Telemetry
Agentic Framework for Modern Development
npx -y skills add LazyIsEfficient/agentic-os --skill telemetryAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 13 stars13 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Internal utility for the skills library itself — opt-in, local-first, privacy-respecting usage telemetry and update checks. Provides version checking, usage logging, and usage reporting for the skills in this repo. Use only when asked to view skill usage stats, configure telemetry opt-in/out, wire a skill to log its own runs, or check for skill updates.
SKILL.md
4.8 KB, as published. Nobody here has run it
Telemetry
Internal, opt-in, local-first telemetry for the skills in this repository. It answers two questions: which skills get used (and how reliably)? and is a newer version of the library available? Every run is logged to a local file regardless of opt-in; data only leaves the machine if the user has explicitly opted in. No PII is ever collected — see README.md for the full field list and privacy commitment.
Where data lives
All state lives under the telemetry data directory — by default ~/.ai-marketing-skills/, the upstream location, which can differ per install:
| Path | Written by | Contents |
|---|---|---|
telemetry-config.json | telemetry_init.py | opted_in (bool), device_id (random UUID), created |
analytics/skill-usage.jsonl | telemetry_log.py | one JSON object per skill run (append-only) |
version-cache.json | version_check.py | last GitHub release seen + check timestamp (24h TTL) |
Each line in skill-usage.jsonl records only anonymous fields: skill name, duration (ms), success, version, OS, arch, Python version, UTC timestamp, and the random device_id. Never code, paths, repo names, or content.
Tools
| Script | Purpose | Invocation |
|---|---|---|
telemetry_init.py | Configure opt-in/out (interactive on first run) | python3 telemetry/scripts/telemetry_init.py (or --yes / --no) |
telemetry_log.py | Log one skill run (called by a skill's preamble) | see integration pattern below |
telemetry_report.py | View local usage stats | python3 telemetry/scripts/telemetry_report.py (--json, --skill <name>) |
version_check.py | Check for a newer library release | python3 telemetry/scripts/version_check.py |
Logging a skill's runs
A skill opts into telemetry by calling telemetry_log.py once at the end of its run. The script always appends to the local JSONL log, and additionally POSTs to the analytics endpoint only if the user opted in. It fails silently and never blocks execution. All four flags are required:
python3 telemetry/scripts/telemetry_log.py \
--skill my-skill-name \
--duration 4500 \
--success true \
--version 1.0.0
Capture a start time before the work, compute --duration in milliseconds after, and pass --success false if the run errored. The endpoint in telemetry_log.py is a stub (ANALYTICS_ENDPOINT) — replace it with a real URL before remote send does anything.
Reading the stats
python3 telemetry/scripts/telemetry_report.py # human-readable summary
python3 telemetry/scripts/telemetry_report.py --json # machine-readable
python3 telemetry/scripts/telemetry_report.py --skill seo-ops # filter to one skill
Reports total runs, runs in the last 7/30 days, per-skill success rates and average durations, and the most-used skill. Reads only the local log — works the same whether or not you opted in.
Checking for updates
python3 telemetry/scripts/version_check.py
Compares a local VERSION file against the latest GitHub release, caches the result for 24h, and stays silent unless a newer version exists. It needs a VERSION file at the repo's skills root (.claude/skills/VERSION) containing the current version (e.g. 1.0.0). If that file is absent the script has no baseline and stays silent rather than reporting a false update — create the file to enable update notices.
Privacy
- Opt-in only — nothing is sent without explicit consent.
- Local-first — usage is always stored locally for your own inspection.
- No PII — no names, emails, paths, repo names, or content.
- Revocable — delete
telemetry-config.jsonfrom the telemetry data directory (by default~/.ai-marketing-skills/) and re-runtelemetry_init.py.
Gives 0 of the 12 instructions most monitoring observability skills give
Counted across 481 of the 483 authors here whose files we hold, read 2026-08-06
- link every alert to a runbookin 43 of 481, across 35 files
- use structured json loggingin 36 of 481, across 31 files
- alert on user-facing symptomsin 20 of 481, across 15 files
- emit structured JSON logs with stable event namesin 18 of 481, across 13 files
- propagate trace context across boundariesin 16 of 481
- use histograms for latency trackingin 14 of 481, across 9 files
- use OpenTelemetry for distributed tracingin 13 of 481, across 8 files
- include a correlation ID on every log linein 13 of 481, across 8 files
- Define service level objectivesin 10 of 481, across 7 files
- Call useAzureMonitor before importing other modulesin 9 of 481, across 2 files
- stop and ask for clarification if inputs are missingin 9 of 481, across 2 files
- define on-call questions before adding telemetryin 9 of 481, across 4 files
Said here and by no other author read
- configure telemetry opt-in
- log one skill run
- view local usage stats
- check for a newer library release
- pass success false if the run errored
- provide all four flags when logging a run
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.