claude

pm-tokens

Token optimization for every stage of the agent journey: crush tool outputs before they enter context, navigate code by map instead of reading files, diet the output register, budget the window cache-aware, measure everything, and hand off sessions at 5% of transcript size

1,544 on GitHubby Mohit Aggarwalv1.0.0MITupdated yesterdayproductivity
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Plugin pm-tokens from marketplace pm-claude-skills in mohitagw15856/pm-claude-skills. Commands read from the repo's manifest — yesterday.

What it installs

Skills (6)

  • context-budget

    Plan a session's context window like the budget it is — what loads up front, what gets linked instead, what stays fetch-on-demand, and how to keep the stable prefix cache-friendly so repeated turns cost cents instead of dollars. Use when asked my agent keeps blowing its context, plan what to load in

  • context-crusher

    Compress tool outputs, logs, and JSON before they enter the context window — structural compression via a deterministic stdlib script (schema + samples + stats instead of 300 raw rows), no API, no summarization loss. Use when asked shrink this tool output, my context is full of JSON, compress these

  • repo-map

    Navigate a codebase by map instead of reading files wholesale — a deterministic stdlib script that emits the tree with line counts and top-level symbols, plus the read-the-map-first discipline that cuts exploration tokens by an order of magnitude. Use when asked explore this repo efficiently, stop r

  • session-handoff

    Write a handoff summary so another agent or person (or a fresh session) can pick up the work with full context. Use when ending a work session, hitting a context limit, switching agents, or pausing a task mid-flight. Produces a structured handoff: what the goal is, what's done, the current state, wh

  • token-cost

    Measure before optimizing — estimate token counts locally with stated heuristics, price them at your model's rates, and quantify before/after savings, because token optimization without measurement is vibes. Use when asked how many tokens is this, what does this context cost per call, is this optimi

  • token-diet

    Cut LLM output tokens 40–70% by stripping grammatical scaffolding while preserving every fact — telegraphic output modes, when they pay (pipelines, long sessions) and when they don't (single shots, human-facing prose), with the mode lines to switch on demand. Use when asked make the model respond te

View source on GitHub mohitagw15856.github.io

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