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SkillAtlasSkill 详情
coreweave-observability
A model-agnostic agent-skills platform.
审核状态：已审核Quality 72Security 70
项目 README
来源文件：README.md
Tons of Skills
A model-agnostic agent-skills platform. The canonical layer is harness-free by construction; Claude Code is currently the verified-native harness. Other harnesses remain engineering candidates until their native-path integration is verified; source research alone is never presented as public support.
Version semantics: the release badge is this marketplace's display version. npm packages, including the
ccpiCLI and publishable plugins, retain their own package versions; they are intentionally not expected to equal the display version. The version-surface checker governs the display surfaces without rewriting package semver.
Install
Inside Claude Code, one command installs the whole marketplace:
/plugin marketplace add jeremylongshore/claude-code-plugins
Or use the CLI:
pnpm add -g @intentsolutionsio/ccpi
ccpi install devops-automation-pack
Browse the marketplace · Explore plugins · Download bundles
Killer Skill of the Week — no-ai-slop by Peter Yang
Strip AI slop from any draft — named-pattern edits that keep the writer's real voice
no-ai-slop does two jobs and refuses to fake a third. In Edit mode it makes the minimum effective edit — cutting throat-clearing, weak verbs, and abstract nouns while deliberately preserving the writer's cadence, bluntness, humor, and honest admissions, so a rough draft still sounds like the same person afterward. In Detect mode it names each AI-slop pattern it finds, quotes the offending line, and gives the fix in a few words — and pointedly does NOT score the draft or guess whether an AI wrote it. That restraint is the whole point: AI detectors guess; named patterns are evidence the reader can check. MIT-licensed, single focused skill, actively maintained by Peter Yang.
"AI detectors guess. Named patterns are evidence the user can check." — Peter Yang
Grade: A | Week of July 22, 2026 (W30) | View on GitHub
Previous picks: tonone, mnemos, databricks-pack, kobiton-automate, skyvern, code-cleanup, web-analytics, token-optimizer, executive-assistant-skills, skill-creator, cursor-pack, crypto-portfolio-tracker. See all at tonsofskills.com.
Scale, labeled
Every number below names the cohort it counts and the command that reproduces it — an unlabeled count is how a corpus ends up with five contradictory answers to "how many skills."
| Count | Cohort | Reproduce with |
|---|---|---|
| 442 | catalog plugins (catalog-entry cohort) | node scripts/generate-readme-toc.mjs over marketplace.extended.json |
| 3,067 | marketplace-visible skills (distinct) | node -e "import('./scripts/corpus-resolver.mjs').then(m=>console.log(m.resolveCorpus('marketplace-visible').length))" |
| 347 | agent definitions in plugins | git ls-files 'plugins/**' | grep '/agents/.*\.md' |
| 19 | plugin categories | ls -d plugins/*/ |
📦 Live npm Downloads
Across 396 published packages in the claude-code-plugins namespace. Updated daily by GitHub Actions.
| Window | All packages | Established (>30d) |
|---|---|---|
| Last 24 hours | 962 | 962 |
| Last 7 days | 2,920 | 2,916 |
| Last 30 days | 12,868 | 12,779 |
"Established" excludes packages first published within the last 30 days, so a bulk-publish event doesn't dominate the headline.
Top 10 by last 30 days:
Last refreshed 2026-08-19T03:03:05.709Z.
Ways in
Five real questions, five doors — each resolves to a live, generated surface, never a hand-maintained list:
- By category — the table below, regenerated from the catalog on every sync.
- By plugin — tonsofskills.com/explore, the full browsable catalog.
- By skill — tonsofskills.com/skills, searchable across the whole corpus.
- By job to be done — tonsofskills.com/cowork, curated bundles as one-click downloads.
- By certification tier — the Certification section below, rendered from the live report.
Browse by category
The 19 categories below link into the live marketplace. Plugin counts are the catalog-entry cohort — regenerated from marketplace.extended.json by this generator; the catalog itself lives on tonsofskills.com, never in this file (§ 6A of the platform blueprint).
