How do you review twenty-six thousand lines of production code in a single sitting? You don’t – you split the problem. This Valentine’s Day session launched six code-reviewer agents in parallel, each specializing in a different layer of the observability-toolkit stack. Ninety-one trace spans later, the results converged into 83 prioritized findings and a clean commit to the backlog.

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       ████████████████████  1.00   healthy
 FAITHFULNESS    ███████████████████░  0.97   healthy
 COHERENCE       ███████████████████░  0.97   healthy
 HALLUCINATION   ███████████████████░  0.04   healthy   (lower is better)
 TOOL ACCURACY   ████████████████████  1.00   healthy
 EVAL LATENCY    ████████████████████  0.004s healthy
 TASK COMPLETION ████████████████████  1.00   healthy

Dashboard status: healthy – All seven metrics in the green. Tool accuracy and task completion at ceiling; LLM-as-Judge scores above 0.96 across the board; hallucination well below the 0.05 threshold.

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. Across 64 tool spans and 2 task completions, every call succeeded with sub-5ms hook latency.

The content quality metrics come from LLM-as-Judge evaluation – a G-Eval pattern where a Sonnet judge read both session outputs (the review document and the updated backlog) and scored along four criteria. The judge also spot-checked 10+ file:line references against actual source files to verify faithfulness.

Per-Output Breakdown

Each output was evaluated independently, then aggregated:

Document	Relevance	Faithfulness	Coherence	Hallucination
docs/reviews/full-stack-review-2026-02-14.md (~400 lines)	1.00	0.95	0.95	0.05
docs/BACKLOG.md (+334 lines)	1.00	0.98	0.98	0.02
Session Average	1.00	0.97	0.97	0.04

What the Judge Found

The backlog update scored highest across all dimensions – perfect relevance, near-perfect faithfulness (0.98), and minimal hallucination (0.02). Every item was accurately transcribed from the review with correct file:line references and proper priority classification.

The review document scored slightly lower on faithfulness (0.95) due to one nuance: the C2 finding claimed DEFAULT_TIMEOUT_MS is “not enforced on all fetch calls,” when the timeout is actually used with setTimeout at line 433. The underlying security concern is valid – not all code paths enforce it consistently – but the wording slightly overstated the gap.

Hallucination was minimal across both outputs. The review added contextual risk assessments (“DoS risk,” “O(n^2) behavior”) that are technically inferences rather than direct code observations, but all were reasonable and defensible. The backlog added P0-P4 priority labels not present in the raw review output, but these mapped logically to the Critical/High/Medium/Low tiers.

The judge verified 10+ source references:

• src/server.ts:198 – type assertion confirmed
• src/lib/llm-as-judge.ts:749-787 – withTimeout race condition exists
• src/lib/quality-metrics.ts:604-615 – percentile bounds check missing
• dashboard/src/api/server.ts:9 – CORS hardcoded to localhost:5173
• manifest.json – shows 6 tools vs 15 actual (verified)
• package.json vs README.md version mismatch (2.0.0 vs v2.0.1, verified)

Session Telemetry

Metric	Value
Session ID	b95c2314-2579-43cb-b5a7-86611356fbb6
Date	2026-02-14
Model	Claude Opus 4.6
Total Spans	94
Tool Calls	64 (success: 64, failed: 0)
Agents Launched	6 (code-reviewer, background)
Tasks Created	2 (review doc, backlog update)
Tasks Completed	2/2
Hooks Observed	session-start, skill-activation-prompt, agent-pre-tool, agent-post-tool, builtin-pre-tool, builtin-post-tool, plugin-pre-tool, plugin-post-tool

Tool Breakdown

Tool	Count	Notes
Bash	42	Git operations, Python metric extraction
Task (agents)	6	Parallel code-reviewer launches
TaskCreate	4	Review + backlog tracking
TaskUpdate	8	Status transitions
Write	2	Review doc + backlog
Read	2	Existing file reads
Glob	1	Review directory check
Skill	1	git-commit-smart

Methodology Notes

Telemetry was extracted from ~/.claude/telemetry/traces-2026-02-14.jsonl (91 spans matched session ID). The session launched 6 background code-reviewer agents; their internal tool calls are tracked separately by the agent framework and not reflected in the parent session’s span count. Rule-based metrics were computed from hook spans only. Token counts were not available from hook telemetry (reported as 0); actual consumption was substantial given six parallel Opus agents processing ~26K lines of source code.

Task completion reflects the explicit TaskCreate/TaskUpdate lifecycle: 2 tasks created, both marked completed. The 6 background agents completed independently and their results were consumed via TaskOutput reads, but those completions are tracked in agent telemetry rather than parent task spans.