Analyzes Claude Code sessions using MCP classifiers to detect usage patterns like correction spirals, rates severity, and provides concrete workflow improvements with metrics and advice.
npx claudepluginhub li195111/claude-token-analyzer --plugin claude-token-analyzerThis skill uses the workspace's default tool permissions.
Analyze one or more sessions with the MCP classifier and convert the result into concrete harness guidance.
Analyzes Claude Code session patterns, categories, trends, benchmarks, and usage for behavioral insights and recommendations. Activates on 'patterns', 'insights', 'how am I doing' queries.
Analyzes Claude Code session logs to extract tool usage stats, thinking blocks, error patterns, debug trajectories, and generate actionable productivity recommendations. Provides cc-session CLI for overviews, timelines, searches.
Analyzes current Claude Code session for agent efficiency (tool precision, autonomy) and quality (CLAUDE.md compliance, code patterns), scoring dimensions and surfacing 2-3 actionable improvements.
Share bugs, ideas, or general feedback.
Analyze one or more sessions with the MCP classifier and convert the result into concrete harness guidance.
Execute mcp__token-analyzer__sync_db when the user asks for "latest" or when the conversation likely depends on newly-created sessions.
For a direct historical session_id lookup, sync_db is optional because classify_session_pattern reads JSONL directly.
If the user provides a session_id, use it directly with mcp__token-analyzer__classify_session_pattern.
If the user does not provide a session_id:
mcp__token-analyzer__analyze_globaltop_sessionsmcp__token-analyzer__classify_session_pattern for each selected sessionUse the local skill reference file references/harness-signals-to-advice.md as the SSOT mapping.
Required output elements:
patternseveritycache_hit_rate, subagent_count, repeated_edit_peak, turn_count, duration_minutes, topic_shift_count)If the user asks for trend context, execute mcp__token-analyzer__trend_report and render a short Unicode sparkline using the returned token totals.
Keep it inline, for example:
14d token trend: ▁▂▃▅▄▆█
## CTA 使用模式分析 — a1b2c3d4
- Pattern: `correction_spiral`
- Severity: `alert`
- Signals: cache_hit_rate 18.0%, repeated_edit_peak 8, output_token_ratio 61.0%, turn_count 42
### 建議
1. 把大檔案切成更小的編輯單元,避免同一檔案反覆來回修補。
2. 明確要求 diff-only 回覆,降低 output token 膨脹。
3. 如果需求已改變,先開新 session 或先 checkpoint,再繼續編輯。
info, keep the tone observational instead of warning-heavy.AMBIGUOUS_SESSION_ID, ask the user for a longer ID rather than guessing.