claude

token

AI Operations Team — Token: Context window optimization, token counting, truncation strategies, and chunking patterns for LLM efficiency.

77 on GitHubby tonone-aiv1.20.0MITupdated 2 days ago
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Plugin token from marketplace tonone-ai in tonone-ai/tonone. Commands read from the repo's manifest — 2 days ago.

What it installs

Skills (4)

  • token-budget

    Design token budgets — system/user/assistant allocation, overflow handling, context compression. Use when asked "we are blowing the context window", "design a token budget", or "handle context overflow".

  • token-chunk

    Design chunking strategies — semantic splitting, overlap tuning, retrieval-aware chunk sizing. Use when asked "how should we chunk documents", "what chunk size for retrieval", or "set up semantic splitting".

  • token-compact

    Plan a verbatim compaction of a Claude Code transcript — score every tool call and result, drop or elide the stale ones, keep the rest byte-for-byte. Use when asked to "compact the transcript", "what can we drop from context", "trim this session", or "shrink the context window without summarizing".

  • token-recon

    Audit token usage patterns — avg context size, waste, truncation frequency, budget adherence. Use when asked to "audit our token usage", "how often do we truncate", or "find context waste".

Subagents (1)

  • token

    Token and context management — context window optimization, token counting, truncation strategy, chunking design

View source on GitHub second.tonone.ai

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