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ideogram-observability表意文字可观察性

Agent Skill

ideogram-observability 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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

533

周安装

22

GitHub Stars

2,116

下载量

174
CodexClaudeCursorGemini CLI

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

unknown

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:ideogram-observability(表意文字可观察性)
来源仓库:https://github.com/jeremylongshore/claude-code-plugins-plus-skills
仓库路径:skills/ideogram-observability
安装命令:
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill ideogram-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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill ideogram-observability

简介

ideogram-observability 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 适用于根据关键词或任务场景从多个来源中筛选出相关线索或文档片段。
  • 通过 npx skills add 命令安装,需确认权限范围和维护状态后再使用。
  • 建议结合原始 README 核验具体用法,避免触发不必要的联网或文件操作。
  • 使用前请评估是否会执行命令或读写文件,确保符合安全策略。

SKILL.md

Ideogram Observability

Overview

Monitor Ideogram AI image generation for latency, cost, error rates, and content safety rejections. Key metrics: generation duration (5-25s depending on model), credit burn rate, safety filter rejection rate, and API availability. Ideogram's API is synchronous, so all observability is request-level instrumentation.

Key Metrics

MetricTypeLabelsAlert Threshold
ideogram_generation_duration_msHistogrammodel, style, speedP95 > 25s
ideogram_generations_totalCountermodel, statusError rate > 5%
ideogram_credits_estimatedCountermodel>$10/hour
ideogram_safety_rejectionsCounterreason>10% rejection rate
ideogram_image_downloadsCounterstatusDownload failures > 1%

Instructions

Step 1: Instrumented Generation Wrapper

import { performance } from "perf_hooks";

interface GenerationMetrics {
  duration: number;
  model: string;
  style: string;
  status: "success" | "error" | "safety_rejected" | "rate_limited";
  seed?: number;
  resolution?: string;
}

const metricsLog: GenerationMetrics[] = [];

async function instrumentedGenerate(
  prompt: string,
  options: { model?: string; style_type?: string; aspect_ratio?: string } = {}
) {
  const model = options.model ?? "V_2";
  const style = options.style_type ?? "AUTO";
  const start = performance.now();

  try {
    const response = await fetch("https://api.ideogram.ai/generate", {
      method: "POST",
      headers: {
        "Api-Key": process.env.IDEOGRAM_API_KEY!,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({
        image_request: { prompt, model, style_type: style, ...options, magic_prompt_option: "AUTO" },
      }),
    });

    const duration = performance.now() - start;

    if (response.status === 422) {
      recordMetric({ duration, model, style, status: "safety_rejected" });
      throw new Error("Safety filter rejected prompt");
    }
    if (response.status === 429) {
      recordMetric({ duration, model, style, status: "rate_limited" });
      throw new Error("Rate limited");
    }
    if (!response.ok) {
      recordMetric({ duration, model, style, status: "error" });
      throw new Error(`API error: ${response.status}`);
    }

    const result = await response.json();
    const image = result.data[0];

    recordMetric({
      duration, model, style, status: "success",
      seed: image.seed, resolution: image.resolution,
    });

    return result;
  } catch (err) {
    if (!metricsLog.find(m => m.duration === performance.now() - start)) {
      recordMetric({ duration: performance.now() - start, model, style, status: "error" });
    }
    throw err;
  }
}

function recordMetric(metric: GenerationMetrics) {
  metricsLog.push(metric);

  // Emit to your metrics backend
  console.log(JSON.stringify({
    event: "ideogram.generation",
    ...metric,
    timestamp: new Date().toISOString(),
  }));
}

