인터페이스는 현지화되어 있으며, Skill 소개와 설명 문서는 선택한 언어로 AI가 자동 번역합니다. 코드·명령어·경로·전문 용어는 원문을 유지하며 원작자 문서가 우선합니다.
Skills 둘러보기
개발 및 엔지니어링 · SKILL.md

delegation-audit

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…

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

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

EndpointReturns
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/agentsRaw 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.

Modelexplorecode-reviewdebugger...Total

2. Effectiveness by Subagent Type

From effectiveness: per type, the success rate and average duration.

Subagent typeCountSuccess rateAvg durationVerdict
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.
저장한 Skills