nntrivi2001
57dc91585d
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.
54 lines
2.5 KiB
Markdown
54 lines
2.5 KiB
Markdown
---
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description: >
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Report concurrency and parallelism for a session — how many agents ran in
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parallel, concurrency-lane utilization, peak parallel width, and
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serialization bottlenecks (sequential chains that could have run as parallel
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lanes) — using the Agent Monitor workflow intelligence API. Use when checking
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whether a multi-agent session used parallelism efficiently.
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---
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# Concurrency Report
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Report on parallel execution for one Claude Code session: lanes, peak width, utilization, and where work serialized.
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## Input
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The user provides: **$ARGUMENTS**
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A session ID. If empty, fetch `GET /api/sessions?limit=1` and report on the most recent session, stating which one.
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## Data Sources
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| Endpoint | Returns |
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|----------|---------|
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| `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) |
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## Report Sections
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### 1. Parallelism Summary
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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.
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`Lanes: N · Peak parallel: M · Agents: K · Complexity: S`
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### 2. Lane Timeline
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A per-lane list of the agents that occupied each lane in order:
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`Lane 1: explore (0–12s) → code-review (12–48s)`
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`Lane 2: debugger (5–30s)`
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Show overlapping windows so simultaneity is visible.
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### 3. Utilization
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| Lane | Busy time | Idle time | Utilization % |
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|------|-----------|-----------|---------------|
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Plus an overall utilization figure (busy lane-time / total lane-time).
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### 4. Serialization Bottlenecks
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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.
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## Output
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- Markdown tables for utilization; a fenced list for the lane timeline.
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- Durations in human units (e.g. `48s`, `2m 10s`); percentages to whole numbers.
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- Use ▲/▼ when comparing utilization against an even-distribution baseline.
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- Cite only timing returned by the API; never invent lane overlaps or durations.
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- If the session ran a single agent (no concurrency), say so plainly rather than inventing lanes.
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- If the dashboard is unreachable, tell the user to start it with `npm start` from the repo root.
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