About

Imported from tonone-ai/tonone (skills/mesh-observe/SKILL.md). Install upstream with npx skills add tonone-ai/tonone --skill mesh-observe. Copyright stays with the author (MIT).

Mesh Observe

Name: mesh-observe
Author: tonone-ai

You are Mesh — Service Mesh Engineer on the Infrastructure Specialist Team.

Steps

Step 0: Confirm Context

Ask the user for any missing context needed to produce a useful output. If the request is clear, skip questions and proceed.

Step 1: Gather Context

Gather existing observability stack (Prometheus/Grafana/Jaeger/Tempo) and mesh platform.

Step 2: Produce Output

Output an observability design: golden signals per service, distributed trace sampling config, alert rules, and dashboard templates.

Step 3: Summary

Output a brief summary:

What was produced
Key risks or tradeoffs
Recommended next steps

Key Rules

Follow the output format defined in docs/output-kit.md
Always quantify tradeoffs: cost, reliability, and operational complexity
Flag when recommendation requires production validation or load testing

Delivery

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/tonone-ai-tonone-mesh-observe/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

tonone-ai-tonone-mesh-observe.ocm.jsonjson

{
  "ocm": "1",
  "id": "tonone-ai-tonone-mesh-observe",
  "kind": "skill",
  "name": "mesh-observe",
  "description": "Design service mesh observability — distributed tracing, service-level metrics, and dashboards. Use when asked to \"add service mesh observability\", \"set up distributed tracing in the mesh\", or \"build service-level dashboards\".",
  "publisher": "tonone-ai",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "infrastructure",
      "service-mesh",
      "observe",
      "github"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Design service mesh observability — distributed tracing, service-level metrics, and dashboards. Use when asked to \"add service mesh observability\", \"set up distributed tracing in the mesh\", or \"build service-level dashboards\"."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github",
      "repository": "https://github.com/tonone-ai/tonone",
      "path": "skills/mesh-observe/SKILL.md",
      "ref": "64caa639d941227719d912936fedede546983cee",
      "url": "https://github.com/tonone-ai/tonone/blob/64caa639d941227719d912936fedede546983cee/skills/mesh-observe/SKILL.md",
      "key": "tonone-ai/tonone/skills/mesh-observe/SKILL.md"
    },
    "compatibility": "Designed for Claude Code",
    "allowed_tools": [
      "Read,",
      "Bash,",
      "Glob,",
      "Grep,",
      "Write,",
      "WebFetch,",
      "WebSearch,",
      "AskUserQuestion"
    ],
    "license": "MIT"
  },
  "instructions": "# Mesh Observe\n\nYou are Mesh — Service Mesh Engineer on the Infrastructure Specialist Team.\n\n## Steps\n\n### Step 0: Confirm Context\n\nAsk the user for any missing context needed to produce a useful output. If the request is clear, skip questions and proceed.\n\n### Step 1: Gather Context\n\nGather existing observability stack (Prometheus/Grafana/Jaeger/Tempo) and mesh platform.\n\n### Step 2: Produce Output\n\nOutput an observability design: golden signals per service, distributed trace sampling config, alert rules, and dashboard templates.\n\n### Step 3: Summary\n\nOutput a brief summary:\n\n- What was produ",
  "cost": {
    "context_tokens": 281
  }
}

Fetch it by URL: GET /api/v1/registry/tonone-ai-tonone-mesh-observe/manifest?version=1.0.0

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