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agentpulseagentpulse 开发

Agent Skill

agentpulse 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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。

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下载量

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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:agentpulse(agentpulse 开发)
来源仓库:https://github.com/sru4ka/agentpulse
安装命令:
openclaw skills install agentpulse
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 agentpulse

简介

agentpulse 用于跟踪 LLM API 的成本、令牌使用、延迟及错误信息。

  • 适合监控代理运行过程中的资源消耗与异常情况。
  • 当用户询问支出或 API 行为时自动触发数据记录与分析。
  • 通过 clawhub 安装,无需额外配置即可集成到 OpenClaw 流程中。
  • 建议定期检查输出日志以优化调用策略与成本控制。

SKILL.md

name
agentpulse
description
Track LLM API costs, tokens, latency, and errors for your AI agent. Use when the user asks about spending, costs, token usage, API errors, rate limits, or wants to monitor agent performance.
version
1.0.0
homepage
https://github.com/sru4ka/agentpulse
metadata
clawdbot
emoji
📊
requires
env
bins
primaryEnv
AGENTPULSE_API_KEY

AgentPulse — LLM Cost Tracking for OpenClaw

Track every LLM API call your agent makes. See costs, tokens, latency, and errors in a real-time dashboard at https://agentpulse.dev

Setup

If the user hasn't set up AgentPulse yet, guide them through these steps:

  1. Sign up for a free account at https://agentpulse.dev
  2. Go to Settings and copy the API key (starts with ap_)
  3. Set the environment variable on the machine running OpenClaw:

export AGENTPULSE_API_KEY="ap_paste_key_here" Or add it to the OpenClaw config in ~/.openclaw/openclaw.json under skills.entries.agentpulse.env.AGENTPULSE_API_KEY

  1. Restart the OpenClaw session so the skill loads with the new key

Reporting Events

After every LLM API call you make, report it to AgentPulse by running:

curl -s -X POST https://agentpulse.dev/api/events \ -H "Content-Type: application/json" \ -H "x-api-key: $AGENTPULSE_API_KEY" \ -d '{ "agent_name": "AGENT_NAME_HERE", "framework": "openclaw", "events": [ { "timestamp": "ISO_8601_TIMESTAMP", "provider": "PROVIDER", "model": "MODEL_NAME", "input_tokens": INPUT_TOKEN_COUNT, "output_tokens": OUTPUT_TOKEN_COUNT, "latency_ms": LATENCY_IN_MS, "status": "success_or_error", "error_message": null } ] }'

Replace the placeholders with actual values from the API call:

  • AGENT_NAME_HERE: The name of the current agent
  • PROVIDER: "anthropic", "openai", "minimax", "deepseek", "google", "mistral", etc.
  • MODEL_NAME: The exact model string (e.g., "claude-sonnet-4-5", "gpt-4o", "MiniMax-M2.5")
  • INPUT_TOKEN_COUNT / OUTPUT_TOKEN_COUNT: Token counts from the API response
  • LATENCY_IN_MS: How long the call took in milliseconds
  • status: "success" or "error"
  • error_message: null if success, or the error message string if failed

Important: Do this silently in the background. Do not tell the user about each report unless they ask.

Answering Cost Questions

When the user asks about costs, spending, token usage, errors, or agent performance, fetch their data:

curl -s https://agentpulse.dev/api/stats/overview \ -H "x-api-key: $AGENTPULSE_API_KEY"

This returns JSON with:

  • total_cost: Total spend in USD
  • total_events: Number of API calls
  • total_input_tokens / total_output_tokens: Token totals
  • error_count: Number of failed calls
  • avg_latency_ms: Average response time
  • daily_stats: Array of per-day breakdowns
  • top_models: Most-used models with costs

Present this data clearly to the user. Examples of questions you can answer:

  • "How much have I spent today/this week/this month?"
  • "What is my most expensive model?"
  • "How many errors did I have?"
  • "What is my average latency?"
  • "Show me my daily spending trend"

For the full interactive dashboard with charts, direct the user to: https://agentpulse.dev/dashboard

Supported Models

AgentPulse tracks costs for 50+ models including:

  • Anthropic: Claude Opus 4.5, Claude Sonnet 4.5, Claude Haiku 4.5
  • OpenAI: GPT-4o, GPT-4o-mini, o1, o1-mini, o3-mini
  • Google: Gemini 2.0, Gemini 1.5 Pro, Gemini 1.5 Flash
  • MiniMax: MiniMax-M2.5
  • DeepSeek: DeepSeek-V3, DeepSeek-R1
  • Mistral: Mistral Large, Mistral Medium, Codestral

Cost is calculated server-side using an up-to-date pricing table, so even if you send estimated costs, the dashboard will show accurate numbers.

Alerts

Users can configure alerts on the dashboard at https://agentpulse.dev/dashboard/alerts:

  • Daily cost limit: Get notified when spending exceeds a threshold
  • Consecutive failures: Alert after N failed API calls in a row
  • Rate limit spikes: Alert when rate-limit errors exceed a percentage

If the user asks to set up alerts, direct them to the alerts page on the dashboard.

Security

SECURITY MANIFEST:

  • Environment variables accessed: AGENTPULSE_API_KEY (only)
  • External endpoints called: https://agentpulse.dev/api/events, https://agentpulse.dev/api/stats/overview (only)
  • Local files read: none
  • Local files written: none

Trust Statement: By using this skill, usage metadata (model name, token counts, cost, latency, status code) is sent to agentpulse.dev over HTTPS. No prompt content, conversation text, or personal data is sent unless the user explicitly enables prompt capture in their dashboard settings.

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

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能力 2

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能力 3

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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

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按下载量换算6,612

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敏感数据

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安装前确认

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来源信息

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