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.
2026-07-29 17:07:45 +07:00
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+---
+description: >
+  Audit model delegation and subagent effectiveness for a session — which
+  models handled which subagent types, per-type success rates and average
+  durations, and wasted delegations (heavy models on trivial work or types
+  that consistently fail) — using the Agent Monitor workflow intelligence API.
+  Use when reviewing how a session delegated work across models and subagents.
+---
+
+# Delegation Audit
+
+Audit how a Claude Code session delegated work: model-to-subagent mapping and whether each delegation paid off.
+
+## Input
+
+The user provides: **$ARGUMENTS**
+
+A session ID. If empty, fetch `GET /api/sessions?limit=1` and audit the most recent session, stating which one.
+
+## Data Sources
+
+| Endpoint | Returns |
+|----------|---------|
+| `GET /api/workflows/{sessionId}` | The `modelDelegation` dataset (which models are delegated which subagent types) and the `effectiveness` dataset (per-type completion/success rate, avg duration, task success) |
+| `GET /api/agents` | Raw subagent records (`type`, `model`, `status`, `depth`, `parent`) to corroborate counts and statuses |
+
+## Report Sections
+
+### 1. Delegation Matrix
+From `modelDelegation`: a model × subagent-type table of how many agents of each type each model ran.
+| Model | explore | code-review | debugger | ... | Total |
+|-------|---------|-------------|----------|-----|-------|
+
+### 2. Effectiveness by Subagent Type
+From `effectiveness`: per type, the success rate and average duration.
+| Subagent type | Count | Success rate | Avg duration | Verdict |
+|---------------|-------|--------------|--------------|---------|
+Mark types below ~70% success as low-yield.
+
+### 3. Wasted Delegations
+Flag, with evidence:
+- A heavy model (e.g. Opus) assigned to a simple/low-stakes subagent type that a cheaper model handled successfully elsewhere — candidate for rebalancing.
+- Subagent types with low success rates (effort spent, task not completed).
+- Duplicate delegations: the same type spawned repeatedly with poor success (retry churn).
+
+### 4. Rebalancing Suggestions
+Concrete model reassignments grounded in the matrix and effectiveness data. State the type, the model used, the success rate, and the suggested model — only where the data supports it.
+
+## Output
+
+- Markdown tables for the matrix and effectiveness.
+- Success rates as percentages; durations in human units (e.g. `1m 12s`).
+- Use ▲/▼ when comparing a type's success rate against the session-wide average.
+- Cite only numbers returned by the API; do not infer success rates that the `effectiveness` dataset does not provide.
+- If the dashboard is unreachable, tell the user to start it with `npm start` from the repo root.