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Official OpenClaw rules 54%

Afrexai Agent Observability

Agent Observability & Monitoring

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0

Installs

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Status

ACTIVE

Visibility

PUBLIC

安装方式

直接复制以下提示词,发送给你的 AI 助手即可完成安装。

请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 Afrexai Agent Observability 技能。 若已安装,则直接安装 Afrexai Agent Observability 技能。

Overview

Skill Key
1kalin/afrexai-agent-observability
Author
1kalin
Source Repo
openclaw/skills
Version
-
Source Path
skills/1kalin/afrexai-agent-observability
Latest Commit SHA
d9150b35c79ef56829463fe296d59e8b80a16a72

Extracted Content

SKILL.md excerpt

# Agent Observability & Monitoring

Score, monitor, and troubleshoot AI agent fleets in production. Built for ops teams running 1-100+ agents.

## What This Does

Evaluates your agent deployment across 6 dimensions and returns a 0-100 health score with specific fixes.

## 6-Dimension Assessment

### 1. Execution Visibility (0-20 pts)
- Can you see what every agent is doing right now?
- Task queue depth, active/idle ratio, error rates
- **Benchmark**: Top quartile tracks 95%+ of agent actions in real-time

### 2. Cost Attribution (0-20 pts)
- Do you know exactly what each agent costs per task?
- Token spend, API calls, compute time, tool invocations
- **Benchmark**: Unmonitored agents waste 30-55% on retries and hallucination loops

### 3. Output Quality (0-15 pts)
- Are agent outputs validated before reaching users or systems?
- Accuracy sampling, hallucination detection, regression tracking
- **Benchmark**: 1 in 12 agent outputs contains a material error without monitoring

### 4. Failure Recovery (0-15 pts)
- What happens when an agent fails mid-task?
- Retry logic, graceful degradation, human escalation paths
- **Benchmark**: Mean time to detect agent failure without monitoring: 4.2 hours

### 5. Security & Boundaries (0-15 pts)
- Are agents staying within authorized scope?
- Tool access auditing, data exfiltration checks, permission drift
- **Benchmark**: 23% of production agents access tools outside their intended scope

### 6. Fleet Coordination (0-15 pts)
- Do multi-agent workflows hand off cleanly?
- Message passing reliability, deadlock detection, duplicate work
- **Benchmark**: Uncoordinated fleets duplicate 18-25% of work

## Scoring

| Score | Rating | Action |
|-------|--------|--------|
| 80-100 | Production-grade | Optimize and scale |
| 60-79 | Operational | Fix gaps before scaling |
| 40-59 | Risky | Immediate remediation needed |
| 0-39 | Blind | Stop scaling, instrument first |

## Quick Assessment Prompt

Ask the agent to evaluate your setup:

``...

README excerpt

# Agent Observability & Monitoring

Score your AI agent fleet health across 6 dimensions (0-100). Detect waste, prevent failures, attribute costs.

**What you get:**
- 6-dimension health scoring (execution visibility, cost attribution, output quality, failure recovery, security, fleet coordination)
- 2026 benchmarks per dimension
- Cost framework by fleet size (1-100+ agents)
- 90-day monitoring roadmap
- 7 common monitoring mistakes
- Industry-specific priority adjustments (10 verticals)

**Who it's for:** Ops teams, platform engineers, CTOs running AI agents in production.

**Links:**
- [AI Agent Context Packs](https://afrexai-cto.github.io/context-packs/) — $47 industry-specific decision frameworks
- [AI Revenue Leak Calculator](https://afrexai-cto.github.io/ai-revenue-calculator/) — find hidden waste
- [Agent Setup Wizard](https://afrexai-cto.github.io/agent-setup/) — configure your stack

Built by [AfrexAI](https://afrexai-cto.github.io/context-packs/).

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