# Agent Orchestration Improve Agent

Systematic improvement of existing agents through performance analysis, prompt...

- Repository: https://github.com/rmyndharis/antigravity-skills (1913 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-14
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add rmyndharis/antigravity-skills --skill agent-orchestration-improve-agent -g -a claude-code -y`
Codex: `npx skills add rmyndharis/antigravity-skills --skill agent-orchestration-improve-agent -g -a codex -y`
Cursor: `npx skills add rmyndharis/antigravity-skills --skill agent-orchestration-improve-agent -g -a cursor -y`
Gemini CLI: `npx skills add rmyndharis/antigravity-skills --skill agent-orchestration-improve-agent -g -a gemini-cli -y`

## Description

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Agent Performance Optimization Workflow

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

[Extended thinking: Agent optimization requires a data-driven approach combining performance metrics, user feedback analysis, and advanced prompt engineering techniques. Success depends on systematic evaluation, targeted improvements, and rigorous testing with rollback capabilities for production safety.]

## Use this skill when

- Improving an existing agent's performance or reliability
- Analyzing failure modes, prompt quality, or tool usage
- Running structured A/B tests or evaluation suites
- Designing iterative optimization workflows for agents

## Do not use this skill when

- You are building a brand-new agent from scratch
- There are no metrics, feedback, or test cases available
- The task is unrelated to agent performance or prompt quality

## Instructions

1. Establish baseline metrics and collect representative examples.
2. Identify failure modes and prioritize high-impact fixes.
3. Apply prompt and workflow improvements with measurable goals.
4. Validate with tests and roll out changes in controlled stages.

## Safety

- Avoid deploying prompt changes without regression testing.
- Roll back quickly if quality or safety metrics regress.

## Phase 1: Performance Analysis and Baseline Metrics

Comprehensive analysis of agent performance using context-manager for historical data collection.

### 1.1 Gather Performance Data

```
Use: context-manager
Command: analyze-agent-performance $ARGUMENTS --days 30
```

Collect metrics including:

- Task completion rate (successful vs failed tasks)
- Response accuracy and factual correctness
- Tool usage efficiency (correct tools, call frequency)
- Average response time and token consumption
- User satisfaction indicators (corrections, retries)
- Hallucination incidents and error patterns

### 1.2 User Feedback Pattern Analysis

The rest: https://github.com/rmyndharis/antigravity-skills/blob/HEAD/skills/agent-orchestration-improve-agent/SKILL.md