robinebers/openusage is a native macOS menu-bar application that tracks usage of AI coding subscriptions, including limits, credits, and spending. Developers use it to monitor plans from services such as Claude, Codex, Cursor, and Copilot, while the catalogue entries support related workflows.
Borrowing it
Nothing to install: this file belongs to robinebers/openusage. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/robinebers/openusage/main/.agents/skills/macos-telemetry/SKILL.mdgit clone --depth 1 https://github.com/robinebers/openusageWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/robinebers/openusage/macos-telemetry)<a href="https://agentmods.dev/skills/robinebers/openusage/macos-telemetry"><img src="https://agentmods.dev/badge/skills/robinebers/openusage/macos-telemetry/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/robinebers/openusage/macos-telemetry"><img src="https://agentmods.dev/badge/skills/robinebers/openusage/macos-telemetry.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.00803 |
| Opus 5.5 | $0.00014 | $0.00321 |
| Sonnet 5.5 | $0.00007 | $0.00161 |
| Haiku 4.5 | $0.00003 | $0.00080 |
Grade A, and why
macos-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 2mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- telemetry — 94% identical, 6 lines differ
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
Loggerfrom theOSLogframework 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
-
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
-
Add the smallest useful instrumentation.
- Create one
Loggerper feature area or type. - Log action boundaries and key state transitions.
- Prefer one high-signal line per user action over noisy value dumps.
- Create one
-
Build and run the app.
- Use
macos-build-run-debugfor the build/run loop. - If
script/build_and_run.shexists, prefer./script/build_and_run.sh --telemetryfor live telemetry checks or./script/build_and_run.sh --logsfor broader process logs. - Exercise the UI or command path that should emit telemetry.
- Use
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.
- 2mo ago First seen · 87 lines · 34 tokens per session scan A b58ff03f4133
macos-telemetry is a skill published in the GitHub repository robinebers/openusage (4,556 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 803 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
macos-telemetry compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| macos-telemetryrobinebers/openusage | Skill | 4.6k | yesterday | MIT | A | 34 | Claude Code, Codex |
| systemic-issue-triageGentleman-Programming/gentle-ai | Skill | 8.4k | yesterday | MIT | A | 57 | Claude Code, Codex |
| issue-root-resolutionGentleman-Programming/gentle-ai | Skill | 8.4k | yesterday | MIT | A | 45 | Claude Code, Codex |
| rdd-defect-workflowGentleman-Programming/gentle-ai | Skill | 8.4k | yesterday | MIT | A | 41 | Claude Code, Codex |
| debug-livemicrosoft/DebugMCP | Skill | 579 | 2d ago | MIT | A | 84 | Claude Code |
| debug-helperJackyST0/awesome-agent-skills | Skill | 667 | today | CC0-1.0 | A | 20 | Claude Code, Codex |
| post-mortemgriddynamics/rosetta | Skill | 370 | today | Apache-2.0 | A | 17 | Claude Code |