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Show TokenMizer token savings stats — how many tokens saved today, this week, cache hit rate, and which layers are saving the most. Use when user asks about token usage, costs, savings, or "how much have we saved".
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
Save the current session to TokenMizer graph memory. Creates a persistent checkpoint with all tasks, decisions, files, and errors — resumable in any future session. Use when user says "save", "checkpoint", "remember this", "I'm done for today", or session is getting long.
Analyze a large file (CSV, Excel, PDF, JSON, code) and return a token-efficient summary. Instead of reading thousands of rows or pages, get schema + statistics + sample in under 500 tokens. Use when user mentions a file path, asks to analyze data, pastes many rows, or references a CSV/Excel/PDF/JSON file.
Load a previous session from TokenMizer graph memory. Returns a compact context block (100-600 tokens) covering goals, completed work, decisions, open tasks, and files. Inject this as system context to continue exactly where you left off. Use when user says "resume", "continue from last time", "load my project", "what did we do on X", or starts a session on a known project.
SOC 職業分類に基づく
| name | stats |
| description | Show TokenMizer token savings stats — how many tokens saved today, this week, cache hit rate, and which layers are saving the most. Use when user asks about token usage, costs, savings, or "how much have we saved". |
Show TokenMizer token savings analytics.
curl -s http://localhost:8000/api/stats
Then also fetch cache stats:
curl -s http://localhost:8000/api/cache/stats
Show:
TokenMizer Stats
─────────────────────────
Today: 12,450 tokens saved (34%) — $0.0373
Week: 87,200 tokens saved (31%) — $0.2616
By layer:
File extraction: 45,000 tokens
Semantic cache: 28,000 tokens
Smart window: 9,200 tokens
Compression: 3,800 tokens
Output trim: 1,200 tokens
Cache: 847 entries | 68% hit rate | 8% full
─────────────────────────
Dashboard: http://localhost:8000
Tell user to start using TokenMizer as their proxy:
from openai import OpenAI
client = OpenAI(
api_key="your-key",
base_url="http://localhost:8000/v1"
)