Skill

cta-trend-watch

Analyzes Claude Code token usage and cost trends over daily/weekly/monthly periods, computes metrics like averages, peak days, trend direction, and monthly projections, with per-project filtering.

monitoring

npx claudepluginhub li195111/claude-token-analyzer --plugin claude-token-analyzer

Tool Access

This skill uses the workspace's default tool permissions.

Preview

Analyze token usage and cost trends over time, with derived metrics and simple forecasting.

SKILL.md

Similar Skills

analyzing-usage

Analyzes Claude Code usage, costs, tokens, efficiency, cache hits, and burn rate using ccusage for stats and termgraph for visuals on work or personal profiles.

5 tools

dev-tools

cta-health-check

Generates a quick one-page report summarizing Claude Code token usage, costs, sessions, projects, cache hit rates, subagent ratios, and top spenders. Routes to cost, anomaly, project, or trend audits.

claude-token-analyzer

genshijin-stats

154

Displays real token usage and estimated savings for current Claude Code session from session logs—no AI estimation. Invoke via `/genshijin-stats` for session stats, `--all` lifetime totals, `--share` summaries, or `--since` periods.

genshijin

Stats

Stars11

Forks4

Last CommitMar 27, 2026

Actions

View Source View Plugin View on GitHub View README

Help us improve

Share bugs, ideas, or general feedback.

CTA Trend Watch — Usage Trend Analysis

Analyze token usage and cost trends over time, with derived metrics and simple forecasting.

Workflow

Step 1: Sync Data

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

Step 2: Fetch Trends

Execute mcp__token-analyzer__trend_report with:

granularity: "daily" (default). Accept "weekly" or "monthly" from user.
last_n_days: 30 (default). Accept custom range from user.
project_path: optional, for per-project filtering.

Step 3: Calculate Derived Metrics

From the returned data_points array, compute:

Daily average cost: avg_daily_cost (from API)
Daily average tokens: avg_daily_tokens (from API)
Peak day: peak_day (from API)
Recent 7-day average: mean of last 7 data points' total_cost
Previous 7-day average: mean of data points [-14:-7] total_cost
Trend direction: (recent_7d - prev_7d) / prev_7d * 100
Monthly projection: avg_daily_cost * total_days_in_current_month

Step 4: Output Report

## CTA 趨勢報告 — 最近 N 天

| 指標 | 值 |
|------|-----|
| 日均成本 | $X.XX USD |
| 日均 Token | X |
| 峰值日 | YYYY-MM-DD ($X.XX) |
| 近 7 天均值 | $X.XX USD |
| 前 7 天均值 | $X.XX USD |
| 趨勢方向 | ↑ +X.X% / ↓ -X.X% |
| 本月預估 | $X.XX USD |

### 趨勢分析
- (Describe trend: stable / rising / declining based on data)
- (If rising >20%: warn and suggest cta-anomaly-hunt)

### 每日明細
| 日期 | 成本 | Token | 會話數 |
|------|------|-------|--------|
| ... | ... | ... | ... |

Step 5 (Conditional): Trend Alert

If the 7-day trend shows >20% increase, proactively suggest:

「趨勢上升幅度較大，建議執行 cta-anomaly-hunt 排查原因。」

Behavior Rules

Default to daily granularity + 30 days. Accept weekly/monthly and custom day ranges.
Flag trends exceeding +20% as warnings and suggest anomaly investigation.
Monthly projection = avg_daily_cost * total days in month (not remaining days).
Support per-project filtering via project_path parameter.
When fewer than 14 data points exist, skip 7-day comparison and note insufficient data.

Output Rules

Use 繁體中文 for prose, English for technical terms.
Currency: $X.XX USD.
Percentages: one decimal place (+15.3%).
Token counts: thousands separator (125,000).
Trend arrows: ↑ for increase, ↓ for decrease, → for stable (< 3%).

Additional Resources

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