Files
Claude-Code-Monitor/plugins/ccam-workflows/skills/concurrency-report/SKILL.md
T
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

2.5 KiB
Raw Blame History

description
description
Report concurrency and parallelism for a session — how many agents ran in parallel, concurrency-lane utilization, peak parallel width, and serialization bottlenecks (sequential chains that could have run as parallel lanes) — using the Agent Monitor workflow intelligence API. Use when checking whether a multi-agent session used parallelism efficiently.

Concurrency Report

Report on parallel execution for one Claude Code session: lanes, peak width, utilization, and where work serialized.

Input

The user provides: $ARGUMENTS

A session ID. If empty, fetch GET /api/sessions?limit=1 and report on the most recent session, stating which one.

Data Sources

Endpoint Returns
GET /api/workflows/{sessionId} The concurrency dataset (overlapping agent execution lanes with start/end timing) and the complexity dataset (numeric score from depth, breadth, and tool diversity)

Report Sections

1. Parallelism Summary

From concurrency: number of distinct lanes, peak parallel width (max agents running simultaneously), and total agents. Pair with the complexity score to judge whether the parallelism matched the work's size. Lanes: N · Peak parallel: M · Agents: K · Complexity: S

2. Lane Timeline

A per-lane list of the agents that occupied each lane in order: Lane 1: explore (012s) → code-review (1248s) Lane 2: debugger (530s) Show overlapping windows so simultaneity is visible.

3. Utilization

Lane Busy time Idle time Utilization %
Plus an overall utilization figure (busy lane-time / total lane-time).

4. Serialization Bottlenecks

Identify sequential chains where one agent waited on the previous despite no apparent dependency — candidates to run as parallel lanes. State the chain and the wall-clock time it cost. Only flag chains the concurrency timing data actually shows as sequential.

Output

  • Markdown tables for utilization; a fenced list for the lane timeline.
  • Durations in human units (e.g. 48s, 2m 10s); percentages to whole numbers.
  • Use ▲/▼ when comparing utilization against an even-distribution baseline.
  • Cite only timing returned by the API; never invent lane overlaps or durations.
  • If the session ran a single agent (no concurrency), say so plainly rather than inventing lanes.
  • If the dashboard is unreachable, tell the user to start it with npm start from the repo root.