A code-reviewer agent was turned loose on the hooks system with a single question: where are the blind spots? It came back with 25 findings – from missing notification handlers to trace correlation gaps to false positives in error detection – then set about fixing them, one commit at a time.

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        ███████████████████░  0.93   healthy
 FAITHFULNESS     ██████████████████░░  0.92   healthy
 COHERENCE        ██████████████████░░  0.92   healthy
 HALLUCINATION    ████████████████████  0.04   healthy   (lower is better)
 TOOL ACCURACY    ████████████████████  1.00   healthy
 EVAL LATENCY     ████████████████████  0.005s healthy
 TASK COMPLETION  ██████████░░░░░░░░░░  0.50   critical

Dashboard status: CRITICAL – Task completion sits at 0.50 because only half of the TaskCreate spans have corresponding TaskUpdate(completed) spans in telemetry. This reflects the tracking ratio, not actual deliverable status: the 14 commits in the session window demonstrate substantial work was completed.

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 read 5 key outputs: the BACKLOG.md audit document, the new notification handler and trace-context modules, and the updated user-prompt handler and output-analyzer. Each file was verified against its backlog claims – imports resolved, constants checked, cross-module integration confirmed.

Per-Output Breakdown

Each output was evaluated independently, then aggregated:

Document	Relevance	Faithfulness	Coherence	Hallucination
BACKLOG.md (775 lines)	0.95	0.90	0.88	0.08
notification.ts (65 lines)	0.95	0.95	0.95	0.02
trace-context.ts (48 lines)	0.95	0.93	0.96	0.02
user-prompt.ts (279 lines)	0.92	0.90	0.88	0.05
output-analyzer.ts (57 lines)	0.90	0.92	0.93	0.03
Session Average	0.93	0.92	0.92	0.04

What the Judge Found

Strongest output: trace-context.ts – minimal, focused, well-documented. The cross-hook flow (user-prompt saves context, pre-tool loads it) was confirmed in source. The 10-minute TTL claim in the backlog matches THRESHOLDS.PROMPT_CONTEXT_TTL_MS = 600_000 exactly.

Most interesting verification: The CR4 division-by-zero fix in user-prompt.ts was correctly implemented with independent denominator guards (hitDenom > 0 and totalContextTokens > 0), addressing the original finding where the guard checked inputTokens > 0 but the denominator was inputTokens + cacheReadTokens.

PII fix confirmed: The prompt.text attribute no longer appears anywhere in user-prompt.ts – only prompt.length is emitted, addressing CR1.

Notable quality issue found: analyzeBuiltinOutput does not populate errorCategory (only errorType), while analyzeOutput populates both. Builtin tool errors cannot be filtered by category in the same way as generic errors. This inconsistency was not flagged in the backlog.

Hallucination concern: The BACKLOG.md document (0.08 hallucination score) contains file/line references for already-fixed items that cannot all be verified at exact line numbers since the code has been modified since the audit. This is expected behavior for a living document, not a factual error.

Session Telemetry

Metric	Value
Session ID	a34b1e7c-26ac-4ed9-a199-e6de226b504e
Date	2026-02-16
Model	Claude (code-reviewer agent)
Total Spans	36
Tool Calls	20 (success: 20, failed: 0)
Input Tokens	0 (not captured)
Output Tokens	0 (not captured)
Cache Read Tokens	0 (not captured)
Commits in Window	14
Files Changed	34 (+1,237 / -86 lines)
Hooks Observed	session-start, skill-activation-prompt, agent-pre-tool, agent-post-tool, builtin-pre-tool, builtin-post-tool, tsc-check

Methodology Notes

• Telemetry data sourced from ~/.claude/telemetry/traces-2026-02-16.jsonl
• Session identified via hook:session-start span with session.id attribute
• Token metrics were not captured for this session (no hook:token-metrics-extraction spans)
• Task completion of 0.50 reflects that only 1 of 2 TaskCreate operations had corresponding TaskUpdate(completed) spans – the session’s 14 commits demonstrate that substantially more work was completed than the metric suggests
• Output files for LLM-as-Judge were identified from git diff in the session’s time window (16:00-16:30 on 2026-02-16), since the Edit tool spans lacked builtin.file_path attributes
• The BACKLOG.md contains accumulated findings from multiple sessions; scoring focused on the sections attributable to this audit session