820/api/analytics → tokens (total_input/total_output/total_cache_read/total_cache_write), tool_usage (top 20), daily_events (365d), daily_sessions (365d), event_types, avg_events_per_session, total_subagents.
  2. Derive signal, citing exact field values:

    • Cache hit rate = total_cache_read / (total_cache_read + total_input).
    • Activity trend: compare the last 7 days of daily_sessions/daily_events against the prior 7.
    • Concentration: the single most-used tool and most-frequent event_type, with its share of the total.
    • Error pressure: APIError share of events; subagent fan-out via total_subagents and avg_events_per_session.
  3. Pick the 3 most decision-relevant findings (biggest cost lever, sharpest trend, or clearest anomaly). For each print:

    • A one-line headline with the supporting number.
    • Why it matters in one sentence.
    • One concrete action.

Output rules: rank by impact (most important first); currency in USD to 4 decimals; rates as percentages to 2 decimals; use ▲/▼ for trend direction; cite only fields the API returned — never fabricate. If curl cannot reach http://localhost:4820, tell the user to start the dashboard with npm start from the repo root.