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afrexai-agent-observabilityafrexai Agent 可观察性

Agent Skill

afrexai-agent-observability 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

18,072

周安装

753

GitHub Stars

公开资料未说明

下载量

6,024
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:afrexai-agent-observability(afrexai Agent 可观察性)
来源仓库:https://github.com/1kalin/afrexai-agent-observability
安装命令:
openclaw skills install afrexai-agent-observability
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install afrexai-agent-observability

简介

从六个关键维度评估和监控 AI 代理队列,以对管理 1-100 多个代理的运营团队的运行状况进行评分、识别问题并优化性能。

SKILL.md

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

ScoreRatingAction
80-100Production-gradeOptimize and scale
60-79OperationalFix gaps before scaling
40-59RiskyImmediate remediation needed
0-39BlindStop scaling, instrument first

Quick Assessment Prompt

Ask the agent to evaluate your setup:

Run the agent observability assessment against our current deployment:
- How many agents are running?
- What monitoring exists today?
- What broke in the last 30 days?
- What's our monthly agent spend?
- Who gets alerted when an agent fails?

Cost Framework

Company SizeUnmonitored WasteMonitoring InvestmentNet Savings
1-5 agents$2K-$8K/mo$500-$1K/mo$1.5K-$7K/mo
5-20 agents$8K-$45K/mo$2K-$5K/mo$6K-$40K/mo
20-100 agents$45K-$200K/mo$8K-$20K/mo$37K-$180K/mo

90-Day Monitoring Roadmap

Week 1-2: Inventory all agents, document intended scope, tag cost centers Week 3-4: Deploy execution logging (every tool call, every output) Month 2: Build dashboards — cost per task, error rate, latency P95 Month 3: Automated alerting — failure detection <5 min, cost anomaly flags, scope violations

7 Monitoring Mistakes

  1. Logging only errors (miss the slow degradation)
  2. No cost attribution (agents burn budget invisibly)
  3. Monitoring agents like servers (they need task-level observability)
  4. Manual review of agent outputs (doesn't scale past 3 agents)
  5. No baseline metrics (can't detect regression without a baseline)
  6. Alerting on everything (alert fatigue kills response time)
  7. Skipping agent-to-agent handoff monitoring (where most fleet failures happen)

Industry Adjustments

IndustryCritical DimensionWhy
Financial ServicesSecurity & BoundariesRegulatory audit trails mandatory
HealthcareOutput QualityClinical accuracy non-negotiable
LegalExecution VisibilityBilling requires task-level tracking
EcommerceCost AttributionMargin-sensitive, waste kills profit
SaaSFleet CoordinationMulti-tenant agent isolation
ManufacturingFailure RecoveryDowntime = production line stops
ConstructionSecurity & BoundariesSafety-critical document handling
Real EstateOutput QualityValuation errors = liability
RecruitmentFleet CoordinationCandidate pipeline handoffs
Professional ServicesCost AttributionClient billing accuracy

Go Deeper

  • AI Agent Context Packs — industry-specific decision frameworks: https://afrexai-cto.github.io/context-packs/
  • AI Revenue Leak Calculator — find where your business loses money to manual processes: https://afrexai-cto.github.io/ai-revenue-calculator/
  • Agent Setup Wizard — configure your agent stack in 5 minutes: https://afrexai-cto.github.io/agent-setup/

Built by AfrexAI — we help businesses run AI agents that actually make money.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

78.81%
按下载量换算4,748

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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