telemetry

A workflow for adding small runtime logs and debug markers to macOS apps using Apple’s unified logging system. It also describes checking those events after building and running the app.

In plain words
What is it for?
Use it when tracing window, sidebar, menu, menu bar, loading, syncing, fallback, or performance behavior, and when verifying that expected events actually occur.
Why use it?
It helps reveal what the app did at runtime without filling the codebase with noisy logs or exposing secrets and private data.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/robinebers/openusage/telemetry
Any agent
npx skills add robinebers/openusage --skill telemetry
Clone the repo
git clone --depth 1 https://github.com/robinebers/openusage

Made for: Claude Code, Codex.

Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 830 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 94% copy Near-identical to another mod 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 $0.00066 $0.00830
Opus 5 $0.00033 $0.00415
Sonnet 5 $0.00013 $0.00166
Haiku 4.5 $0.00007 $0.00083

Measured 2d ago against content hash 7ff07549cd06, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

telemetry 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 2d 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.

Origin

This is a copy

94% identical to macos-telemetry — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agents/skills/telemetry/SKILL.md · 87 lines

How it starts

The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Telemetry

Quick Start

Use this skill to add lightweight app instrumentation that helps debug behavior without turning the codebase into a logging landfill. Prefer Apple's unified logging APIs and verify the events after a build/run loop.

Core Guidelines

  • Prefer Logger from the OSLog framework for structured app logs.
  • Give each feature a clear subsystem/category pair so runtime filtering stays easy.
  • Log meaningful user and app lifecycle events: window opening, sidebar selection changes, menu commands, menu bar extra actions, sync/load milestones, and unexpected fallback paths.
  • Keep info logs concise and stable. Use debug logs for noisy state details.
  • Do not log secrets, auth tokens, personal data, or raw document contents.
  • Add signposts only when measuring timing or performance spans; do not overinstrument by default.

Minimal Logger Pattern

import OSLog

private let logger = Logger(
  subsystem: Bundle.main.bundleIdentifier ?? "SampleApp",
  category: "Sidebar"
)

@MainActor
func selectItem(_ item: SidebarItem) {
  logger.info("Selected sidebar item: \(item.id, privacy: .public)")
  selection = item.id
}

Use feature-specific categories like Windowing, Commands, MenuBar, Sidebar, Sync, or Import so logs can be filtered quickly.

Workflow

  1. Identify the behavior that needs observability.

    • Window open/close
    • Sidebar or inspector selection changes
    • Menu or keyboard command actions
    • Menu bar extra actions
    • Background load/sync/import events
    • Error and recovery paths
  2. Add the smallest useful instrumentation.

    • Create one Logger per feature area or type.
    • Log action boundaries and key state transitions.
    • Prefer one high-signal line per user action over noisy value dumps.
  3. Build and run the app.

    • Use build-run-debug for the build/run loop.
    • If script/build_and_run.sh exists, prefer ./script/build_and_run.sh --telemetry for live telemetry checks or ./script/build_and_run.sh --logs for broader process logs.
    • Exercise the UI or command path that should emit telemetry.

Read the full file on GitHub · 87 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. 2d ago First seen · 87 lines · 66 tokens per session scan A 7ff07549cd06

Subscribe to this mod's changes

telemetry is a skill published in the GitHub repository robinebers/openusage (3,975 stars, last pushed today), licensed MIT. It adds 66 tokens to every session and 830 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to macos-telemetry, differing in 8 lines, and is treated as a copy.

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