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.
npx skills add fanfan-de/anybox --skill telemetrygit clone --depth 1 https://github.com/fanfan-de/anyboxWrote 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/fanfan-de/anybox/telemetry)<a href="https://agentmods.dev/skills/fanfan-de/anybox/telemetry"><img src="https://agentmods.dev/badge/skills/fanfan-de/anybox/telemetry.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00031 | $0.00795 |
| Opus 5 | $0.00015 | $0.00398 |
| Sonnet 5 | $0.00006 | $0.00159 |
| Haiku 4.5 | $0.00003 | $0.00080 |
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 8d 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.
This is a copy
94% identical to macos-telemetry — 6 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.
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
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 ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 87 lines · 31 tokens per session scan A 64fae6fd88c4
telemetry is a skill published in the GitHub repository fanfan-de/anybox (57 stars, last pushed 25d ago), licensed MIT. It adds 31 tokens to every session and 795 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to macos-telemetry, differing in 6 lines, and is treated as a copy.
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