understudy

Orchestrate local-first agent improvement workflows using traces, evals, and route decisions.

10|5|Updated Jun 3, 2026

Search Tags:#agent #orchestration #governance #traces #local-first #eval #understudy

Ready to run

npx skills add https://github.com/understudylabs/understudy-agent-tools --skill understudy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: understudy
Source: https://github.com/understudylabs/understudy-agent-tools/tree/main/skills/understudy
Command: npx skills add https://github.com/understudylabs/understudy-agent-tools --skill understudy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Understudy is agent improvement infrastructure: it helps a coding agent improve its developer's LLM system from real traces. This skill is the orchestrator — it gives your agent the loop and routes each stage to exactly one worker skill. It does not do the work inline.

Core Features & Use Cases

Orchestrates trace → evaluate → optimize (GEPA, automatic prompt evolution) → compare → deploy via worker skills.
Local-first workflow with no required registration, auth, or hosted gateway; artifacts remain on the developer's machine.
Produces auditable artifacts and safety gates to govern when data is uploaded or deployed, plus a structured knowledge base for improvements.
Routes each workload step to a single, appropriate worker skill and generates an Understudy Agent Improvement Report when complete.

Quick Start

Run the understudy orchestrator on your local codebase to begin capturing traces and evaluating improvements.

Frequently Asked Questions about understudy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I improve a coding agent using local LLM traces?▼

You can improve a coding agent using local traces by running an orchestration loop that captures real traces, builds eval harnesses, compares incumbent vs candidate routes, and deploys improvements privately on your machine.

What is local-first agent orchestration and how does it work?▼

Local-first agent orchestration coordinates agent improvement workflows by routing each stage—trace capture, evaluation, optimization, and deployment—to a single worker skill, keeping all artifacts on your local machine without requiring hosted gateways.

Can I evaluate and optimize prompts automatically without uploading data?▼

Yes, you can run automatic prompt evolution and eval harnesses locally without registration or auth, applying safety gates to govern when data is uploaded or deployed while maintaining auditable decision records.

Do I need a hosted gateway to compare candidate agent routes?▼

No, you do not need a hosted gateway to compare routes. The workflow operates entirely local-first, allowing you to evaluate candidate routes against incumbents on your own machine and generate structured improvement reports.

What is the best way to build an eval harness from real agent traces?▼

The best way to build an eval harness from traces is to use an orchestrator that routes trace capture to a dedicated worker skill, generates auditable artifacts, and applies safety gates before any comparison or deployment.

What are the limitations of local-first agent improvement workflows?▼

The main limitation is that the orchestrator does not perform work inline; it strictly routes each workload step to a separate worker skill, requiring those worker skills to be present to execute the actual evaluation and optimization.