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Agent skill

Intercom Observability

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

A skill your agent uses when you need production monitoring for an Intercom integration — instrumenting API calls with metrics and traces, standing up dashboards, or wiring alerts for error rate…

MITAuto-check passedDevOps & Cloud

Install Intercom Observability

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill intercom-observability -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace intercom-observability --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/intercom-observability .claude/skills/intercom-observability && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
intercom-observability
GitHub stars
2.8k
Token cost
~1.7k tokens
SKILL.md length
561 words
Files
3 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when you need production monitoring for an Intercom integration — instrumenting API calls with metrics and traces, standing up dashboards, or wiring alerts for error rate…

  • Works in 6 steps: Prometheus metrics → Instrumented client wrapper → Structured logging → …
  • You need production monitoring for an Intercom integration — instrumenting API calls with metrics and traces
  • SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 4 more sections
  • Needs INTERCOM_ACCESS_TOKEN

What it does

Intercom Observability is an agent skill from jeremylongshore/tons-of-skills-marketplace. Use when you need production monitoring for an Intercom integration — instrumenting API calls with metrics and traces, standing up dashboards, or wiring alerts for error rate, latency, and rate-limit health. Set up observability for Intercom integrations with Prometheus metrics, OpenTelemetry traces, structured logging, and alert rules. Trigger with phrases like "intercom monitoring", "intercom metrics", "intercom observability", "monitor intercom", "intercom alerts", "intercom tracing".

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/examples.md` and `references/implementation.md`). Compatibility notes: Designed for Claude Code

It sits in DevOps & Cloud, covering Observability and Monitoring and alerting. It works with Intercom, OpenTelemetry and Prometheus. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • You need production monitoring for an Intercom integration — instrumenting API calls with metrics and traces
  • Standing up dashboards
  • Wiring alerts for error rate
  • Rate-limit health

Example prompts

  • “intercom monitoring”
  • “intercom metrics”
  • “intercom observability”
  • “/intercom-observability”

Requirements

  • Node.js
  • A credential in INTERCOM_ACCESS_TOKEN
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Prometheus metrics
  2. Instrumented client wrapper
  3. Structured logging
  4. OpenTelemetry tracing
  5. Alert rules
  6. Metrics endpoint

What it can do on your machine

Read from SKILL.md and the folder at commit 80f86df. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • prometheus.io
    • opentelemetry.io
    • getpino.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • INTERCOM_ACCESS_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Intercom Observability loads about 1.7k tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 129 tokens; SKILL.md has 561 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~129
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit 80f86df, republished under its MIT licence (© jeremylongshore). 561 words, ~1,680 tokens.

Download SKILL.mdSave it as .claude/skills/intercom-observability/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
intercom-observability
description
Use when you need production monitoring for an Intercom integration — instrumenting API calls with metrics and traces, standing up dashboards, or wiring alerts for error rate, latency, and rate-limit health. Set up observability for Intercom integrations with Prometheus metrics, OpenTelemetry traces, structured logging, and alert rules. Trigger with phrases like "intercom monitoring", "intercom metrics", "intercom observability", "monitor intercom", "intercom alerts", "intercom tracing".
allowed-tools
Read, Write, Edit
compatibility
Designed for Claude Code
version
1.6.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, support, messaging, intercom

Intercom Observability

Overview

Comprehensive observability for Intercom integrations covering Prometheus metrics, OpenTelemetry traces, structured logging, and alert rules for error rates, latency, and rate-limit usage. Read this page for the workflow and shape of each layer, then drill into references/implementation.md for the full, copy-pasteable code and references/examples.md for end-to-end worked scenarios.

Prerequisites

  • Prometheus or compatible metrics backend
  • OpenTelemetry SDK (optional, for tracing)
  • Pino or similar structured logger
  • Grafana or alerting system

Instructions

Build the six observability layers in order. Each step below is the summary and the essential skeleton — the complete implementation for every step lives in references/implementation.md.

Step 1: Prometheus metrics

Define five instruments on a shared Registry: a request counter, a duration histogram, an error counter, a rate-limit gauge, and a webhook counter. Label by endpoint/method/status (never by unbounded IDs — see Error Handling).

typescript
import { Registry, Counter, Histogram, Gauge } from "prom-client";
const registry = new Registry();
const intercomRequests = new Counter({
  name: "intercom_api_requests_total",
  help: "Total Intercom API requests",
  labelNames: ["endpoint", "method", "status"] as const,
  registers: [registry],
});
// + duration Histogram, error Counter, rate-limit Gauge, webhook Counter

Full metric set → references/implementation.md, Step 1.

Step 2: Instrumented client wrapper

Wrap IntercomClient in a Proxy that times every service method, increments the success/error counters, records error/status codes on IntercomError, and zeros the rate-limit gauge on a 429 — so instrumentation is automatic for all endpoints. Full proxy → references/implementation.md, Step 2.

Step 3: Structured logging

Configure Pino with a contact serializer that emits only id/role and never logs email, name, or phone. Add logIntercomOp and logWebhook helpers for consistent operation/webhook log lines. Full logger → references/implementation.md, Step 3.

Step 4: OpenTelemetry tracing

Wrap calls in tracedIntercomCall, which opens a per-operation intercom.* span, sets OK/ERROR status, records exceptions, and attaches status_code/error_code/request_id attributes on Intercom errors. Full tracer → references/implementation.md, Step 4.

Step 5: Alert rules

Ship the Prometheus rule group with five alerts: high error rate (>5%), high P95 latency (>3s), low rate limit (<1000), auth failures (401s), and webhook failures. Full YAML → references/implementation.md, Step 5.

