From pm-tokens
Plans a session's context window budget: what loads up front, what gets linked, what stays fetch-on-demand, with cache-aware prefix layout to reduce per-turn costs.
How this skill is triggered — by the user, by Claude, or both
Slash command
/pm-tokens:context-budgetThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
A context window is a budget that gets re-spent every single turn — everything sitting in it rides every call, which is how a session that "only loaded a few files" ends up paying for them forty times. This skill plans the spend before the session: what earns a permanent seat (loaded once, up front, stable), what gets *linked* (a map or index, with the full thing fetch-on-demand), and what neve...
A context window is a budget that gets re-spent every single turn — everything sitting in it rides every call, which is how a session that "only loaded a few files" ends up paying for them forty times. This skill plans the spend before the session: what earns a permanent seat (loaded once, up front, stable), what gets linked (a map or index, with the full thing fetch-on-demand), and what never enters at all. The quiet second half is cache-awareness: providers price cached prefix tokens at a fraction of fresh ones, but only if the prefix stays byte-identical — so the layout (stable things first, volatile things last) is itself a cost decision.
Ask for these if not provided:
| Piece | Size (~tokens) | Verdict | Why |
|---|---|---|---|
| [load / link (with its index) / fetch-on-demand / exclude] |
[Ordered: instructions → schemas/standing refs → the maps/indexes → (volatility line) → working state → history — with the cache note per section]
[Turn 1 / mid-session / near-limit — the arithmetic, cache-adjusted where pricing is known]
[The order, each with its recovery route: "crushed outputs first — refetchable via [command]"]
npx claudepluginhub mohitagw15856/pm-claude-skills --plugin pm-tokensMonitors, caps, and recovers from context accumulation in agentic systems with per-cycle cost tracking, budget enforcement, and emergency pruning. Use for long-lived agent loops, rising API costs, or post-mortem analysis.
Curate the model's context window for the most signal per token — stop burning tokens on bloat. Triggers on "context engineering", "burning tokens", "context window full", "reduce token usage", "the model lost the thread", "too much in context", "prompt caching", "session handoff", or any long / expensive session. The discipline of deciding what goes in the window (and what doesn't): cache-stable instructions, retrieve don't dump, fresh docs at query time, audit your MCPs, compress tool output, and measure tokens like an infra cost.
Designs LLM context windows: allocates token budgets, orders information for attention, selects relevant data, and applies RAG/summarization strategies.