Step 2: Cost Estimation Metrics

const MODEL_COST_USD: Record<string, number> = {
  V_2_TURBO: 0.05, V_2: 0.08, V_2A: 0.04, V_2A_TURBO: 0.025,
};

function estimateCost(model: string, numImages: number = 1): number {
  return (MODEL_COST_USD[model] ?? 0.08) * numImages;
}

function costReport(metrics: GenerationMetrics[]) {
  const successful = metrics.filter(m => m.status === "success");
  const totalCost = successful.reduce((sum, m) => sum + estimateCost(m.model), 0);
  const byModel = Object.groupBy(successful, m => m.model);

  console.log("=== Ideogram Cost Report ===");
  console.log(`Total generations: ${successful.length}`);
  console.log(`Estimated cost: $${totalCost.toFixed(2)}`);

  for (const [model, gens] of Object.entries(byModel)) {
    const cost = (gens?.length ?? 0) * (MODEL_COST_USD[model] ?? 0.08);
    console.log(`  ${model}: ${gens?.length ?? 0} images, ~$${cost.toFixed(2)}`);
  }
}

Step 3: Prometheus Metrics (Optional)

import { Counter, Histogram, register } from "prom-client";

const generationDuration = new Histogram({
  name: "ideogram_generation_duration_seconds",
  help: "Ideogram image generation duration",
  labelNames: ["model", "style", "status"],
  buckets: [2, 5, 10, 15, 20, 30, 60],
});

const generationTotal = new Counter({
  name: "ideogram_generations_total",
  help: "Total Ideogram generations",
  labelNames: ["model", "status"],
});

const estimatedCostTotal = new Counter({
  name: "ideogram_estimated_cost_usd",
  help: "Estimated Ideogram API cost in USD",
  labelNames: ["model"],
});

// Expose metrics endpoint
app.get("/metrics", async (req, res) => {
  res.set("Content-Type", register.contentType);
  res.end(await register.metrics());
});

Step 4: Alerting Rules

# prometheus-rules.yml
groups:
  - name: ideogram
    rules:
      - alert: IdeogramGenerationSlow
        expr: histogram_quantile(0.95, rate(ideogram_generation_duration_seconds_bucket[15m])) > 25
        for: 5m
        annotations:
          summary: "Ideogram P95 generation time exceeds 25 seconds"

      - alert: IdeogramHighErrorRate
        expr: rate(ideogram_generations_total{status="error"}[10m]) / rate(ideogram_generations_total[10m]) > 0.05
        for: 5m
        annotations:
          summary: "Ideogram error rate exceeds 5%"

      - alert: IdeogramHighCostRate
        expr: rate(ideogram_estimated_cost_usd[1h]) > 10
        annotations:
          summary: "Ideogram burning >$10/hour"

      - alert: IdeogramSafetyRejectionSpike
        expr: rate(ideogram_generations_total{status="safety_rejected"}[1h]) / rate(ideogram_generations_total[1h]) > 0.1
        annotations:
          summary: "Ideogram safety rejection rate exceeds 10%"

Step 5: Dashboard Panel Queries

# Grafana dashboard panels:
# 1. Generation volume:     sum(rate(ideogram_generations_total[5m])) by (model)
# 2. Latency distribution:  histogram_quantile(0.5, rate(ideogram_generation_duration_seconds_bucket[5m]))
# 3. Error rate:            sum(rate(ideogram_generations_total{status!="success"}[5m])) / sum(rate(ideogram_generations_total[5m]))
# 4. Cost per hour:         sum(rate(ideogram_estimated_cost_usd[1h]))
# 5. Safety rejections:     sum(rate(ideogram_generations_total{status="safety_rejected"}[1h]))

Error Handling

IssueCauseSolution
Generation timeoutComplex prompt or QUALITY speedAlert at P95 > 25s, suggest TURBO
402 credit errorCredits exhaustedAlert immediately, pause batch jobs
High rejection rateUser prompts hitting safety filterReview prompt patterns, add pre-screening
429 sustainedConcurrency too highReduce queue concurrency, alert ops

Output

  • Instrumented generation wrapper with metrics collection
  • Cost estimation and reporting
  • Prometheus metrics with alerting rules
  • Grafana dashboard query templates

Resources

Next Steps

For incident response, see ideogram-incident-runbook.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

Codex

36.86%
按下载量换算64

Claude

27.81%
按下载量换算48

Cursor

18.52%
按下载量换算32

Gemini CLI

8.41%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

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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。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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