agentsclimarketplace

Telemetry aggregator

Skill hamza-ali-shahjahan/hamzaish/factory/agents/portfolio/telemetry-aggregator

Your Claude Code setup that builds, launches, and learns — across the whole product lifecycle. Point Claude Code at this and it runs your whole startup factory — and gets smarter every session. Works with Cursor, Codex & Windsurf too.

Install
npx -y skills add hamza-ali-shahjahan/hamzaish --skill telemetry-aggregator

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 5 stars5 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Pull metrics across all products into a single view. Powers the dashboard and the daily/weekly briefings.

SKILL.md

2.7 KB, as published. Nobody here has run it

Telemetry Aggregator

When you activate

  • Powers dashboard/ rendering
  • Powers /portfolio-pulse skill
  • User asks: "give me numbers across all products", "what's the total MRR?", "total errors today?"

What you produce

A normalized telemetry payload — usable by the dashboard's renderer or any downstream agent:

{
  "as_of": "2026-05-19T14:32:00Z",
  "totals": {
    "mrr_usd": 0,
    "paying_customers": 0,
    "active_users_7d": 0,
    "errors_24h": 0,
    "search_impressions_7d": 0
  },
  "products": [
    {
      "slug": "linkedup",
      "stage": "mvp",
      "metrics": {
        "mrr_usd": 0,
        "paying_customers": 0,
        "active_users_7d": 0,
        "signups_7d": 0,
        "errors_24h": 0,
        "top_queries_7d": [{"query": "...", "clicks": 0, "impressions": 0, "position": 0}],
        "health_score": 0.0
      },
      "connectors_status": {
        "stripe": "connected | not_connected | error",
        "posthog": "connected | not_connected | error",
        "sentry": "connected | not_connected | error",
        "gsc": "connected | not_connected | error"
      }
    }
  ]
}

Protocol

  1. Read all products/*/product.config.json.
  2. For each product, for each connector listed, call the connector with the credentials referenced.
  3. Handle errors gracefully — a missing connector returns not_connected, an erroring one returns error with a brief reason in the dashboard, not a crash.
  4. Compute the composite health_score per product: 
    health = w_retention * D7_retention + w_revenue * MRR_growth + w_errors * (1 - error_rate_normalized)
    weights default: 0.4 / 0.3 / 0.3. Tune per product type.
  5. Cache results for 5 minutes (don't hammer the APIs).

Connectors

All in dashboard/lib/connectors/:

  • stripe.ts — fetchStripeMetrics(account_id)
  • posthog.ts — fetchPostHogMetrics(project_id)
  • sentry.ts — fetchSentryMetrics(org, project)
  • gsc.ts — fetchGSCMetrics(property)
  • plausible.ts — fetchPlausibleMetrics(domain)
  • ga4.ts — fetchGA4Metrics(measurement_id)

Each returns a normalized shape: { status: 'ok' | 'error', data?: {...}, error?: string }.

Sources

  • stack/analytics-stack.md (for the connector list)
  • dashboard/lib/connectors/*.ts (the actual implementations)

What you don't do

  • Don't fail the whole aggregation because one connector errors. Return partial data.
  • Don't store secrets in product.config.json — only references.
  • Don't call connectors more often than every 5 minutes per product.

Gives 0 of the 12 instructions most monitoring observability skills give

Counted across 481 of the 483 authors here whose files we hold, read 2026-08-06

  • link every alert to a runbookin 43 of 481, across 35 files
  • use structured json loggingin 36 of 481, across 31 files
  • alert on user-facing symptomsin 20 of 481, across 15 files
  • emit structured JSON logs with stable event namesin 18 of 481, across 13 files
  • propagate trace context across boundariesin 16 of 481
  • use histograms for latency trackingin 14 of 481, across 9 files
  • use OpenTelemetry for distributed tracingin 13 of 481, across 8 files
  • include a correlation ID on every log linein 13 of 481, across 8 files
  • Define service level objectivesin 10 of 481, across 7 files
  • Call useAzureMonitor before importing other modulesin 9 of 481, across 2 files
  • stop and ask for clarification if inputs are missingin 9 of 481, across 2 files
  • define on-call questions before adding telemetryin 9 of 481, across 4 files

Said here and by no other author read

  • read all product config files
  • call each connector with referenced credentials
  • handle connector errors gracefully without crashing
  • compute composite health score per product
  • use default health score weights of 0.4 retention 0.3 revenue 0.3 errors
  • cache results for 5 minutes

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.