cta-anomaly-hunt

name	cta-anomaly-hunt
description	This skill should be used when the user asks about "anomalies", "problems", "有異常嗎", "哪些會話有問題", "排查", "診斷", or suspects waste or efficiency issues in Claude Code usage. Scans for 6 types of statistical anomalies with severity scoring and automatic drill-down into suspicious sessions. Can also be routed from the main cta skill.

CTA Anomaly Hunt — Statistical Anomaly Detection

Scan for anomalies, triage by severity, and drill into suspicious sessions for root cause diagnosis.

Workflow

Step 1: Sync Data

Execute mcp__token-analyzer__sync_db. Skip if already called in this conversation.

Step 2: Scan Anomalies

Execute mcp__token-analyzer__anomaly_scan with:

• stddev_threshold: 2.0 (default), user can adjust (1.5 sensitive / 3.0 conservative)
• min_tokens_for_cache_check: 10000 (default), adjustable
• project_path: optional

IMPORTANT: anomaly_scan output can exceed 370K characters. Write raw output to ${TMPDIR:-/tmp}/cta-anomaly-report.json before parsing.

Step 3: Triage by Severity

Sort and present anomalies in this priority order:

1. CostInefficient — High cost + low cache (most dangerous composite anomaly)
2. HighCost — Pure cost outlier
3. LowCacheHitRate — Efficiency problem
4. ExcessiveToolUse — Possible infinite loop or inefficient workflow
5. HighTokenUsage — Token consumption outlier
6. UnusualModelMix — Multi-model switching

Anomalies with severity scores sort before those without.

Step 4: Deep Dive (Top 3-5 Sessions)

For the highest-severity anomalies, execute mcp__token-analyzer__analyze_session with each session_id to obtain the 10-dimension analysis.

Limit automatic drill-down to 5 sessions maximum to avoid excessive token consumption.

Step 5: Output Diagnostic Cards

For each analyzed session, output a diagnostic card:

### 🔴 Session a1b2c3d4 — CostInefficient (severity: 8.5)
- **成本**：$X.XX（超出平均 X.Xσ）
- **Cache 命中率**：X.X%（平均 XX.X%）
- **模型**：claude-opus-4-6
- **Tool 排名**：Read(35), Bash(12), Edit(8)
- **壓縮事件**：2 次（turn 8, turn 15）
- **可能原因**：上下文頻繁壓縮導致 cache 失效，大量重讀檔案
- **建議**：考慮分拆任務以減少壓縮頻率

Infer "possible cause" and "suggestion" from the 10-dimension analysis data:

• High compression events → context window overflow, suggest splitting tasks
• High tool use + low cache → repetitive file reading, suggest caching strategy
• Multiple models → possible failover or misconfiguration
• Excessive turns → possible stuck loop, check tool_ranking for patterns

Behavior Rules

1. Always write raw anomaly_scan output to ${TMPDIR:-/tmp}/cta-anomaly-report.json first.
2. Anomalies without severity (HighTokenUsage, ExcessiveToolUse, UnusualModelMix) rank after those with severity.
3. Maximum 5 automatic drill-down sessions. Offer to analyze more if the user requests.
4. User can adjust stddev_threshold (1.5=sensitive, 2.0=balanced, 3.0=conservative).
5. User can adjust min_tokens_for_cache_check to filter short sessions from cache analysis.

Output Rules

• Use 繁體中文 for prose, English for technical terms.
• Currency: $X.XX USD.
• session_id: first 8 characters.
• Severity color coding: 🔴 high (>5), 🟡 medium (2-5), 🟢 low (<2).

Additional Resources

For MCP tool parameter details and anomaly type definitions: ${CLAUDE_PLUGIN_ROOT}/skills/cta/references/tool-reference.md