Step 6: Metrics endpoint

Expose the registry on GET /metrics for Prometheus to scrape. Full route → references/implementation.md, Step 6.

Show full SKILL.md (255 more words)Show less

Output

Applying this skill produces:

  • Instrumented client — an IntercomClient proxy that emits metrics on every call, with zero per-call changes to existing code.
  • Metrics — intercom_api_requests_total, intercom_api_request_duration_seconds, intercom_api_errors_total, intercom_rate_limit_remaining, intercom_webhooks_processed_total, scraped at GET /metrics.
  • Traces — one per-operation intercom.* span per call, with Intercom error attributes on failures.
  • Structured logs — PII-redacted JSON operation and webhook log lines.
  • Alerts — a Prometheus rule group covering error rate, latency, rate limit, auth, and webhooks.
Key metrics summary
MetricTypeAlert Threshold
intercom_api_requests_totalCounterN/A (baseline)
intercom_api_request_duration_secondsHistogramP95 > 3s
intercom_api_errors_totalCounter> 5% error rate
intercom_rate_limit_remainingGauge< 1000
intercom_webhooks_processed_totalCounterFailed > 10%

Error Handling

IssueCauseSolution
High cardinalityToo many unique labelsUse endpoint groups, not IDs
Missing metricsUninstrumented callsWrap client with proxy
Alert stormsWrong thresholdsTune based on baseline data
Log volume too highDebug logging in prodSet LOG_LEVEL=info

Examples

The following scenarios are covered in full in references/examples.md:

  • Contact lookup end-to-end — one contacts.find call producing a counter increment, a histogram sample, a span, and a PII-redacted log line.
  • Rate-limit (429) event — how the proxy zeros the rate-limit gauge and which alerts fire.
  • Webhook success/failure accounting — counting processed vs. failed webhooks per topic.
  • Scraping /metrics — the raw Prometheus exposition and how it feeds Grafana and the alert rules.

Minimal end-to-end skeleton:

typescript
const client = instrumentedClient(new IntercomClient({ token: process.env.INTERCOM_ACCESS_TOKEN! }));
const contact = await tracedIntercomCall(
  "contacts.find",
  { "intercom.contact_id": contactId },
  () => client.contacts.find({ contactId })
);

Resources

Next Steps

For incident response once these signals are firing, see the intercom-incident-runbook skill, which turns these alerts into a triage-and-mitigation procedure.

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/.curated/intercom-observability of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/examples.md
  • references/implementation.md

Open the folder on GitHubat commit 80f86df

Compare with similar skills

Intercom Observability next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Intercom Observability compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Intercom Observability this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1.7kAutomated safety check: PassMIT
Developing Funboost Mixinydf0509/funboost893—~2.1kAutomated safety check: PassNone
Archestra Dev Observabilityarchestra-ai/archestra4.4k—~1.2kAutomated safety check: PassCustom licence
Frontmcp Observabilityagentfront/frontmcp146—~4.6kAutomated safety check: PassApache-2.0
Monitoring Observabilityahmedasmar/devops-claude-skills203—~3.9kAutomated safety check: PassNone
Observability Architecturemajiayu000/litellm-rs117—~1.3kAutomated safety check: PassMIT

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Works with

Categories

Questions about Intercom Observability

What does Intercom Observability do?

A skill your agent uses when you need production monitoring for an Intercom integration — instrumenting API calls with metrics and traces, standing up dashboards, or wiring alerts for error rate…. Intercom Observability is an agent skill from jeremylongshore/tons-of-skills-marketplace. Use when you need production monitoring for an Intercom integration — instrumenting API calls with metrics and traces, standing up dashboards, or wiring alerts for error rate, latency, and rate-limit health.

When should I use Intercom Observability?

Intercom Observability fits situations like: you need production monitoring for an Intercom integration — instrumenting API calls with metrics and traces; standing up dashboards; wiring alerts for error rate; rate-limit health.

How do I install Intercom Observability in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill intercom-observability -a claude-code`. Or copy the skill folder (skills/.curated/intercom-observability in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/intercom-observability in your project. Claude Code loads it when a task matches its description.

How do I install Intercom Observability in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill intercom-observability -a codex`. Or copy the skill folder (skills/.curated/intercom-observability in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/intercom-observability in your project. Codex loads it when a task matches its description.

Can I use Intercom Observability in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill intercom-observability -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/intercom-observability, .gemini/skills/intercom-observability, .github/skills/intercom-observability and .opencode/skills/intercom-observability in your project.

What does Intercom Observability need to run?

Going by SKILL.md and its folder, Intercom Observability needs credentials named INTERCOM_ACCESS_TOKEN. Our summary lists: Node.js; A credential in INTERCOM_ACCESS_TOKEN. Its frontmatter pre-approves these tools: Read, Write, Edit. Compatibility (from SKILL.md): Designed for Claude Code.

Does Intercom Observability access the network?

SKILL.md names 3 domains. As links in the text: prometheus.io, opentelemetry.io and getpino.io. This is read from the text; nothing was executed.

Is Intercom Observability safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Intercom Observability use?

Intercom Observability is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Intercom Observability use?

About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.9k tokens, read only when the agent opens those files.

What are the alternatives to Intercom Observability?

Skills that share tags, products or a category with Intercom Observability: Developing Funboost Mixin (ydf0509/funboost, 893 stars), Archestra Dev Observability (archestra-ai/archestra, 4.4k stars), Frontmcp Observability (agentfront/frontmcp, 146 stars) and Monitoring Observability (ahmedasmar/devops-claude-skills, 203 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Intercom Observability?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,825 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 9, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.