| Category | Plugins | |
|---|---|---|
| 🤖 | AI & Machine Learning | 36 |
| 🎭 | AI Agents & Agency | 10 |
| 🔌 | API Development | 26 |
| 💼 | Business Tools | 6 |
| 👥 | Community | 21 |
| ₿ | Crypto & Web3 | 27 |
| 💾 | Database | 26 |
| 🎨 | Design | 2 |
| 🔧 | DevOps & Infrastructure | 36 |
| 📚 | Examples & Templates | 5 |
| 🧩 | MCP Servers | 16 |
| 📦 | Packages | 5 |
| ⚡ | Performance | 25 |
| ✅ | Productivity | 30 |
| 🎁 | SaaS Skill Packs | 106 |
| 🔐 | Security | 27 |
| ✨ | Skill Enhancers | 9 |
| 🧪 | Testing | 28 |
| 📁 | Analytics | 1 |
What the classes mean
Four artifact classes live in this repository, distinguished on sight and never blurred — provenance is a truth requirement here, not a UX nicety:
| Class | What it is | How the reader can tell |
|---|---|---|
| Canonical skill | First-party, harness-free, the source of truth | No .source.json in its plugin directory |
| Generated adapter | A thin, machine-produced harness projection | Lives under a generated path with a "generated — do not edit" header |
| First-party package | An Intent Solutions distribution (npm, cowork zip) | @intentsolutionsio scope, IS-authored license |
| Upstream mirror | Somebody else's work, hosted mirror-by-default | .source.json present — upstream author, license, and pinned commit recorded |
Certification
Not yet certified. The certification program (tiers T0–T4 with retained, hash-matched evidence) is a later epic of the platform blueprint; until its report exists, no artifact on this surface claims a tier. This line is rendered from the absence of certification-report.json — honestly, not cosmetically.
Contribute
Start with the contribution guide, then the intake and review standards every submission passes through:
- Contribution requirements — including the AI-assistance disclosure expectation.
- Skill submission intake standard — the tiered document matrix new plugins ship with.
- External-PR review standard — how maintainers triage and what gets a submission merged.
Governance
- STANDARDS.md — the public spec posture and the canonical-documents index (one owner per fact class).
- The platform master blueprint — CI gates, release, docs governance, and this README's own landing contract.
- GOVERNANCE.md · SECURITY.md · LICENSE
Provenance
External plugins are hosted mirror-by-default: the contributor's repository stays the source of truth, every mirrored source is pinned in a content lockfile, and upstream credit — author, license, resolved commit — is recorded in the mirror itself. Improvements flow by upstreaming to the author's repository, never by silently editing the mirror. The full decision record is the external-sync model.
License
MIT for the repository scaffolding and first-party tooling; each plugin carries its own license in its manifest, and mirrored plugins keep their upstream license verbatim.
DevOps 与部署
中风险
- 来源需自行核对维护者身份。
- 包含脚本或命令调用，安装前请复核。
- 可能需要外部 token、网络权限或第三方服务。
- 未检测到高风险命令。
- 扫描发现：2 条。
Codex — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
- 将 "skills/.curated/coreweave-observability" 文件夹复制到 Codex 的 skills 目录中。
- 重启 Codex 让新的 skill 生效。
Codex — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Codex 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Codex 让新的 skill 生效。
Claude Code — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
- 将 "skills/.curated/coreweave-observability" 文件夹复制到 Claude Code 的 skills 目录中。
- 重启 Claude Code 让新的 skill 生效。
Claude Code — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Claude Code 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Claude Code 让新的 skill 生效。
Cursor — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
- 将 "skills/.curated/coreweave-observability" 文件夹复制到 Cursor 的 skills 目录中。
- 重启 Cursor 让新的 skill 生效。
Cursor — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Cursor 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Cursor 让新的 skill 生效。
GitHub Copilot — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
- 将 "skills/.curated/coreweave-observability" 文件夹复制到 GitHub Copilot 的 skills 目录中。
- 重启 GitHub Copilot 让新的 skill 生效。
GitHub Copilot — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 GitHub Copilot 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 GitHub Copilot 让新的 skill 生效。
Windsurf — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
- 将 "skills/.curated/coreweave-observability" 文件夹复制到 Windsurf 的 skills 目录中。
- 重启 Windsurf 让新的 skill 生效。
Windsurf — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Windsurf 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: coreweave-observability
description: 'Set up GPU monitoring and observability for CoreWeave workloads.
Use when implementing GPU metrics dashboards, configuring alerts,
or tracking inference latency and throughput.
Trigger with phrases like "coreweave monitoring", "coreweave observability",
"coreweave gpu metrics", "coreweave grafana".
'
allowed-tools: Read, Write, Edit, Bash(kubectl:*), Grep
version: 1.11.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags:
- saas
- gpu-cloud
- kubernetes
- inference
- coreweave
compatibility: Designed for Claude CodeCoreWeave Observability
Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.
