Macos telemetry probe

Skill Xopoko/build-swift-apps/skills/macos-telemetry-probe

Add and verify lightweight macOS runtime telemetry with `Logger`/`os.Logger`, `log stream`, Console filters, signposts, and build-run checks.From its SKILL.md

Install

npx -y skills add Xopoko/build-swift-apps --skill macos-telemetry-probe

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

SKILL.md

2.0 KB, 406 tokens by cl100k_base, as published. Nobody here has run it

macOS Telemetry Probe

Add high-signal app instrumentation without leaving noisy permanent logs. Prefer Apple's unified logging and verify events after build/run.

Rules

Use Logger from OSLog.
Give each feature a clear subsystem/category.
Log meaningful lifecycle/user events: windows, sidebar/inspector selection, commands, menu bar actions, sync/load milestones, fallback/error paths.
Keep info logs stable; use debug for noisy state.
Never log secrets, tokens, personal data, or raw document contents.
Add signposts only for timing/performance spans.

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 categories like Windowing, Commands, MenuBar, Sidebar, Sync, or Import.

Workflow

Identify the behavior needing observability.
Add one useful log per action boundary or key state transition.
Build/run with macos-runtime-debugger; if present, prefer ./script/build_and_run.sh --telemetry or --logs.
Exercise the UI/command path.

Verify through Console or:

log stream --style compact --predicate 'process == "AppName"'
log stream --style compact --predicate 'subsystem == "com.example.app" && category == "Sidebar"'

Keep useful logs; remove or demote temporary noise.

Verification

Confirm the app builds, the relevant action emits exactly one clear line or bounded sequence, logs filter by process/subsystem/category, no sensitive payloads are written, and temporary debug noise is gone. If the task is mainly crash/backtrace work, switch to macos-runtime-debugger.

What ships with it: 1 file

367 B alongside SKILL.md

agents/

openai.yaml367 B

Gives 0 of the 12 instructions most monitoring observability skills give in 406 tokens

Counted across 530 of the 532 authors here whose files we hold, read 2026-09-06

Use structured JSON loggingin 40 of 530, across 36 files
Link every alert to a runbookin 29 of 530, across 27 files
Attach correlation IDs to every log linein 19 of 530, across 16 files
Alert on symptoms rather than causesin 19 of 530, across 17 files
Use OpenTelemetry for distributed tracingin 15 of 530, across 14 files
Alert on symptoms users feelin 15 of 530, across 13 files
Implement health check endpointsin 14 of 530, across 10 files
Inspect existing dashboards firstin 12 of 530, across 4 files
Build the minimum useful boardin 12 of 530, across 4 files
Start from operator questionsin 12 of 530, across 4 files
Propagate trace context across boundariesin 11 of 530, across 10 files
Include trace id in all log entriesin 10 of 530, across 9 files

Said here and by no other author read

Confirm execution context is macOS
Use Logger from OSLog
Give each feature a subsystem and category
Log meaningful lifecycle and user events
Keep info logs stable
Use debug for noisy state

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.