A Thursday night code health check turned into a 60-file cleanup. The Bug Detective skill scanned every error source available for the TCAD Scraper – tests, TypeScript compiler, Biome linter, scraper logs, GitHub issues – and found the codebase in surprisingly good shape: 617 tests passing, zero type errors, but 67 lint issues quietly accumulating. The session fixed them all, then discovered the production server had been offline for three days.

Quality Scorecard

Seven metrics. Three from rule-based telemetry analysis, four from LLM-as-Judge evaluation of the session outputs. Together they form a complete picture of how well this session did its job.

The Headline

 RELEVANCE       ███████████████████░  0.94   healthy
 FAITHFULNESS    ███████████████████░  0.94   healthy
 COHERENCE       ███████████████████░  0.93   healthy
 HALLUCINATION   ███████████████████░  0.04   healthy  (lower is better)
 TOOL ACCURACY   ████████████████████  1.00   healthy
 EVAL LATENCY    ████████████████████  2.1ms  healthy
 TASK COMPLETION ████████████████████  1.00   healthy

Dashboard status: healthy – All seven metrics within healthy thresholds. Tool accuracy and task completion at 100%. The judge flagged one minor factual imprecision in the bugfix plan but no material errors.

How We Measured

The first three metrics – tool correctness, evaluation latency, and task completion – were derived automatically from OpenTelemetry trace spans. Every tool call emits a span; the rule engine checks whether it succeeded and how long it took.

The content quality metrics come from LLM-as-Judge evaluation – a G-Eval pattern where an AI judge reads the session’s outputs and scores along four criteria: relevance, faithfulness, coherence, and hallucination. The judge evaluated three outputs: the bugfix plan document, the 60-file commit, and the production diagnostics findings. Each was independently scored, then averaged.

Per-Output Breakdown

Each output was evaluated independently, then aggregated:

Document	Relevance	Faithfulness	Coherence	Hallucination
Bugfix plan (plan.md)	0.95	0.88	0.95	0.10
Code fixes (commit 8d60f04, 60 files)	0.97	0.95	0.93	0.02
Production diagnostics	0.90	0.98	0.90	0.00
Session Average	0.94	0.94	0.93	0.04

What the Judge Found

The code commit scored highest across the board (0.97 relevance, 0.02 hallucination). All eight semantic changes verified in the diff matched their descriptions exactly, and the post-fix verification confirmed zero errors remaining. The judge noted that mixing biome-ignore suppressions with actual fixes in a single commit is a style choice, not an error.

The bugfix plan had the highest hallucination score (0.10) due to one characterization issue: the isNaN() to Number.isNaN() change was described as a “potential bug” at P1 priority. The judge pointed out that in this specific usage (date.getTime() always returns a number or NaN), both functions behave identically. It’s a valid lint best-practice fix, but calling it a correctness bug overstates the severity.

The production diagnostics scored highest on faithfulness (0.98) with zero hallucination. Every claim – Hobbes offline for 3 days, Cloudflare error 1033, 100% packet loss – was directly traceable to observable system output. Relevance was slightly lower (0.90) because the session could diagnose but not remediate a hardware/network issue.

The judge also noted one code quality subtlety: replacing describedById! with describedById as string in SearchBox.test.tsx trades one type-safety escape hatch for another. A runtime check or ?? '' fallback would have been more robust.

Session Telemetry

Metric	Value
Session ID	4718d5f1-4d1f-413f-bd70-ae400917268b
Date	2026-02-13
Model	claude-opus-4-6
Total Spans	153
Tool Calls	115 (success: 115, failed: 0)
Hooks Observed	session-start, builtin-pre-tool, builtin-post-tool, agent-pre-tool, agent-post-tool, skill-activation-prompt

Tool Breakdown

Tool	Calls
Bash	85
TaskUpdate	13
TaskCreate	6
Write	4
Edit	4
TaskOutput	3

Methodology Notes

Telemetry was captured via OpenTelemetry hooks exporting to SigNoz Cloud. Token usage metrics were not available for this session (token extraction hook spans present but with zero values). The 153 spans represent the full session lifecycle from Bug Detective scan through commit and push. Task completion reflects 6/6 tracked tasks completed via the TaskCreate/TaskUpdate workflow. LLM-as-Judge evaluation was performed by the genai-quality-monitor agent, which independently verified claims against source files and tool outputs.