mcpbeat

Delegation Audit

hoangsonww/delegation-audit

> 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.

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the whole folder, loaded on every use
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instructions only
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copies elsewhere
how many repositories repackaged it
867
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/hoangsonww/Claude-Code-Agent-Monitor --skill delegation-audit

The instruction itself

9 sections, as written by the author

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.

How to use it

Copy the folder

Take hoangsonww/delegation-audit from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.