Overview
CoreWeave runs GPU-intensive workloads on Kubernetes where hardware failures, memory exhaustion, and underutilization directly impact cost and reliability. Observability must cover DCGM GPU metrics, Kubernetes pod health, inference latency, and job completion rates. Proactive monitoring prevents wasted spend on idle GPUs and catches OOM conditions before they cascade.
Prerequisites
- A metrics backend receiving Kubernetes and DCGM exporter metrics.
- A named dashboard and on-call owner for the namespace or service.
- Log and trace redaction rules that exclude prompts, model outputs, tokens, and credentials.
Instructions
- Tag metrics with bounded values such as namespace, model family, and status; do not use request IDs, prompts, or user identifiers as labels.
- Build dashboards for utilization, memory, queue depth, latency, error rate, and restart rate, then set alert thresholds from a measured baseline.
- Route critical alerts to the responsible on-call team and link a runbook that includes a safe scale-down or rollback action.
- Test one alert in a non-production namespace and verify that the receipt contains only operational metadata, not workload data.
Key Metrics
| Metric | Type | Target | Alert Threshold |
|---|---|---|---|
| GPU utilization | Gauge | > 60% | < 20% for 30m |
| GPU memory usage | Gauge | < 85% | > 95% for 5m |
| Inference latency p99 | Histogram | < 200ms | > 500ms |
| Job completion rate | Counter | > 99% | < 95% per hour |
| Pod restart count | Counter | 0 | > 3 in 15m |
| Node GPU temperature | Gauge | < 80C | > 85C for 10m |
Instrumentation
async function trackInference(model: string, fn: () => Promise<any>) {
const start = Date.now();
try {
const result = await fn();
metrics.record('coreweave.inference.latency', Date.now() - start, { model, status: 'ok' });
metrics.increment('coreweave.inference.completed', { model });
return result;
} catch (err) {
metrics.increment('coreweave.inference.errors', { model, error: err.code });
throw err;
}
}
Health Check Dashboard
async function coreweaveHealth(): Promise<Record<string, string>> {
const gpu = await queryPrometheus('avg(DCGM_FI_DEV_GPU_UTIL)');
const mem = await queryPrometheus('avg(DCGM_FI_DEV_FB_USED/(DCGM_FI_DEV_FB_USED+DCGM_FI_DEV_FB_FREE))');
const pods = await queryPrometheus('kube_deployment_status_replicas_available{namespace="inference"}');
return {
gpu_utilization: gpu > 20 ? 'healthy' : 'underutilized',
gpu_memory: mem < 0.9 ? 'healthy' : 'critical',
inference_pods: pods > 0 ? 'healthy' : 'down',
};
}
Alerting Rules
const alerts = [
{ metric: 'DCGM_FI_DEV_GPU_UTIL', condition: 'avg < 20', window: '30m', severity: 'warning' },
{ metric: 'gpu_memory_pct', condition: '> 0.95', window: '5m', severity: 'critical' },
{ metric: 'inference_latency_p99', condition: '> 500ms', window: '10m', severity: 'warning' },
{ metric: 'pod_restart_count', condition: '> 3', window: '15m', severity: 'critical' },
];
Structured Logging
function logGpuEvent(event: string, node: string, data: Record<string, any>) {
console.log(JSON.stringify({
service: 'coreweave', event, node,
gpu_model: data.gpu_model, utilization: data.util,
memory_pct: data.memPct, temperature: data.temp,
timestamp: new Date().toISOString(),
}));
}
Error Handling
| Signal | Meaning | Action |
|---|---|---|
| GPU util < 20% sustained | Idle GPUs burning cost | Scale down or reassign workload |
| GPU memory > 95% | OOM imminent | Reduce batch size or add nodes |
| Pod CrashLoopBackOff | Driver or config failure | Check DCGM logs, restart node |
| Inference latency spike | Contention or throttling | Review GPU temp and queue depth |
| Node NotReady | Hardware or network issue | Cordon node, migrate pods |
Output
- A bounded-label GPU and workload dashboard with actionable alert rules.
- A redacted event trail linking an alert to the namespace, model family, severity, and response owner.
- A tested incident path for capacity, memory, latency, and node-health failures.
Examples
Use a non-production workload to verify the alert route without disrupting a live service:
kubectl -n inference-staging scale deployment/summarizer --replicas=0
kubectl -n inference-staging get pods --watch
# Confirm the unavailable-replica alert reaches the test route, then restore it.
kubectl -n inference-staging scale deployment/summarizer --replicas=1
Record the alert ID and restoration time, not request or model content. Escalate a node or memory alert through the runbook before deleting pods or changing quotas.
Resources
Next Steps
For incident response, see coreweave-incident-runbook.
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