| name | delegation-audit |
| 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.
