alibaba-observability-and-cost

alibaba-observability-and-cost is a skill for Claude Code from jsgerman-oss/blackrim-nimbus-skills. It costs 82 tokens per session (2,510 once invoked), scanned A, original, MIT.

A guide for monitoring Alibaba Cloud systems and managing their cloud costs. It covers metrics, logs, application traces, alerts, dashboards, bills, budgets, resource tags, and savings options.

In plain words
What is it for?
Use it to set up observability for services, create SLO dashboards and alerts, diagnose performance or cost regressions, review quarterly spending, and assess reserved-capacity or savings options.
Why use it?
It helps teams find latency and error problems, understand where money is being spent, and detect unusual cost increases. Alibaba Cloud is a cloud provider with services such as CloudMonitor, Simple Log Service, and ARMS.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the cloud-alibaba plugin — 6 skills, 1 command, 2 agents shipped together

Good fit Use it to set up observability for services, create SLO dashboards and alerts, diagnose performance or cost regressions, review quarterly spending, and assess reserved-capacity or savings options.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jsgerman-oss/blackrim-nimbus-skills/alibaba-observability-and-cost
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add jsgerman-oss/blackrim-nimbus-skills --skill alibaba-observability-and-cost
Clone the repo
git clone --depth 1 https://github.com/jsgerman-oss/blackrim-nimbus-skills

Made for: Claude Code.

Or install cloud-alibaba, the plugin that ships this one along with the rest of its 6 skills, 1 command, 2 agents.

Wrote this? Show the measurements

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README.md
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Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,510 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00082 $0.02510
Opus 5 $0.00041 $0.01255
Sonnet 5 $0.00016 $0.00502
Haiku 4.5 $0.00008 $0.00251

Measured 12d ago against content hash 4d83d2e35264, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

alibaba-observability-and-cost scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

cloud-alibaba/skills/alibaba-observability-and-cost/SKILL.md · 153 lines

How it starts

The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Alibaba Observability and Cost

When to use

  • Setting up logs, metrics, and traces for a new service.
  • Building an SLO dashboard or wiring an alarm to an on-call channel.
  • Diagnosing a latency, error rate, or cost regression.
  • Evaluating whether to purchase Savings Plans or Reserved Instances.
  • Sizing a quarterly cloud cost review with resource group and tag-based breakdowns.

Observability pillars

Pillar Service
Infrastructure metrics CloudMonitor (metrics, host metrics, custom metrics, alarm rules)
Logs (ingest + query + alert) Simple Log Service (SLS) — Logstore for raw logs, Metricstore for timeseries metrics
APM / distributed traces ARMS (Application Real-Time Monitoring Service) — trace, profiling, RUM
Synthetics ARMS Synthetic Monitoring (canary-style availability checks)
Real User Monitoring ARMS Front-End Monitoring (browser + mobile SDK)
K8s observability ARMS Prometheus (managed Prometheus + Grafana for ACK clusters)
Dashboards CloudMonitor custom dashboards, SLS Dashboards, ARMS Service Map, Grafana

Defaults — every service

  • Structured JSON logs with a stable request-id field propagated across the call chain.
  • CloudMonitor host agent on every ECS instance for memory, disk, and process metrics (not reported by the hypervisor by default).
  • SLS log ingestion from every compute runtime (ECS, FC, ACK pods, SAE) — stdout + stderr collected by Logtail agent or SLS SDK.
  • ARMS trace SDK (OpenTelemetry or Alibaba-native) for any service with user-visible latency; propagate TraceId as a log field.
  • At least one alarm per service tied to user-visible pain — error rate, p99 latency, queue depth, dead-letter count. Never alarm on CPU alone.
  • SLS Logstore TTL: set explicitly (30–90 d for hot query; archive to OSS Cold Archive for long retention). Default unlimited TTL is an unbounded bill.

CloudMonitor

  • Metrics coverage: ECS / ACK node / RDS / Redis / OSS / SLB metrics are ingested automatically once the resource is created. Explicit agent install required for in-OS memory and disk.
  • Custom metrics: use CloudMonitor custom metric API or embedded-format logs (SLS-based) for application-level signals; batch PutCustomMetricData calls to stay within rate limits.
  • Alarm rules: specify metric, statistic (avg / max / sum), period (1 min minimum), threshold, and evaluation count. Alarm action: notification contact group (DingTalk, email, SMS) or EventBridge for programmatic response.
  • Event rules: resource events (ECS Spot interruption, Auto Scaling activity, RDS failover) are CloudMonitor events — wire to EventBridge → Function Compute for automated response.
  • Composite alarms: supported; combine multiple metric alarms to suppress noise (e.g., alert only when both error rate and latency exceed threshold simultaneously).
  • System event alarms: subscribe to Alibaba Cloud service disruptions and maintenance events via CloudMonitor system events.

Read the full file on GitHub · 153 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 153 lines · 82 tokens per session scan A 4d83d2e35264

Subscribe to this mod's changes

alibaba-observability-and-cost is a skill published in the GitHub repository jsgerman-oss/blackrim-nimbus-skills (8 stars, last pushed 26d ago), licensed MIT. It adds 82 tokens to every session and 2,510 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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