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Quality telemetry

Skill roronoazoroshao369/vibe-coding-os/skills/core/quality-telemetry

Vibe Coding OS — Claude/Codex/Cursor skill framework with 139 skills, 111 commands, 95 templates, 22 tracked sources, 28/28 validation gates PASS. Quality Shield, Engineering Discipline Pack, plugin marketplace.

Install
npx -y skills add roronoazoroshao369/vibe-coding-os --skill quality-telemetry

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

One thing to look at

  • 0 stars0 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.

SKILL.md

2.4 KB, 443 tokens by cl100k_base, as published. Nobody here has run it

Skill: Quality Telemetry & Analytics

Purpose

Collect local-first quality telemetry from quality engine runs, sessions, and reviews to support trend analysis, model comparison, and continuous quality improvement.

When to use

Use after quality engine runs, before merging, during periodic quality reviews, or when analyzing quality trends across time, tasks, or models. Choose this skill when you want to record, aggregate, or review quality signals instead of only viewing a single run result.

Inputs

  • Quality engine JSON results
  • Model name or profile
  • Task type or category
  • Session context, repository path, or run timestamp
  • Optional time window for scoped metrics

Outputs

  • Recorded quality event payloads
  • Session-level aggregated metrics
  • Trend reports with pass, warn, fail, timing, and model or task comparisons
  • Recommended adjustments for quality config or model selection

Workflow

  1. Emit a quality event immediately after each meaningful quality engine run.
  2. Capture session metrics from the collected events.
  3. Generate a trend report over the selected time window or dataset.
  4. Review insights for recurring failures, regressions, or improvements.
  5. Adjust quality config, model profile selection, or workflow discipline based on evidence.

Failure modes

  • Emitting telemetry that includes secrets, credentials, or unnecessary personal data.
  • Only reviewing the latest run and missing recurring patterns.
  • Comparing runs across different task types without segmenting the analysis.
  • Ignoring timing signals that indicate a profile is too light or too heavy.
  • Treating telemetry as a replacement for quality gates instead of a feedback layer.

Verification checklist

  • Quality event emitted with model, task type, timestamps, and aggregated gate results.
  • Metrics are scoped correctly by session, model, or task type.
  • Trend report covers the requested time window and clearly shows regressions or improvements.
  • No sensitive or private data is emitted in telemetry payloads.
  • Recommended config adjustments are specific, actionable, and limited to quality behavior.

Related skills

  • skills/core/quality-engine/SKILL.md
  • skills/core/model-aware-config/SKILL.md

Gives 0 of the 12 instructions most monitoring observability skills give in 443 tokens

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

  • emit quality event after each run
  • capture session metrics from events
  • generate trend report over time window
  • review insights for recurring patterns
  • adjust quality config based on evidence
  • scope metrics by session, model, or task type

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.

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