A prior session’s quality report flagged a potential two-tailed p-value bug in the feature engineering library. This session set out to fix it – and discovered the math was right all along. The real deliverable became a clearer comment, a set of scipy-validated regression tests, and a long-overdue extraction of an inline error taxonomy into a shared classifier.

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       ████████████████████  0.97   healthy
 FAITHFULNESS    ███████████████████░  0.95   healthy
 COHERENCE       ███████████████████░  0.94   healthy
 HALLUCINATION   ████████████████████  0.03   healthy  (lower is better)
 TOOL ACCURACY   ████████████████████  1.00   healthy
 EVAL LATENCY    ████████████████████  0.004s healthy
 TASK COMPLETION ░░░░░░░░░░░░░░░░░░░░  N/A    --

Dashboard status: healthy – All measured metrics within healthy thresholds. Task completion not applicable (no TaskCreate/TaskUpdate spans).

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 deliverables: the audit analysis itself, the pearsonPValue comment fix with regression tests, and the notification classifier extraction. Each claim was cross-referenced against actual diffs, scipy reference values, and test results.

Per-Output Breakdown

Each output was evaluated independently, then aggregated:

Output	Relevance	Faithfulness	Coherence	Hallucination
Audit analysis (6 deliverables verified)	0.95	0.93	0.90	0.05
quality-feature-engineering.ts (comment + test)	0.98	0.97	0.95	0.02
categorizers.ts + notification.ts (classifier extraction)	0.97	0.95	0.96	0.03
Session Average	0.97	0.95	0.94	0.03

What the Judge Found

The feature engineering fix scored highest on faithfulness (0.97). All four scipy reference values – pearsonPValue(0.6033, 10) = 0.0649, (0.5, 20) = 0.0247, (0.3, 30) = 0.1082, (-0.8, 10) = 0.0056 – were verified against actual scipy.stats.pearsonr with a maximum deviation of 0.000954. The algebraic identity 2 * 0.5 * I_x(df/2, 1/2) = I_x(df/2, 1/2) was confirmed correct, making the original audit flag a false positive.

The classifier extraction achieved the highest coherence (0.96). The refactoring replaced two inline boolean checks (isError, isWarning) with a single classifyNotification() call returning a typed severity string. Pattern coverage expanded from 4 to 9 terms across error and warning categories. The judge noted the function uses a generic string type for the level parameter rather than a union type – acceptable but slightly less type-safe.

The audit analysis itself scored lowest on coherence (0.90) because it was a reasoning exercise without a persistent artifact, making structural evaluation harder. All numeric claims (37 commits, 24 backlog items, 6 deliverables) were traceable to git history.

No hallucination concerns across any output. Every number, file path, and mathematical claim was verified against actual artifacts.

Session Telemetry

Metric	Value
Session ID	8d1c75fb-257e-4ee6-8230-9d60d37df9ac
Date	2026-02-16
Model	Claude Opus 4.6
Total Spans	74
Tool Calls	53 (success: 53, failed: 0)
Hooks Observed	session-start, builtin-post-tool, mcp-pre-tool, mcp-post-tool, tsc-check, skill-activation-prompt

Methodology Notes

Telemetry was extracted from ~/.claude/telemetry/traces-2026-02-16.jsonl filtered by session.id = 8d1c75fb. Token counts were not captured in trace attributes for this session (hook token-metrics spans exist in adjacent sessions but not this one). Task completion is N/A because no TaskCreate/TaskUpdate tool calls were instrumented. The session’s outputs were identified from git commits ec44eee (pearsonPValue comment + tests) and 1d18420 (classifier extraction), plus the in-conversation audit analysis.